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Store multi-dimensional in a size-mutable, labeled data structure Parameters ---------- data : BlockManager axes : list copy : bool, default False )_mgr_cacher _item_cache_cache_is_copy_name _metadata_flagsz list[str]_internal_namesset[str]_internal_names_set _accessorszfrozenset[str] _hidden_attrsrNz+weakref.ReferenceType[NDFrame] | str | Nonerr0rdict[Hashable, Any]_attrsstr_typc [RUSS5 [RUSU5 [RUS05 [RUS05 [RUS[USS95 g)NrrrrrT)allows_duplicate_labels)object __setattr__r|selfdatas E/var/www/html/env/lib/python3.13/site-packages/pandas/core/generic.py__init__NDFrame.__init__sc4T24.4342.45t+TUFcUR5H5upVUcM [U5nURU5nURXgS9nM7 U(aUR 5nUbg[ U[ 5(aC[UR5S:Xa*URSRRU:XaU$URUS9nU$)z passed a manager and a axes dictaxisrdtype) itemsr_get_block_manager_axis reindex_axiscopy isinstancerlenblocksvaluesrastype)clsmgrrrraaxebm_axiss r _init_mgrNDFrame._init_mgrsjjlFA"3'55a8&&s&9 # ((*C  3 -- Oq(JJqM((..%7 jjuj- rTcz[URXS9nURX3RS9R U5$)a Private helper function to create a DataFrame with specific manager. Parameters ---------- typ : {"block", "array"} copy : bool, default True Only controls whether the conversion from Block->ArrayManager copies the 1D arrays (to ensure proper/contiguous memory layout). Returns ------- DataFrame New DataFrame using specified manager type. Is not guaranteed to be a copy or not. )typrr)rr_constructor_from_mgrr __finalize__)rrrnew_mgrs r _as_managerNDFrame._as_manager<s8&TYYC;))' )ERRSWXXrcRURU5n[RX15 U$)a3 Construct a new object of this type from a Manager object and axes. Parameters ---------- mgr : Manager Must have the same ndim as cls. axes : list[Index] Notes ----- The axes must match mgr.axes, but are required for future-proofing in the event that axes are refactored out of the Manager objects. )__new__rr)rrrobjs r _from_mgrNDFrame._from_mgrSs%"kk#" rcUR$)a Dictionary of global attributes of this dataset. .. warning:: attrs is experimental and may change without warning. See Also -------- DataFrame.flags : Global flags applying to this object. Notes ----- Many operations that create new datasets will copy ``attrs``. Copies are always deep so that changing ``attrs`` will only affect the present dataset. ``pandas.concat`` copies ``attrs`` only if all input datasets have the same ``attrs``. Examples -------- For Series: >>> ser = pd.Series([1, 2, 3]) >>> ser.attrs = {"A": [10, 20, 30]} >>> ser.attrs {'A': [10, 20, 30]} For DataFrame: >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) >>> df.attrs = {"A": [10, 20, 30]} >>> df.attrs {'A': [10, 20, 30]} )rrs rattrs NDFrame.attrsksH{{rc$[U5UlgN)dictr)rvalues rrrs 5k rcUR$)a) Get the properties associated with this pandas object. The available flags are * :attr:`Flags.allows_duplicate_labels` See Also -------- Flags : Flags that apply to pandas objects. DataFrame.attrs : Global metadata applying to this dataset. Notes ----- "Flags" differ from "metadata". Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in :attr:`DataFrame.attrs`. Examples -------- >>> df = pd.DataFrame({"A": [1, 2]}) >>> df.flags Flags can be get or set using ``.`` >>> df.flags.allows_duplicate_labels True >>> df.flags.allows_duplicate_labels = False Or by slicing with a key >>> df.flags["allows_duplicate_labels"] False >>> df.flags["allows_duplicate_labels"] = True )rrs rflags NDFrame.flagssN{{r)rrcrURU=(a [5(+S9nUbX#RS'U$)ai Return a new object with updated flags. Parameters ---------- copy : bool, default False Specify if a copy of the object should be made. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` allows_duplicate_labels : bool, optional Whether the returned object allows duplicate labels. Returns ------- Series or DataFrame The same type as the caller. See Also -------- DataFrame.attrs : Global metadata applying to this dataset. DataFrame.flags : Global flags applying to this object. Notes ----- This method returns a new object that's a view on the same data as the input. Mutating the input or the output values will be reflected in the other. This method is intended to be used in method chains. "Flags" differ from "metadata". Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in :attr:`DataFrame.attrs`. Examples -------- >>> df = pd.DataFrame({"A": [1, 2]}) >>> df.flags.allows_duplicate_labels True >>> df2 = df.set_flags(allows_duplicate_labels=False) >>> df2.flags.allows_duplicate_labels False deepr)rrr)rrrdfs r set_flagsNDFrame.set_flagss9xYYD>)<)>%>Y ? " .2IHH. / rctUb4[U5nURS:Xa[SURS35eU$)zvalidate the passed dtypeVz+compound dtypes are not implemented in the z constructor)rhkindNotImplementedError__name__)rrs r_validate_dtypeNDFrame._validate_dtypesK   'EzzS )!ll^<9  rc[U5e)zJ Used when a manipulation result has the same dimensions as the original. rKrs r _constructorNDFrame._constructors "$''rc[R"[U5RS3[[ 5S9 UR $)NzV._data is deprecated and will be removed in a future version. Use public APIs instead. stacklevel)warningswarntyperDeprecationWarningrUrrs r_data NDFrame._datas?  Dz""#$9 9 ')  yyrz!list[Literal['index', 'columns']] _AXIS_ORDERSr)rindexrowszdict[Axis, AxisInt]_AXIS_TO_AXIS_NUMBERint_info_axis_numberLiteral['index', 'columns']_info_axis_name _AXIS_LENc U=(d URVs0sHo3URU5_M nnURU5 U$s snf)z%Return an axes dictionary for myself.)r _get_axisupdate)rrkwargsrds r_construct_axes_dictNDFrame._construct_axes_dict3sM-1,ED4E4E,E G,Eaq! !,E G  HsActURU$![a [SUSUR35ef=f)NzNo axis named z for object type )rKeyError ValueErrorr)rrs r_get_axis_numberNDFrame._get_axis_number<sG U++D1 1 U~dV3DS\\NST T Us&7cBURU5nURU$r)r!r)rr axis_numbers r_get_axis_nameNDFrame._get_axis_nameDs%**40  ,,rcpURU5nUS;deUS:Xa UR$UR$)N>rrr)r!rcolumns)rrr$s rrNDFrame._get_axisJs:++D1 f$$$(A-tzz?4<<?rcVURU5nURnUS:XaSU- $U$)z'Map the axis to the block_manager axis.r)r!r)rrndims rrNDFrame._get_block_manager_axisPs2##D)}} 19t8O rc4[X5n0nUSn[UR5H@upVUbU=pxO USU3nUnURU5n U R 5n X*lXU'MB [ U[5(aUn OUR 5n XU'U$)Nrlevel_)getattr enumeratenamesget_level_values to_seriesrrr) rr axis_indexrprefixinamekeylevel level_valuessdindexs r_get_axis_resolversNDFrame._get_axis_resolvers[sT( a !1!12GA""e  qc*%66u=L&&(A GcF3 j* - -F))+F$rc SSKJn 0nURH#nURUR U55 M% UR 5VVs0sH%upE[ U[5(aMU"U5U_M' snn$s snnf)Nrclean_column_name)pandas.core.computation.parsingrBrrr>rrr)rrBr axis_namekvs r_get_index_resolversNDFrame._get_index_resolverszslE,.**I HHT--i8 9+56GGIXIDAZPQSVEW'!!$a'IXXXs A?. A?c SSKJn SSKJn [ U[ 5(aU"UR 5U0$[URUR55VVs0sHRup4[ U[5(aMU"U5U"USURX0RUS9RU5_MT snn$s snnf)z Return the special character free column resolvers of a dataframe. Column names with special characters are 'cleaned up' so that they can be referred to by backtick quoting. Used in :meth:`DataFrame.eval`. rrArF)rrr8r)rCrBpandas.core.seriesrrrlr8zipr(_iter_column_arraysrrdtypesr)rrBrrErFs r_get_cleaned_column_resolvers%NDFrame._get_cleaned_column_resolverss F- dI & &%dii0$7 7 DLL$*B*B*DE  Fa%  ! a &TZZa{{1~#l4  !F    s B:<:B:c,[XR5$r)r0rrs r _info_axisNDFrame._info_axisst1122rc[URR5S:XagURRSRR 5$)NrF)rrrrefs has_referencers r_is_view_after_cow_rules NDFrame._is_view_after_cow_rulessB tyy A %yy"''5577rcB^[U4SjTR55$)z# Return a tuple of axis dimensions c3X># UHn[TRU55v M! g7frrr.0rrs r NDFrame.shape..s$G5FS*++5Fs'*)tuplerrs`rshape NDFrame.shapes GT5F5FGGGrcbURVs/sHoRU5PM sn$s snf)z/ Return index label(s) of the internal NDFrame )rr)rrs rr NDFrame.axess+,0+<+<=+>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.ndim 1 >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.ndim 2 )rr,rs rr, NDFrame.ndims,yy~~rcT[[R"UR55$)a Return an int representing the number of elements in this object. Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. See Also -------- ndarray.size : Number of elements in the array. Examples -------- >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.size 3 >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.size 4 )rnpprodrars rsize NDFrame.sizes02774::&''rrrc$URXSUS9$)a Assign desired index to given axis. Indexes for%(extended_summary_sub)s row labels can be changed by assigning a list-like or Index. Parameters ---------- labels : list-like, Index The values for the new index. axis : %(axes_single_arg)s, default 0 The axis to update. The value 0 identifies the rows. For `Series` this parameter is unused and defaults to 0. copy : bool, default True Whether to make a copy of the underlying data. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` Returns ------- %(klass)s An object of type %(klass)s. See Also -------- %(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s. F)rr)_set_axis_nocheck)rlabelsrrs rset_axisNDFrame.set_axiss\%%fE%MMrrcU(a[XRU5U5 gURU=(a [5(+S9n[XURU5U5 U$Nr)setattrr%rr)rrorrrrs rrnNDFrame._set_axis_nochecksT  D--d3V <))!C.A.C*C)DC C++D16 :Jrcp[U5nURRX5 UR5 g)zr This is called from the cython code when we set the `index` attribute directly, e.g. `series.index = [1, 2, 3]`. N)rrrp_clear_item_cache)rrros r _set_axisNDFrame._set_axis's, f% 4(  rc [R"S[U5RS[U5RS3[[ 5S9 UR U5nUR U5nXE:Xa%URU=(a [5(+S9$XEXT0n[UR5Vs/sH"opRURXw55PM$ nnURRXE5n URR (Ga"[#UR[$5(Ga['U USUSSS S S 9n [#U [$5(de[#UR[$5(deURR(SR*U R(SlU R(SR*R-U R(S5 [5(dUS LaU RS S9n UR/XR0S 9n U R3USS9$UR4"U /UQ7SS 06R3USS9$s snf)a Interchange axes and swap values axes appropriately. .. deprecated:: 2.1.0 ``swapaxes`` is deprecated and will be removed. Please use ``transpose`` instead. Returns ------- same as input Examples -------- Please see examples for :meth:`DataFrame.transpose`. 'zN.swapaxes' is deprecated and will be removed in a future version. Please use 'z.transpose' instead.rrrrNFblock)rrrTrswapaxesmethodr)rr r r FutureWarningrUr!rrrangerrget_valuesr}ris_single_blockrrrrrU add_referencerrrr) raxis1axis2rr7jmappingrEnew_axes new_valuesrouts rr}NDFrame.swapaxes1s$"  T ##$%:..//C E ')    ! !% (  ! !% ( 699$"D/B/D+D9E E,?DT^^?TU?T!NN7;;q#45?TU\\**10 99 $ $ $DII|)L)L%   Gg|44 44dii66 66%)YY%5%5a%8%=%=GNN1  " NN1  " " 0 01B C&((T->!,,D,1,,W<<,HC##D#< <      ,tJ, /  0/Vs7)Ir)rcfURU5nURU5nURXBSS9$)a Return {klass} with requested index / column level(s) removed. Parameters ---------- level : int, str, or list-like If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels. axis : {{0 or 'index', 1 or 'columns'}}, default 0 Axis along which the level(s) is removed: * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row. For `Series` this parameter is unused and defaults to 0. Returns ------- {klass} {klass} with requested index / column level(s) removed. Examples -------- >>> df = pd.DataFrame([ ... [1, 2, 3, 4], ... [5, 6, 7, 8], ... [9, 10, 11, 12] ... ]).set_index([0, 1]).rename_axis(['a', 'b']) >>> df.columns = pd.MultiIndex.from_tuples([ ... ('c', 'e'), ('d', 'f') ... ], names=['level_1', 'level_2']) >>> df level_1 c d level_2 e f a b 1 2 3 4 5 6 7 8 9 10 11 12 >>> df.droplevel('a') level_1 c d level_2 e f b 2 3 4 6 7 8 10 11 12 >>> df.droplevel('level_2', axis=1) level_1 c d a b 1 2 3 4 5 6 7 8 9 10 11 12 Nrl)r droplevelrp)rr:rro new_labelss rrNDFrame.droplevelqs6z%%%e, }}Z}>>rcXnX U$r)ritemresults rpop NDFrame.pops J rc^Uc[UR5OURU54mUR[ U4Sj[ UR 555n[U[5(aURUSS9nU$)a Squeeze 1 dimensional axis objects into scalars. Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged. This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call `squeeze` to ensure you have a Series. Parameters ---------- axis : {0 or 'index', 1 or 'columns', None}, default None A specific axis to squeeze. By default, all length-1 axes are squeezed. For `Series` this parameter is unused and defaults to `None`. Returns ------- DataFrame, Series, or scalar The projection after squeezing `axis` or all the axes. See Also -------- Series.iloc : Integer-location based indexing for selecting scalars. DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame. Examples -------- >>> primes = pd.Series([2, 3, 5, 7]) Slicing might produce a Series with a single value: >>> even_primes = primes[primes % 2 == 0] >>> even_primes 0 2 dtype: int64 >>> even_primes.squeeze() 2 Squeezing objects with more than one value in every axis does nothing: >>> odd_primes = primes[primes % 2 == 1] >>> odd_primes 1 3 2 5 3 7 dtype: int64 >>> odd_primes.squeeze() 1 3 2 5 3 7 dtype: int64 Squeezing is even more effective when used with DataFrames. >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) >>> df a b 0 1 2 1 3 4 Slicing a single column will produce a DataFrame with the columns having only one value: >>> df_a = df[['a']] >>> df_a a 0 1 1 3 So the columns can be squeezed down, resulting in a Series: >>> df_a.squeeze('columns') 0 1 1 3 Name: a, dtype: int64 Slicing a single row from a single column will produce a single scalar DataFrame: >>> df_0a = df.loc[df.index < 1, ['a']] >>> df_0a a 0 1 Squeezing the rows produces a single scalar Series: >>> df_0a.squeeze('rows') a 1 Name: 0, dtype: int64 Squeezing all axes will project directly into a scalar: >>> df_0a.squeeze() 1 c3l># UH)upUT;a[U5S:XaSO [S5v M+ g7f)rrN)rslice)r]r7rrs rr^"NDFrame.squeeze.."s30DA$Y3q6Q;E$K?0s14squeezer~) rrr!ilocr`r1rrrr)rrrrs @rrNDFrame.squeezes~P)- uT^^$4;P;PQU;V:X %dii0   fg & &((i(@F rignore)rr(rrrr:errorscUcUcUc [S5eUcUbUb [S5eUb [S5eO!U(aURU5S:XaUnOUnURU5 U(aUO$URU=(a [ 5(+S9n [ X#45GHupU cM UR U 5n [R"U 5n UbU RU5n[U 5(dU R(a$Ub!U RU5RU 5nOU RU 5nUS:XaL[XS:H5(a7[ U 5VVs/sHup/XS:XdMUPM nnn[US35eU R!XS 9nU R#UU S S S 9 U R%5 GM" U(aUR'U 5 gU R)US S9$s snnf)Nzmust pass an index to rename:Cannot specify both 'axis' and any of 'index' or 'columns'zem:; ;   3P!R" --d3q8  77@ diiT5WBUBW>Wi&X%./?%@ !G#(B**<8A ,,U3L))<GW$W]-C)D)D-6l,C&,CLE">R/,C#& #n%55G#HII++A+;I  $ $YWdQV $ W  $ $ &7&A:    (&&tH&= =&s )G1;G1.)rr(rrrcgrrrrrr(rrrs r rename_axisNDFrame.rename_axisv r)rr(rrcgrrrs rrrrrcgrrrs rrrrrcjX#S.nUbURU5n[US5nU(a[5(aSnU[RLaV[ U5=(d" [ U5=(a [U5(+nU(aURXXeS9$[S5eU(aUOURUS9n [UR5HnURURU55n U [RLaM8[ U 5=(d" [ U 5=(a [U 5(+nU(aU n OH[R "U 5n UR#U5R$n U Vs/sH o"U5PM n nU RXSUS9 M U(dU $gs snf) ap Set the name of the axis for the index or columns. Parameters ---------- mapper : scalar, list-like, optional Value to set the axis name attribute. index, columns : scalar, list-like, dict-like or function, optional A scalar, list-like, dict-like or functions transformations to apply to that axis' values. Note that the ``columns`` parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects. Use either ``mapper`` and ``axis`` to specify the axis to target with ``mapper``, or ``index`` and/or ``columns``. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to rename. For `Series` this parameter is unused and defaults to 0. copy : bool, default None Also copy underlying data. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` inplace : bool, default False Modifies the object directly, instead of creating a new Series or DataFrame. Returns ------- Series, DataFrame, or None The same type as the caller or None if ``inplace=True``. See Also -------- Series.rename : Alter Series index labels or name. DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index. Notes ----- ``DataFrame.rename_axis`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter ``copy`` is ignored. The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis *labels*. We *highly* recommend using keyword arguments to clarify your intent. Examples -------- **Series** >>> s = pd.Series(["dog", "cat", "monkey"]) >>> s 0 dog 1 cat 2 monkey dtype: object >>> s.rename_axis("animal") animal 0 dog 1 cat 2 monkey dtype: object **DataFrame** >>> df = pd.DataFrame({"num_legs": [4, 4, 2], ... "num_arms": [0, 0, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs num_arms dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("animal") >>> df num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("limbs", axis="columns") >>> df limbs num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 **MultiIndex** >>> df.index = pd.MultiIndex.from_product([['mammal'], ... ['dog', 'cat', 'monkey']], ... names=['type', 'name']) >>> df limbs num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2 >>> df.rename_axis(index={'type': 'class'}) limbs num_legs num_arms class name mammal dog 4 0 cat 4 0 monkey 2 2 >>> df.rename_axis(columns=str.upper) LIMBS num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2 rr(NrFrz,Use `.rename` to alter labels with a mapper.rT)r!rXrr no_defaultrgrcra_set_axis_namer rrrrr%rsrrr2)rrrr(rrrr non_mapperrrFnewnamesrcurnamesr8s rrrs~b3  ((.D%gy9 '))D  '"6*V$A\&-A)A **w+!!OPP%T$)))*>Fdnn-HHT0067&&q\Vl1o.UlSToBU  H2215A#~~d399H4<=HD$HH=%%h4d%S.  >s>F0cURU5nURU5RU5n[US5nU(aUOUR US9nUS:XaXVlOXVlU(dU$g)a Set the name(s) of the axis. Parameters ---------- name : str or list of str Name(s) to set. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to set the label. The value 0 or 'index' specifies index, and the value 1 or 'columns' specifies columns. inplace : bool, default False If `True`, do operation inplace and return None. copy: Whether to make a copy of the result. Returns ------- Series, DataFrame, or None The same type as the caller or `None` if `inplace` is `True`. See Also -------- DataFrame.rename : Alter the axis labels of :class:`DataFrame`. Series.rename : Alter the index labels or set the index name of :class:`Series`. Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`. Examples -------- >>> df = pd.DataFrame({"num_legs": [4, 4, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs dog 4 cat 4 monkey 2 >>> df._set_axis_name("animal") num_legs animal dog 4 cat 4 monkey 2 >>> df.index = pd.MultiIndex.from_product( ... [["mammal"], ['dog', 'cat', 'monkey']]) >>> df._set_axis_name(["type", "name"]) num_legs type name mammal dog 4 cat 4 monkey 2 rrrN)r!r set_namesrXrrr()rr8rrridxrenameds rrNDFrame._set_axis_nameXsln$$T*nnT",,T2%gy9!$tyydy'; 19M!ONrcF^^[UU4SjTR55$)Nc3># UH4nTRU5RTRU55v M6 g7fr)requals)r]rotherrs rr^(NDFrame._indexed_same..s5 BSQDNN1  $ $U__Q%7 8 8BSs>> df = pd.DataFrame({1: [10], 2: [20]}) >>> df 1 2 0 10 20 DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True. >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]}) >>> exactly_equal 1 2 0 10 20 >>> df.equals(exactly_equal) True DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True. >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]}) >>> different_column_type 1.0 2.0 0 10 20 >>> df.equals(different_column_type) True DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types. >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]}) >>> different_data_type 1 2 0 10.0 20.0 >>> df.equals(different_data_type) False F)rr r rrrrs rrNDFrame.equalssN^5$t*--D$u+1N1NWe$yy ++rcSSjnURRU5nURX"RS9nUR USS9$)Nc[UR5(a[R"U5$[R"U5$r)r`roperatorinvnegrs rblk_func!NDFrame.__neg__..blk_funcs3V\\** ||F++  ||F++rr__neg__r~rrrapplyrrrrrnew_dataress rrNDFrame.__neg__sH ,99??8,(( (FY77rcSSjnURRU5nURX"RS9nUR USS9$)Nc[UR5(aUR5$[R"U5$r)r`rrrposrs rr!NDFrame.__pos__..blk_funcs.V\\**{{}$  ||F++rr__pos__r~rrrs rrNDFrame.__pos__sH ,99??8,(( (FY77rcUR(dURSS9$URR[R 5nUR XRS9nURUSS9$)NFrr __invert__r~) rjrrrrinvertrrr)rrrs rrNDFrame.__invert__s]yy99%9( (99??8??3(( (F\::rcF[S[U5RS35e)NzThe truth value of a zC is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().)r r rrs r __nonzero__NDFrame.__nonzero__'s-#DJ$7$7#89C C  rcv[R"[U5RS3[[ 5S9 UR 5n[U[[R45(a [U5$[U5(a![S[U5R35eUR5 g)a Return the bool of a single element Series or DataFrame. .. deprecated:: 2.1.0 bool is deprecated and will be removed in future version of pandas. For ``Series`` use ``pandas.Series.item``. This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception). Returns ------- bool The value in the Series or DataFrame. See Also -------- Series.astype : Change the data type of a Series, including to boolean. DataFrame.astype : Change the data type of a DataFrame, including to boolean. numpy.bool_ : NumPy boolean data type, used by pandas for boolean values. Examples -------- The method will only work for single element objects with a boolean value: >>> pd.Series([True]).bool() # doctest: +SKIP True >>> pd.Series([False]).bool() # doctest: +SKIP False >>> pd.DataFrame({'col': [True]}).bool() # doctest: +SKIP True >>> pd.DataFrame({'col': [False]}).bool() # doctest: +SKIP False This is an alternative method and will only work for single element objects with a boolean value: >>> pd.Series([True]).item() # doctest: +SKIP True >>> pd.Series([False]).item() # doctest: +SKIP False zG.bool is now deprecated and will be removed in future version of pandasrz0bool cannot act on a non-boolean single element T)rr r rrrUrrboolrhbool_rgr r)rrFs rr NDFrame.bool0s`  Dz""#$* * ')  LLN a$) * *7N q\\B:&&')  rcURR[R5nUR XR S9R USS9$)a& Return a Series/DataFrame with absolute numeric value of each element. This function only applies to elements that are all numeric. Returns ------- abs Series/DataFrame containing the absolute value of each element. See Also -------- numpy.absolute : Calculate the absolute value element-wise. Notes ----- For ``complex`` inputs, ``1.2 + 1j``, the absolute value is :math:`\sqrt{ a^2 + b^2 }`. Examples -------- Absolute numeric values in a Series. >>> s = pd.Series([-1.10, 2, -3.33, 4]) >>> s.abs() 0 1.10 1 2.00 2 3.33 3 4.00 dtype: float64 Absolute numeric values in a Series with complex numbers. >>> s = pd.Series([1.2 + 1j]) >>> s.abs() 0 1.56205 dtype: float64 Absolute numeric values in a Series with a Timedelta element. >>> s = pd.Series([pd.Timedelta('1 days')]) >>> s.abs() 0 1 days dtype: timedelta64[ns] Select rows with data closest to certain value using argsort (from `StackOverflow `__). >>> df = pd.DataFrame({ ... 'a': [4, 5, 6, 7], ... 'b': [10, 20, 30, 40], ... 'c': [100, 50, -30, -50] ... }) >>> df a b c 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50 >>> df.loc[(df.c - 43).abs().argsort()] a b c 1 5 20 50 0 4 10 100 2 6 30 -30 3 7 40 -50 rabs)r8)rrrhrrrr)rres_mgrs rr NDFrame.absssKH))//"&&)))' )ERR uS  rc"UR5$r)rrs r__abs__NDFrame.__abs__sxxzrc@URU5RUSS9$)N __round__r~)roundr)rdecimalss rrNDFrame.__round__s!zz(#00k0JJrcURU5nUSL=(aH [U5=(a6 XRUR;=(a UR XS9(+$)a Test whether a key is a level reference for a given axis. To be considered a level reference, `key` must be a string that: - (axis=0): Matches the name of an index level and does NOT match a column label. - (axis=1): Matches the name of a column level and does NOT match an index label. Parameters ---------- key : Hashable Potential level name for the given axis axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- is_level : bool Nr)r!rmrr2_is_label_reference)rr9raxis_ints r_is_level_referenceNDFrame._is_level_referencesg,((. tO AC  Ayy*000 A,,S,@@  rc^^^TRU5mU4Sj[TR55nTSL=(a( [T5=(a [ UU4SjU55$)a Test whether a key is a label reference for a given axis. To be considered a label reference, `key` must be a string that: - (axis=0): Matches a column label - (axis=1): Matches an index label Parameters ---------- key : Hashable Potential label name, i.e. Index entry. axis : int, default 0 Axis perpendicular to the axis that labels are associated with (0 means search for column labels, 1 means search for index labels) Returns ------- is_label: bool c36># UHoT:wdM Uv M g7frrr]rr s rr^.NDFrame._is_label_reference..K#8R(Nbb#8  Nc3H># UHnTTRU;v M g7frrr]rr9rs rr^r>:RC499R=(:")r!rrrmany)rr9r other_axesr s`` @rr NDFrame._is_label_referencesV*((.K5#8K  tO ?C  ?>:>> rcLURXS9=(d URXS9$)a Test whether a key is a label or level reference for a given axis. To be considered either a label or a level reference, `key` must be a string that: - (axis=0): Matches a column label or an index level - (axis=1): Matches an index label or a column level Parameters ---------- key : Hashable Potential label or level name axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- bool r)r r )rr9rs r_is_label_or_level_reference$NDFrame._is_label_or_level_reference s5*'''7 4;S;S # UHoT:wdM Uv M g7frrrs rr^:NDFrame._check_label_or_level_ambiguity..7rrNc3H># UHnTTRU;v M g7frrrs rr^r=rrr)anr)rcolumnr{z ' is both  z level and z label, which is ambiguous.)r!rrrmrr2rr ) rr9rr level_article level_type label_article label_typemsgr s `` @r_check_label_or_level_ambiguity'NDFrame._check_label_or_level_ambiguity"s(((.K5#8K  OC  tyy*000>:>>>$,q=o &M $,q=o &M C5 =/:,k /:,.IK S/ !?1! rc\URU5n[UR5Vs/sH o3U:wdM UPM nnURXS9(a,UR XS9 UR XSS9R nOHURXS9(a)URURU5R nO [U5eURS:aQU(a*[URUS5[5(aSnOSnUS:XaSOSn[SUS US U35eU$s snf) a Return a 1-D array of values associated with `key`, a label or level from the given `axis`. Retrieval logic: - (axis=0): Return column values if `key` matches a column label. Otherwise return index level values if `key` matches an index level. - (axis=1): Return row values if `key` matches an index label. Otherwise return column level values if 'key' matches a column level Parameters ---------- key : Hashable Label or level name. axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- np.ndarray or ExtensionArray Raises ------ KeyError if `key` matches neither a label nor a level ValueError if `key` matches multiple labels rrrzX For a multi-index, the label must be a tuple with elements corresponding to each level.r"rzThe z label 'z' is not unique.)r!rrr r)xsrr rr3rr,rrrr )rr9rrrr multi_messagelabel_axis_names r_get_label_or_level_values"NDFrame._get_label_or_level_valuesNs'@$$T*#(#8G#8R$Jb#8 G  # #C # 3  0 0 0 @WWS!}W5==F  % %c % 5YYt_55c:BBF3-  ;;?j 1 )F SSG !# *.!)hO'xu4D]OT  5Hs D)D)c URU5n[R"U5nUVs/sHo0RX2S9(aMUPM nnU(a[ SUSU35eUVs/sHo0R X2S9(dMUPM nnUVs/sHo0R X2S9(aMUPM nnUR SS9nUS:Xa2U(aURUSSS9 U(aURUS SS 9 U$U(ad[UR[5(a!URRU5Ul O$[URR5Ul U(aURUSSS 9 U$s snfs snfs snf) ai Drop labels and/or levels for the given `axis`. For each key in `keys`: - (axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level. - (axis=1): If key matches an index label then drop the row. Otherwise if key matches a column level then drop the level. Parameters ---------- keys : str or list of str labels or levels to drop axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- dropped: DataFrame Raises ------ ValueError if any `keys` match neither a label nor a level rz;The following keys are not valid labels or levels for axis z: FrrT)droprrrr)r!rsmaybe_make_listrr r r reset_indexr3rr(rrrrj)rkeysrrE invalid_keyslevels_to_droplabels_to_dropdroppeds r_drop_labels_or_levelsNDFrame._drop_labels_or_levelss{6$$T*%%d+ !#D#DQ#D#RAt   ##'&<.:  &*TT-E-Ea-E-S!TT%)XT1I1I!1I1W!TX )))' 19##Nt#L ^!T B gooz::&-oo&?&?&OGO'11E1E&FGO ^!T BW UXs#FF+FFF -F zClassVar[None]__hash__c,[UR5$)z Iterate over info axis. Returns ------- iterator Info axis as iterator. Examples -------- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) >>> for x in df: ... print(x) A B )iterrRrs r__iter__NDFrame.__iter__s"DOO$$rcUR$)aV Get the 'info axis' (see Indexing for more). This is index for Series, columns for DataFrame. Returns ------- Index Info axis. Examples -------- >>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]}, ... index=['a', 'b', 'c']) >>> d A B a 1 0 b 2 4 c 3 8 >>> d.keys() Index(['A', 'B'], dtype='object') rRrs rr7 NDFrame.keyss.rc#B# URH nXU4v M g7f)z{ Iterate over (label, values) on info axis This is index for Series and columns for DataFrame. Returns ------- Generator NrD)rhs rr NDFrame.items s!A!W* !sc,[UR5$)zReturns length of info axis)rrRrs r__len__NDFrame.__len__s4??##rcXR;$)z#True if the key is in the info axisrD)rr9s r __contains__NDFrame.__contains__soo%%rcB^[U4SjTR55$)a[ Indicator whether Series/DataFrame is empty. True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0. Returns ------- bool If Series/DataFrame is empty, return True, if not return False. See Also -------- Series.dropna : Return series without null values. DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. Notes ----- If Series/DataFrame contains only NaNs, it is still not considered empty. See the example below. Examples -------- An example of an actual empty DataFrame. Notice the index is empty: >>> df_empty = pd.DataFrame({'A' : []}) >>> df_empty Empty DataFrame Columns: [A] Index: [] >>> df_empty.empty True If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty: >>> df = pd.DataFrame({'A' : [np.nan]}) >>> df A 0 NaN >>> df.empty False >>> df.dropna().empty True >>> ser_empty = pd.Series({'A' : []}) >>> ser_empty A [] dtype: object >>> ser_empty.empty False >>> ser_empty = pd.Series() >>> ser_empty.empty True c3^># UH"n[TRU55S:Hv M$ g7f)rNr[r\s rr^ NDFrame.empty..\s&J8I13t~~a()Q.8Is*-)rrrs`rempty NDFrame.empty"stJ8I8IJJJri__array_priority__cURn[R"X1S9n[URUR5(a[ 5(aUR R(ax[URRSUR5(aF[URUR5(a!UR5nSURl U$)NrrF) rrhasarrayr[rrrrrNrviewr writeable)rrrrarrs r __array__NDFrame.__array__esjj- 6<< 3 3#%% ))dkk..q16<<@@^ ciiFFhhj&+ # rc8[R"XU/UQ70UD6$r)rr array_ufunc)rufuncrinputsrs r__array_ufunc__NDFrame.__array_ufunc__ws!$$T&L6LVLLrc *URVs0sHo[XS5_M nnURURURURUR R Vs0sHoUR U_M snS.UE$s snfs snf)N)rrrrr)rr0rrrr_keys)rrEmetas r __getstate__NDFrame.__getstate__s37>>B>a74D))>BIIIIZZ151A1AB1AA$**Q-'1AB     C Cs B +B c [U[5(aXlGOf[U[5(Ga6SU;aSU;aUR S5US'UR S5nUbUR S05nUc0n[ RUSU5 UR SSS05n[ RUS[U40UD65 [URUR-5n[U5H,nXa;dM US:wdMXn[ RXU5 M. UR5H"upgXe;dM [ RXU5 M$ O%[S5e[U5S :Xa [S5e0Ulg) Nr rrrrrTz(Pre-0.12 pickles are no longer supportedr+)rrrrrrrrr|setrrlistrrrr)rstaterrrrdrErFs r __setstate__NDFrame.__setstate__sT e\ * *I t $ $%F%$7 % ' 2f ))F#C (B/=E""459 (-F,MN""453G3GH 4//$..@AdAza8m!H**4A6$ "KKMDA}**4A6* **TUU Z1_%&PQ Q35rc~SSR[[U55S3n[U5RSUS3$)N[,]())joinmaprr r)rpreprs r__repr__NDFrame.__repr__sACHHSt456a8t*%%&awa00rcX[R"S5S:XaUR5$g)z} Returns a LaTeX representation for a particular object. Mainly for use with nbconvert (jupyter notebook conversion to pdf). zstyler.render.reprlatexN)r get_optionto_latexrs r _repr_latex_NDFrame._repr_latex_s'   1 2g ===? "rc[R"S5(a\UR[R"S55nURSS9n[ [ U5n[ U[RS9$g)zP Not a real Jupyter special repr method, but we use the same naming convention. zdisplay.html.table_schemazdisplay.max_rowstable)orient)object_pairs_hookN) rrzheadto_jsonr rr collections OrderedDict)rras_jsons r_repr_data_resource_NDFrame._repr_data_resource_sd   8 9 999V../ABCDll'l2G3(GK4K4KL L :rz3.0r excel_writerto_excel)version allowed_argsr8storage_optionsz1.2.0)rrstorage_options_versionaddedr,c Uc0n[U[5(aUOUR5nSSKJn U"UUUUUUUU U S9 nUR UUU U UU UUS9 g)a Write {klass} to an Excel sheet. To write a single {klass} to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an `ExcelWriter` object with a target file name, and specify a sheet in the file to write to. Multiple sheets may be written to by specifying unique `sheet_name`. With all data written to the file it is necessary to save the changes. Note that creating an `ExcelWriter` object with a file name that already exists will result in the contents of the existing file being erased. Parameters ---------- excel_writer : path-like, file-like, or ExcelWriter object File path or existing ExcelWriter. sheet_name : str, default 'Sheet1' Name of sheet which will contain DataFrame. na_rep : str, default '' Missing data representation. float_format : str, optional Format string for floating point numbers. For example ``float_format="%.2f"`` will format 0.1234 to 0.12. columns : sequence or list of str, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of string is given it is assumed to be aliases for the column names. index : bool, default True Write row names (index). index_label : str or sequence, optional Column label for index column(s) if desired. If not specified, and `header` and `index` are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. startrow : int, default 0 Upper left cell row to dump data frame. startcol : int, default 0 Upper left cell column to dump data frame. engine : str, optional Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this via the options ``io.excel.xlsx.writer`` or ``io.excel.xlsm.writer``. merge_cells : bool, default True Write MultiIndex and Hierarchical Rows as merged cells. inf_rep : str, default 'inf' Representation for infinity (there is no native representation for infinity in Excel). freeze_panes : tuple of int (length 2), optional Specifies the one-based bottommost row and rightmost column that is to be frozen. {storage_options} .. versionadded:: {storage_options_versionadded} engine_kwargs : dict, optional Arbitrary keyword arguments passed to excel engine. See Also -------- to_csv : Write DataFrame to a comma-separated values (csv) file. ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame. io.formats.style.Styler.to_excel : Add styles to Excel sheet. Notes ----- For compatibility with :meth:`~DataFrame.to_csv`, to_excel serializes lists and dicts to strings before writing. Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook. Examples -------- Create, write to and save a workbook: >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df1.to_excel("output.xlsx") # doctest: +SKIP To specify the sheet name: >>> df1.to_excel("output.xlsx", ... sheet_name='Sheet_name_1') # doctest: +SKIP If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object: >>> df2 = df1.copy() >>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP ... df1.to_excel(writer, sheet_name='Sheet_name_1') ... df2.to_excel(writer, sheet_name='Sheet_name_2') ExcelWriter can also be used to append to an existing Excel file: >>> with pd.ExcelWriter('output.xlsx', ... mode='a') as writer: # doctest: +SKIP ... df1.to_excel(writer, sheet_name='Sheet_name_3') To set the library that is used to write the Excel file, you can pass the `engine` keyword (the default engine is automatically chosen depending on the file extension): >>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP Nr)ExcelFormatter)na_repcolsheader float_formatr index_label merge_cellsinf_rep) sheet_namestartrowstartcol freeze_panesenginer engine_kwargs)rrkto_framepandas.io.formats.excelrwrite)rrrrrr(rrrrrrrrrrrrr formatters rrNDFrame.to_excels|R  Ml33T:" %##    !%+'  r path_or_bufrcompression_options)rrinfercSSKJn Uc US:XaSnOUcSn[R"U 5 U =(d Sn UR UUUUUUUUUU U U U U S9$)a Convert the object to a JSON string. Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters ---------- path_or_buf : str, path object, file-like object, or None, default None String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. orient : str Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * The format of the JSON string: - 'split' : dict like {{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} Describing the data, where data component is like ``orient='records'``. date_format : {{None, 'epoch', 'iso'}} Type of date conversion. 'epoch' = epoch milliseconds, 'iso' = ISO8601. The default depends on the `orient`. For ``orient='table'``, the default is 'iso'. For all other orients, the default is 'epoch'. double_precision : int, default 10 The number of decimal places to use when encoding floating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError. force_ascii : bool, default True Force encoded string to be ASCII. date_unit : str, default 'ms' (milliseconds) The time unit to encode to, governs timestamp and ISO8601 precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively. default_handler : callable, default None Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object. lines : bool, default False If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. index : bool or None, default None The index is only used when 'orient' is 'split', 'index', 'column', or 'table'. Of these, 'index' and 'column' do not support `index=False`. indent : int, optional Length of whitespace used to indent each record. {storage_options} mode : str, default 'w' (writing) Specify the IO mode for output when supplying a path_or_buf. Accepted args are 'w' (writing) and 'a' (append) only. mode='a' is only supported when lines is True and orient is 'records'. Returns ------- None or str If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None. See Also -------- read_json : Convert a JSON string to pandas object. Notes ----- The behavior of ``indent=0`` varies from the stdlib, which does not indent the output but does insert newlines. Currently, ``indent=0`` and the default ``indent=None`` are equivalent in pandas, though this may change in a future release. ``orient='table'`` contains a 'pandas_version' field under 'schema'. This stores the version of `pandas` used in the latest revision of the schema. Examples -------- >>> from json import loads, dumps >>> df = pd.DataFrame( ... [["a", "b"], ["c", "d"]], ... index=["row 1", "row 2"], ... columns=["col 1", "col 2"], ... ) >>> result = df.to_json(orient="split") >>> parsed = loads(result) >>> dumps(parsed, indent=4) # doctest: +SKIP {{ "columns": [ "col 1", "col 2" ], "index": [ "row 1", "row 2" ], "data": [ [ "a", "b" ], [ "c", "d" ] ] }} Encoding/decoding a Dataframe using ``'records'`` formatted JSON. Note that index labels are not preserved with this encoding. >>> result = df.to_json(orient="records") >>> parsed = loads(result) >>> dumps(parsed, indent=4) # doctest: +SKIP [ {{ "col 1": "a", "col 2": "b" }}, {{ "col 1": "c", "col 2": "d" }} ] Encoding/decoding a Dataframe using ``'index'`` formatted JSON: >>> result = df.to_json(orient="index") >>> parsed = loads(result) >>> dumps(parsed, indent=4) # doctest: +SKIP {{ "row 1": {{ "col 1": "a", "col 2": "b" }}, "row 2": {{ "col 1": "c", "col 2": "d" }} }} Encoding/decoding a Dataframe using ``'columns'`` formatted JSON: >>> result = df.to_json(orient="columns") >>> parsed = loads(result) >>> dumps(parsed, indent=4) # doctest: +SKIP {{ "col 1": {{ "row 1": "a", "row 2": "c" }}, "col 2": {{ "row 1": "b", "row 2": "d" }} }} Encoding/decoding a Dataframe using ``'values'`` formatted JSON: >>> result = df.to_json(orient="values") >>> parsed = loads(result) >>> dumps(parsed, indent=4) # doctest: +SKIP [ [ "a", "b" ], [ "c", "d" ] ] Encoding with Table Schema: >>> result = df.to_json(orient="table") >>> parsed = loads(result) >>> dumps(parsed, indent=4) # doctest: +SKIP {{ "schema": {{ "fields": [ {{ "name": "index", "type": "string" }}, {{ "name": "col 1", "type": "string" }}, {{ "name": "col 2", "type": "string" }} ], "primaryKey": [ "index" ], "pandas_version": "1.4.0" }}, "data": [ {{ "index": "row 1", "col 1": "a", "col 2": "b" }}, {{ "index": "row 2", "col 1": "c", "col 2": "d" }} ] }} r)jsonrisoepoch)rrr date_formatdouble_precision force_ascii date_unitdefault_handlerlines compressionrindentrmode) pandas.iorris_nonnegative_intr)rrrrrrrrrrrrrrrs rrNDFrame.to_json| s|P #  6W#4K  !K!!&)1||##-#+#+  rto_hdfcJSSKJn URUUUUUUUUUU U U U U US9 g)ap Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key. .. warning:: One can store a subclass of ``DataFrame`` or ``Series`` to HDF5, but the type of the subclass is lost upon storing. For more information see the :ref:`user guide `. Parameters ---------- path_or_buf : str or pandas.HDFStore File path or HDFStore object. key : str Identifier for the group in the store. mode : {'a', 'w', 'r+'}, default 'a' Mode to open file: - 'w': write, a new file is created (an existing file with the same name would be deleted). - 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created. - 'r+': similar to 'a', but the file must already exist. complevel : {0-9}, default None Specifies a compression level for data. A value of 0 or None disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. append : bool, default False For Table formats, append the input data to the existing. format : {'fixed', 'table', None}, default 'fixed' Possible values: - 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable. - 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. - If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed". index : bool, default True Write DataFrame index as a column. min_itemsize : dict or int, optional Map column names to minimum string sizes for columns. nan_rep : Any, optional How to represent null values as str. Not allowed with append=True. dropna : bool, default False, optional Remove missing values. data_columns : list of columns or True, optional List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See :ref:`Query via data columns`. for more information. Applicable only to format='table'. errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. encoding : str, default "UTF-8" See Also -------- read_hdf : Read from HDF file. DataFrame.to_orc : Write a DataFrame to the binary orc format. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a SQL table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file. Examples -------- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, ... index=['a', 'b', 'c']) # doctest: +SKIP >>> df.to_hdf('data.h5', key='df', mode='w') # doctest: +SKIP We can add another object to the same file: >>> s = pd.Series([1, 2, 3, 4]) # doctest: +SKIP >>> s.to_hdf('data.h5', key='s') # doctest: +SKIP Reading from HDF file: >>> pd.read_hdf('data.h5', 'df') # doctest: +SKIP A B a 1 4 b 2 5 c 3 6 >>> pd.read_hdf('data.h5', 's') # doctest: +SKIP 0 1 1 2 2 3 3 4 dtype: int64 r)pytables) r complevelcomplibappendformatr min_itemsizenan_repdropna data_columnsrencodingN)rrr)rrr9rrrrrrrrrrrrrs rrNDFrame.to_hdf sHH '    %%  r)rr8conto_sqlc >SSKJn U RUUUUUUUUUU S9 $)a Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten. Parameters ---------- name : str Name of SQL table. con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. See `here `_. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion. schema : str, optional Specify the schema (if database flavor supports this). If None, use default schema. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists. * fail: Raise a ValueError. * replace: Drop the table before inserting new values. * append: Insert new values to the existing table. index : bool, default True Write DataFrame index as a column. Uses `index_label` as the column name in the table. Creates a table index for this column. index_label : str or sequence, default None Column label for index column(s). If None is given (default) and `index` is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. chunksize : int, optional Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once. dtype : dict or scalar, optional Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns. method : {None, 'multi', callable}, optional Controls the SQL insertion clause used: * None : Uses standard SQL ``INSERT`` clause (one per row). * 'multi': Pass multiple values in a single ``INSERT`` clause. * callable with signature ``(pd_table, conn, keys, data_iter)``. Details and a sample callable implementation can be found in the section :ref:`insert method `. Returns ------- None or int Number of rows affected by to_sql. None is returned if the callable passed into ``method`` does not return an integer number of rows. The number of returned rows affected is the sum of the ``rowcount`` attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable which may not reflect the exact number of written rows as stipulated in the `sqlite3 `__ or `SQLAlchemy `__. .. versionadded:: 1.4.0 Raises ------ ValueError When the table already exists and `if_exists` is 'fail' (the default). See Also -------- read_sql : Read a DataFrame from a table. Notes ----- Timezone aware datetime columns will be written as ``Timestamp with timezone`` type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone. Not all datastores support ``method="multi"``. Oracle, for example, does not support multi-value insert. References ---------- .. [1] https://docs.sqlalchemy.org .. [2] https://www.python.org/dev/peps/pep-0249/ Examples -------- Create an in-memory SQLite database. >>> from sqlalchemy import create_engine >>> engine = create_engine('sqlite://', echo=False) Create a table from scratch with 3 rows. >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']}) >>> df name 0 User 1 1 User 2 2 User 3 >>> df.to_sql(name='users', con=engine) 3 >>> from sqlalchemy import text >>> with engine.connect() as conn: ... conn.execute(text("SELECT * FROM users")).fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')] An `sqlalchemy.engine.Connection` can also be passed to `con`: >>> with engine.begin() as connection: ... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']}) ... df1.to_sql(name='users', con=connection, if_exists='append') 2 This is allowed to support operations that require that the same DBAPI connection is used for the entire operation. >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']}) >>> df2.to_sql(name='users', con=engine, if_exists='append') 2 >>> with engine.connect() as conn: ... conn.execute(text("SELECT * FROM users")).fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'), (0, 'User 4'), (1, 'User 5'), (0, 'User 6'), (1, 'User 7')] Overwrite the table with just ``df2``. >>> df2.to_sql(name='users', con=engine, if_exists='replace', ... index_label='id') 2 >>> with engine.connect() as conn: ... conn.execute(text("SELECT * FROM users")).fetchall() [(0, 'User 6'), (1, 'User 7')] Use ``method`` to define a callable insertion method to do nothing if there's a primary key conflict on a table in a PostgreSQL database. >>> from sqlalchemy.dialects.postgresql import insert >>> def insert_on_conflict_nothing(table, conn, keys, data_iter): ... # "a" is the primary key in "conflict_table" ... data = [dict(zip(keys, row)) for row in data_iter] ... stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"]) ... result = conn.execute(stmt) ... return result.rowcount >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing) # doctest: +SKIP 0 For MySQL, a callable to update columns ``b`` and ``c`` if there's a conflict on a primary key. >>> from sqlalchemy.dialects.mysql import insert >>> def insert_on_conflict_update(table, conn, keys, data_iter): ... # update columns "b" and "c" on primary key conflict ... data = [dict(zip(keys, row)) for row in data_iter] ... stmt = ( ... insert(table.table) ... .values(data) ... ) ... stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c) ... result = conn.execute(stmt) ... return result.rowcount >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update) # doctest: +SKIP 2 Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars. >>> df = pd.DataFrame({"A": [1, None, 2]}) >>> df A 0 1.0 1 NaN 2 2.0 >>> from sqlalchemy.types import Integer >>> df.to_sql(name='integers', con=engine, index=False, ... dtype={"A": Integer()}) 3 >>> with engine.connect() as conn: ... conn.execute(text("SELECT * FROM integers")).fetchall() [(1,), (None,), (2,)] r)sql)schema if_existsrr chunksizerr)rrr) rr8rrrrrrrrrs rrNDFrame.to_sql9 s<h "zz   #  rpath to_picklec$SSKJn U"UUUUUS9 g)as Pickle (serialize) object to file. Parameters ---------- path : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. File path where the pickled object will be stored. {compression_options} protocol : int Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL. .. [1] https://docs.python.org/3/library/pickle.html. {storage_options} See Also -------- read_pickle : Load pickled pandas object (or any object) from file. DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Examples -------- >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}}) # doctest: +SKIP >>> original_df # doctest: +SKIP foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> original_df.to_pickle("./dummy.pkl") # doctest: +SKIP >>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP >>> unpickled_df # doctest: +SKIP foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 r)r)rprotocolrN)pandas.io.pickler)rrrrrrs rrNDFrame.to_pickle s~ /  #+  r to_clipboardc :SSKJn UR"U4XS.UD6 g)a\ Copy object to the system clipboard. Write a text representation of object to the system clipboard. This can be pasted into Excel, for example. Parameters ---------- excel : bool, default True Produce output in a csv format for easy pasting into excel. - True, use the provided separator for csv pasting. - False, write a string representation of the object to the clipboard. sep : str, default ``'\t'`` Field delimiter. **kwargs These parameters will be passed to DataFrame.to_csv. See Also -------- DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_csv. Notes ----- Requirements for your platform. - Linux : `xclip`, or `xsel` (with `PyQt4` modules) - Windows : none - macOS : none This method uses the processes developed for the package `pyperclip`. A solution to render any output string format is given in the examples. Examples -------- Copy the contents of a DataFrame to the clipboard. >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C']) >>> df.to_clipboard(sep=',') # doctest: +SKIP ... # Wrote the following to the system clipboard: ... # ,A,B,C ... # 0,1,2,3 ... # 1,4,5,6 We can omit the index by passing the keyword `index` and setting it to false. >>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP ... # Wrote the following to the system clipboard: ... # A,B,C ... # 1,2,3 ... # 4,5,6 Using the original `pyperclip` package for any string output format. .. code-block:: python import pyperclip html = df.style.to_html() pyperclip.copy(html) r) clipboards)excelsepN)rrr)rrrrrs rrNDFrame.to_clipboarde s P )EEEfErc[S5nURS:XaURRU5$URR U5$)al Return an xarray object from the pandas object. Returns ------- xarray.DataArray or xarray.Dataset Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series. See Also -------- DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Notes ----- See the `xarray docs `__ Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2), ... ('parrot', 'bird', 24.0, 2), ... ('lion', 'mammal', 80.5, 4), ... ('monkey', 'mammal', np.nan, 4)], ... columns=['name', 'class', 'max_speed', ... 'num_legs']) >>> df name class max_speed num_legs 0 falcon bird 389.0 2 1 parrot bird 24.0 2 2 lion mammal 80.5 4 3 monkey mammal NaN 4 >>> df.to_xarray() # doctest: +SKIP Dimensions: (index: 4) Coordinates: * index (index) int64 32B 0 1 2 3 Data variables: name (index) object 32B 'falcon' 'parrot' 'lion' 'monkey' class (index) object 32B 'bird' 'bird' 'mammal' 'mammal' max_speed (index) float64 32B 389.0 24.0 80.5 nan num_legs (index) int64 32B 2 2 4 4 >>> df['max_speed'].to_xarray() # doctest: +SKIP array([389. , 24. , 80.5, nan]) Coordinates: * index (index) int64 0 1 2 3 >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01', ... '2018-01-02', '2018-01-02']) >>> df_multiindex = pd.DataFrame({'date': dates, ... 'animal': ['falcon', 'parrot', ... 'falcon', 'parrot'], ... 'speed': [350, 18, 361, 15]}) >>> df_multiindex = df_multiindex.set_index(['date', 'animal']) >>> df_multiindex speed date animal 2018-01-01 falcon 350 parrot 18 2018-01-02 falcon 361 parrot 15 >>> df_multiindex.to_xarray() # doctest: +SKIP Dimensions: (date: 2, animal: 2) Coordinates: * date (date) datetime64[ns] 2018-01-01 2018-01-02 * animal (animal) object 'falcon' 'parrot' Data variables: speed (date, animal) int64 350 18 361 15 xarrayr)rIr, DataArray from_seriesDatasetfrom_dataframe)rrs r to_xarrayNDFrame.to_xarray sFZ,H5 99>##//5 5>>006 6rbufcgrrrrr(rrr formattersrsparsify index_names bold_rows column_format longtableescaperdecimal multicolumnmulticolumn_formatmultirowcaptionrpositions rr{NDFrame.to_latex 2 rcgrrrs rr{r rrr{cz^^&URS:XaUR5nU c[R"S5S:Hn U c[R"S5S:Hn Uc[R"S5nUc[R"S5nUc[R"S 5nU b [ U [ 5(d [ S 5eUc[UR5O [U5n[ U[[45(a*[U5U:wa[ S US [U5S 35eUU (aSOSUS.nSS0UEnSS0UEn[ T[ 5(aU4Sjm&OTm&U&4SjnSn[ U[5(a7[UR5VVs0sHunnU[UUUS9_M nnnO[ U[5(aURSS5nURSS5nUbURSU05 UbURSU05 UnUR!SS9Rn U H,n!U!UR#5;dMURU!T&05 M. OUcTb [USS9nUU/n"/n#/n$U(a7U#R%URVs/sH nUU;dM UPM snSS.5 USLaU#R%SS05 O2[ U[[45(aU$R%USS.5 U/n"USLaU#R%SS05 U SLaU#R%SSS .5 SUUU (aSOSU(aUOS!U3U(aS"OS#UUUUU U(a![ UR&[(5(aS$OSU S%. n%UR+UU#U$SU0UEU"U%S&9$s snnfs snf)'a Render object to a LaTeX tabular, longtable, or nested table. Requires ``\usepackage{{booktabs}}``. The output can be copy/pasted into a main LaTeX document or read from an external file with ``\input{{table.tex}}``. .. versionchanged:: 2.0.0 Refactored to use the Styler implementation via jinja2 templating. Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. columns : list of label, optional The subset of columns to write. Writes all columns by default. header : bool or list of str, default True Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names. index : bool, default True Write row names (index). na_rep : str, default 'NaN' Missing data representation. formatters : list of functions or dict of {{str: function}}, optional Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns. float_format : one-parameter function or str, optional, default None Formatter for floating point numbers. For example ``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will both result in 0.1234 being formatted as 0.12. sparsify : bool, optional Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module. index_names : bool, default True Prints the names of the indexes. bold_rows : bool, default False Make the row labels bold in the output. column_format : str, optional The columns format as specified in `LaTeX table format `__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'. longtable : bool, optional Use a longtable environment instead of tabular. Requires adding a \usepackage{{longtable}} to your LaTeX preamble. By default, the value will be read from the pandas config module, and set to `True` if the option ``styler.latex.environment`` is `"longtable"`. .. versionchanged:: 2.0.0 The pandas option affecting this argument has changed. escape : bool, optional By default, the value will be read from the pandas config module and set to `True` if the option ``styler.format.escape`` is `"latex"`. When set to False prevents from escaping latex special characters in column names. .. versionchanged:: 2.0.0 The pandas option affecting this argument has changed, as has the default value to `False`. encoding : str, optional A string representing the encoding to use in the output file, defaults to 'utf-8'. decimal : str, default '.' Character recognized as decimal separator, e.g. ',' in Europe. multicolumn : bool, default True Use \multicolumn to enhance MultiIndex columns. The default will be read from the config module, and is set as the option ``styler.sparse.columns``. .. versionchanged:: 2.0.0 The pandas option affecting this argument has changed. multicolumn_format : str, default 'r' The alignment for multicolumns, similar to `column_format` The default will be read from the config module, and is set as the option ``styler.latex.multicol_align``. .. versionchanged:: 2.0.0 The pandas option affecting this argument has changed, as has the default value to "r". multirow : bool, default True Use \multirow to enhance MultiIndex rows. Requires adding a \usepackage{{multirow}} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module, and is set as the option ``styler.sparse.index``. .. versionchanged:: 2.0.0 The pandas option affecting this argument has changed, as has the default value to `True`. caption : str or tuple, optional Tuple (full_caption, short_caption), which results in ``\caption[short_caption]{{full_caption}}``; if a single string is passed, no short caption will be set. label : str, optional The LaTeX label to be placed inside ``\label{{}}`` in the output. This is used with ``\ref{{}}`` in the main ``.tex`` file. position : str, optional The LaTeX positional argument for tables, to be placed after ``\begin{{}}`` in the output. Returns ------- str or None If buf is None, returns the result as a string. Otherwise returns None. See Also -------- io.formats.style.Styler.to_latex : Render a DataFrame to LaTeX with conditional formatting. DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table. Notes ----- As of v2.0.0 this method has changed to use the Styler implementation as part of :meth:`.Styler.to_latex` via ``jinja2`` templating. This means that ``jinja2`` is a requirement, and needs to be installed, for this method to function. It is advised that users switch to using Styler, since that implementation is more frequently updated and contains much more flexibility with the output. Examples -------- Convert a general DataFrame to LaTeX with formatting: >>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'], ... age=[26, 45], ... height=[181.23, 177.65])) >>> print(df.to_latex(index=False, ... formatters={"name": str.upper}, ... float_format="{:.1f}".format, ... )) # doctest: +SKIP \begin{tabular}{lrr} \toprule name & age & height \\ \midrule RAPHAEL & 26 & 181.2 \\ DONATELLO & 45 & 177.7 \\ \bottomrule \end{tabular} rNzstyler.latex.environmentrzstyler.format.escaperyzstyler.sparse.columnszstyler.latex.multicol_alignzstyler.sparse.indexz&`column_format` must be str or unicodezWriting z cols but got z aliases)rrrrrc>TU-$rr)xrs r"NDFrame.to_latex..s |a7Grc`>[U[[45(a TbT"U5$U"U5$r)rfloatcomplex)r alt_format_ float_format_s r_wrapNDFrame.to_latex.._wrap s/!eW-..=3L$Q''"1~%r)r __index__ __columns__rr)includecU$rrrFs rrr&sqrr()subsetrF)rorrT)r2rznaive-tnaivezskip-last;data) hrules sparse_indexsparse_columns environmentmulticol_alignmultirow_alignrrrrrclinesrhide relabel_indexr format_index render_kwargs)r,rrrzrrr rr(rir`r1rrrr select_dtypesr7rrr_to_latex_via_styler)'rrr(rrrrrrrrrrrrrrrrrrrlength base_format_ index_format_column_format_r formatters_r7cindex_formattercolumn_formatter float_columnscol format_index_hide_relabel_index_render_kwargs_rs' ` @rr{r; s` 99>==?D  ))*DETI >&&'=>'IF   ++,CDK  %!'!2!23P!Q   (()>?H  $Z s-K-KEF F&-oT\\"3w< ftUm , ,V1Fxx~c&k](ST T!'gT *0(Cl(C *0!)D|)D lC ( (-GM(M & >B j$ ' '&dll33DAq75jm<<3 K D ) )(nn[$?O)~~mTB *$$k?%CD+%%{4D&EF$K ..w.?GGM$joo//&&]';<% L$<!%[AK&7 %'  LL*.,,K,Q!7:Jq,K%  U? LL&), - u . .  ! !VY"G H*OM E> LL&'* + %  LL49 :$&*3;1,-.%-c7  *Z J??'"# ((( (= =&( )  }6LsN2* N88N8r cSSKJn [SU5nU"USS9nSHin [5U n [ U [ 5(a[ X5"S 0U D6 M8[ U [5(dMOU Hn [ X5"S 0U D6 M Mk Uc0OUnURS5(aURS5 UR"S S U0UD6$) a" Render object to a LaTeX tabular, longtable, or nested table. Uses the ``Styler`` implementation with the following, ordered, method chaining: .. code-block:: python styler = Styler(DataFrame) styler.hide(**hide) styler.relabel_index(**relabel_index) styler.format(**format) styler.format_index(**format_index) styler.to_latex(buf=buf, **render_kwargs) Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. hide : dict, list of dict Keyword args to pass to the method call of ``Styler.hide``. If a list will call the method numerous times. relabel_index : dict, list of dict Keyword args to pass to the method of ``Styler.relabel_index``. If a list will call the method numerous times. format : dict, list of dict Keyword args to pass to the method call of ``Styler.format``. If a list will call the method numerous times. format_index : dict, list of dict Keyword args to pass to the method call of ``Styler.format_index``. If a list will call the method numerous times. render_kwargs : dict Keyword args to pass to the method call of ``Styler.to_latex``. Returns ------- str or None If buf is None, returns the result as a string. Otherwise returns None. r)Stylerrr,)uuid)rrrrrcg)Nztextbf:--rwrap;rrs rr.NDFrame._to_latex_via_styler..s'8rrr) pandas.io.formats.styler#r varsrrr0rir map_indexr{) rrrrrrrr#stylerkw_namekwsub_kws rrNDFrame._to_latex_via_styler[s` 3K&2&JGB"d##(.2.B%% FF,6v6! K,3   [ ) )   8 9838-88rcgrrrrrrrr(rrrrrrquoting quotecharlineterminatorrr doublequote escapecharrrrs rto_csvNDFrame.to_csvrrcgrrr0s rr6r7rrr6c[U[5(aUOUR5n[UUUUUUS9n[ U5R UUUU UU U UUU UU UUUUS9$)a Write object to a comma-separated values (csv) file. Parameters ---------- path_or_buf : str, path object, file-like object, or None, default None String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with `newline=''`, disabling universal newlines. If a binary file object is passed, `mode` might need to contain a `'b'`. sep : str, default ',' String of length 1. Field delimiter for the output file. na_rep : str, default '' Missing data representation. float_format : str, Callable, default None Format string for floating point numbers. If a Callable is given, it takes precedence over other numeric formatting parameters, like decimal. columns : sequence, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of strings is given it is assumed to be aliases for the column names. index : bool, default True Write row names (index). index_label : str or sequence, or False, default None Column label for index column(s) if desired. If None is given, and `header` and `index` are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R. mode : {{'w', 'x', 'a'}}, default 'w' Forwarded to either `open(mode=)` or `fsspec.open(mode=)` to control the file opening. Typical values include: - 'w', truncate the file first. - 'x', exclusive creation, failing if the file already exists. - 'a', append to the end of file if it exists. encoding : str, optional A string representing the encoding to use in the output file, defaults to 'utf-8'. `encoding` is not supported if `path_or_buf` is a non-binary file object. {compression_options} May be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'. Passing compression options as keys in dict is supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'. quoting : optional constant from csv module Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format` then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric. quotechar : str, default '\"' String of length 1. Character used to quote fields. lineterminator : str, optional The newline character or character sequence to use in the output file. Defaults to `os.linesep`, which depends on the OS in which this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.). .. versionchanged:: 1.5.0 Previously was line_terminator, changed for consistency with read_csv and the standard library 'csv' module. chunksize : int or None Rows to write at a time. date_format : str, default None Format string for datetime objects. doublequote : bool, default True Control quoting of `quotechar` inside a field. escapechar : str, default None String of length 1. Character used to escape `sep` and `quotechar` when appropriate. decimal : str, default '.' Character recognized as decimal separator. E.g. use ',' for European data. errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. {storage_options} Returns ------- None or str If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None. See Also -------- read_csv : Load a CSV file into a DataFrame. to_excel : Write DataFrame to an Excel file. Examples -------- Create 'out.csv' containing 'df' without indices >>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'], ... 'mask': ['red', 'purple'], ... 'weapon': ['sai', 'bo staff']}}) >>> df.to_csv('out.csv', index=False) # doctest: +SKIP Create 'out.zip' containing 'out.csv' >>> df.to_csv(index=False) 'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n' >>> compression_opts = dict(method='zip', ... archive_name='out.csv') # doctest: +SKIP >>> df.to_csv('out.zip', index=False, ... compression=compression_opts) # doctest: +SKIP To write a csv file to a new folder or nested folder you will first need to create it using either Pathlib or os: >>> from pathlib import Path # doctest: +SKIP >>> filepath = Path('folder/subfolder/out.csv') # doctest: +SKIP >>> filepath.parent.mkdir(parents=True, exist_ok=True) # doctest: +SKIP >>> df.to_csv(filepath) # doctest: +SKIP >>> import os # doctest: +SKIP >>> os.makedirs('folder/subfolder', exist_ok=True) # doctest: +SKIP >>> df.to_csv('folder/subfolder/out.csv') # doctest: +SKIP )framerrrrr)r3rrrrr1r(rrrr2rr4r5r)rrkrrrr6)rrrrrr(rrrrrrr1r2r3rrr4r5rrrrrs rr6r7s~ l33T&%  !+22 )####!+!3  rc[U5e)z Reset the cacher. rrs r _reset_cacherNDFrame._reset_cachers"$''rc[5(agU(aURSS9 U(aUR5 gg)z See if we need to update our parent cacher if clear, then clear our cache. Parameters ---------- clear : bool, default False Clear the item cache. verify_is_copy : bool, default True Provide is_copy checks. Nreferentr)r_check_setitem_copyrw)rclearverify_is_copyrs r_maybe_update_cacherNDFrame._maybe_update_cachers8"     $ $z $ 2   " " $ rc[U5errrs rrwNDFrame._clear_item_cache !$''rc :[R"SU5 [U[5(dr[R "U[R S9nUS:XaHURS:Xa8[5(a)[U[U55(aURSS9$OURS:Xa![[U5RS35e[R "S[U5RS 3["[%5S 9 [R&"UR(UR*UR,[R S9nUR.R1UUR3U5S S 9nUR5XDR6S 9R9USS9$)a Return the elements in the given *positional* indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object. Parameters ---------- indices : array-like An array of ints indicating which positions to take. axis : {0 or 'index', 1 or 'columns', None}, default 0 The axis on which to select elements. ``0`` means that we are selecting rows, ``1`` means that we are selecting columns. For `Series` this parameter is unused and defaults to 0. **kwargs For compatibility with :meth:`numpy.take`. Has no effect on the output. Returns ------- same type as caller An array-like containing the elements taken from the object. See Also -------- DataFrame.loc : Select a subset of a DataFrame by labels. DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis. Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0), ... ('parrot', 'bird', 24.0), ... ('lion', 'mammal', 80.5), ... ('monkey', 'mammal', np.nan)], ... columns=['name', 'class', 'max_speed'], ... index=[0, 2, 3, 1]) >>> df name class max_speed 0 falcon bird 389.0 2 parrot bird 24.0 3 lion mammal 80.5 1 monkey mammal NaN Take elements at positions 0 and 3 along the axis 0 (default). Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3. >>> df.take([0, 3]) name class max_speed 0 falcon bird 389.0 1 monkey mammal NaN Take elements at indices 1 and 2 along the axis 1 (column selection). >>> df.take([1, 2], axis=1) class max_speed 0 bird 389.0 2 bird 24.0 3 mammal 80.5 1 mammal NaN We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists. >>> df.take([-1, -2]) name class max_speed 1 monkey mammal NaN 3 lion mammal 80.5 rrrrNrz1.take requires a sequence of integers, not slice.zPassing a slice to zq.take is deprecated and will raise in a future version. Use `obj[slicer]` or pass a sequence of integers instead.rTrverifyrtaker~)nv validate_takerrrhrVintpr,rrrrrr rrr rrUarangestartstopsteprrLrrrr)rindicesrrrs rrL NDFrame.takesdX V$'5))jj8G LLA%'))$Wc$i88yydy++ YY!^:&&'(  MM%d4j&9&9%:;22+-  ii w||W\\G99>> --d3"  ))()GTT U  rcURXS9nURS:XaEURU5RURU55(dUR U5 U$)a7 Internal version of the `take` method that sets the `_is_copy` attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning). For Series this does the same as the public take (it never sets `_is_copy`). See the docstring of `take` for full explanation of the parameters. )rTrr+)rLr,rr _set_is_copy)rrTrrs r_take_with_is_copyNDFrame._take_with_is_copy.sV76 99>&"2"24"8"?"?t@T"U"U    % rcURU5nURU5n[U[5(a [ S5eUb[U[ 5(d [ S5eUR XUS9upg[S5/UR-nXhU'[U5n URU n [XRU5U5 U $US:XaU(aX$URn O URn [U [ 5(aAU RUSS9uplU(d'[ R""U5(aXUS-n OXn OU R%U5n[U[&R(5(aNUR*[&R,:Xa!UR/5un UR1XS9$UR1XbS9$[3U5(dXn [3U5(aUS:XayURS:XaUR4U$UR6R9U5nUR;XR<S 9n URUU lU RAU5n O`[3U5(a URSS2[XfS-54n O0US:XaURSS2U4n OURUn W U lU RCX RD(+S 9 U $) a Return cross-section from the Series/DataFrame. This method takes a `key` argument to select data at a particular level of a MultiIndex. Parameters ---------- key : label or tuple of label Label contained in the index, or partially in a MultiIndex. axis : {0 or 'index', 1 or 'columns'}, default 0 Axis to retrieve cross-section on. level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position. drop_level : bool, default True If False, returns object with same levels as self. Returns ------- Series or DataFrame Cross-section from the original Series or DataFrame corresponding to the selected index levels. See Also -------- DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position. Notes ----- `xs` can not be used to set values. MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of `xs` functionality, see :ref:`MultiIndex Slicers `. Examples -------- >>> d = {'num_legs': [4, 4, 2, 2], ... 'num_wings': [0, 0, 2, 2], ... 'class': ['mammal', 'mammal', 'mammal', 'bird'], ... 'animal': ['cat', 'dog', 'bat', 'penguin'], ... 'locomotion': ['walks', 'walks', 'flies', 'walks']} >>> df = pd.DataFrame(data=d) >>> df = df.set_index(['class', 'animal', 'locomotion']) >>> df num_legs num_wings class animal locomotion mammal cat walks 4 0 dog walks 4 0 bat flies 2 2 bird penguin walks 2 2 Get values at specified index >>> df.xs('mammal') num_legs num_wings animal locomotion cat walks 4 0 dog walks 4 0 bat flies 2 2 Get values at several indexes >>> df.xs(('mammal', 'dog', 'walks')) num_legs 4 num_wings 0 Name: (mammal, dog, walks), dtype: int64 Get values at specified index and level >>> df.xs('cat', level=1) num_legs num_wings class locomotion mammal walks 4 0 Get values at several indexes and levels >>> df.xs(('bird', 'walks'), ... level=[0, 'locomotion']) num_legs num_wings animal penguin 2 2 Get values at specified column and axis >>> df.xs('num_wings', axis=1) class animal locomotion mammal cat walks 0 dog walks 0 bat flies 2 bird penguin walks 2 Name: num_wings, dtype: int64 z7list keys are not supported in xs, pass a tuple insteadNzIndex must be a MultiIndex)r: drop_levelrrrrrr)#r!rrrirr get_loc_levelrr,r`rrtr%r(r_get_loc_levelr is_integerget_locrhndarrayrrnonzerorXrgrrfast_xs_constructor_sliced_from_mgrrrrrW_is_view)rr9rr:r[rolocnew_ax_indexerrrrrindsrs rr- NDFrame.xs?sT$$T*% c4 UV V  fj11 <== ..sJ.WKCd }tyy0H TNHoGYYw'F F11$7 @M 19y LLEJJE eZ ( ("11#Q1?NC>>#&& %C!G 4I % I--$C#rzz**99(!kkmGT2242CC2232BBS>>!J S>>daiyyA~||C((ii'',G66w\\6RF::c?FL((.F s^^YYq%1W"556F QYYYq#v&FYYs^F$FL D??':; rc[U5err)rrs r __getitem__NDFrame.__getitem__rHrcpURRUSS9n[U[R5(am[ R "UR[RSS9[U55n[U[R5(aURUSS9$UnURU5$)z; __getitem__ for the case where the key is a slice object. getitem)rFr\rr) r_convert_slice_indexerrrhrarmaybe_indices_to_slicerrOrrL_slice)rr9slobjrs r_getitem_sliceNDFrame._getitem_slices 11#I1F eRZZ ( (00 RWW5 13t9G'2::..yyqy11E{{5!!rcF[U[5(d[U55eURU5nURR XS9nUR X3RS9nURU5nUS:g=(d URnURXS9 U$)zX Construct a slice of this container. Slicing with this method is *always* positional. rrrr\) rrr rr get_slicerrrrerW)rrsrrris_copys rrrNDFrame._slice s %''4e4'++D1))%%e%7++G,,+G$$T*!).vD/ rcbU(dSUlgUce[R"U5Ulgr)rweakrefref)rr|rs rrWNDFrame._set_is_copys& DM? "?#KK,DMrcDUR(aURSS9 g)a Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check. Should be called just near setting a value Will return a boolean if it we are a view and are cached, but a single-dtype meaning that the cacher should be updated following setting. r?r@F)rrArs r%_check_is_chained_assignment_possible-NDFrame._check_is_chained_assignment_possible#s ==  $ $z $ 2rc[5(d[5(agU(dUR(dg[R"S5nUcgURbo[ UR[ 5(dPUR5n[R"U5(aUb"URUR:XaSUlg[ UR[ 5(a URnO US:XaSnOSnUS:Xa [U5eUS:Xa#[R"U[[5S9 gg) a Parameters ---------- t : str, the type of setting error force : bool, default False If True, then force showing an error. validate if we are doing a setitem on a chained copy. It is technically possible to figure out that we are setting on a copy even WITH a multi-dtyped pandas object. In other words, some blocks may be views while other are not. Currently _is_view will ALWAYS return False for multi-blocks to avoid having to handle this case. df = DataFrame(np.arange(0,9), columns=['count']) df['group'] = 'b' # This technically need not raise SettingWithCopy if both are view # (which is not generally guaranteed but is usually True. However, # this is in general not a good practice and we recommend using .loc. df.iloc[0:5]['group'] = 'a' Nzmode.chained_assignmentr?z A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copya A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copyrr r)rrrrrzrrgc get_referentsrarNrr rOrU)rrforcerrs rrANDFrame._check_setitem_copy2s4  $6$8$8  !!";< =  == $Z s-K-K A##A&&1=QWW =R $  dmmS ) ) A *_? ?  G &q) ) F? MM!3@P@R S rcDSnSnURS:Xa8[UR[5(aXRR;nU(aZ[U[ 5(dU4nURH2n[U[ 5(dMUS[U5U:XdM.X SnM4 U(d>URSRU5nURRU5Ul URU g![ a Nf=f![a gf=f)z Delete item Fr+NTr)r,rr(r_enginerr`rrr`rideleterr)rr9deletedmaybe_shortcutrrfs r __delitem__NDFrame.__delitem__}s  99>jzBB "%LL,@,@!@ c5))f||c5))c*CHo.D "G$))B-'',C ))#.DI   %+  ,   s#D4 D DD DDc`U(a'URR(d [S5egg)NzQCannot specify 'inplace=True' when 'self.flags.allows_duplicate_labels' is False.)rrr )rrs rr2NDFrame._check_inplace_and_allows_duplicate_labelss+ 4::==A >7rcFX$![[[4a Us$f=f)a Get item from object for given key (ex: DataFrame column). Returns default value if not found. Parameters ---------- key : object Returns ------- same type as items contained in object Examples -------- >>> df = pd.DataFrame( ... [ ... [24.3, 75.7, "high"], ... [31, 87.8, "high"], ... [22, 71.6, "medium"], ... [35, 95, "medium"], ... ], ... columns=["temp_celsius", "temp_fahrenheit", "windspeed"], ... index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"), ... ) >>> df temp_celsius temp_fahrenheit windspeed 2014-02-12 24.3 75.7 high 2014-02-13 31.0 87.8 high 2014-02-14 22.0 71.6 medium 2014-02-15 35.0 95.0 medium >>> df.get(["temp_celsius", "windspeed"]) temp_celsius windspeed 2014-02-12 24.3 high 2014-02-13 31.0 high 2014-02-14 22.0 medium 2014-02-15 35.0 medium >>> ser = df['windspeed'] >>> ser.get('2014-02-13') 'high' If the key isn't found, the default value will be used. >>> df.get(["temp_celsius", "temp_kelvin"], default="default_value") 'default_value' >>> ser.get('2014-02-10', '[unknown]') '[unknown]' )rr  IndexError)rr9defaults rr NDFrame.gets+l 9 *j1 N s   c.URR$)z:Return boolean indicating if self is view of another array)ris_viewrs rreNDFrame._is_viewsyy   rc`URURUUUUS9nUR"S0UD6$)a Return an object with matching indices as other object. Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False. Parameters ---------- other : Object of the same data type Its row and column indices are used to define the new indices of this object. method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default): don't fill gaps * pad / ffill: propagate last valid observation forward to next valid * backfill / bfill: use next valid observation to fill gap * nearest: use nearest valid observations to fill gap. copy : bool, default True Return a new object, even if the passed indexes are the same. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. Returns ------- Series or DataFrame Same type as caller, but with changed indices on each axis. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices. Notes ----- Same as calling ``.reindex(index=other.index, columns=other.columns,...)``. Examples -------- >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'], ... [31, 87.8, 'high'], ... [22, 71.6, 'medium'], ... [35, 95, 'medium']], ... columns=['temp_celsius', 'temp_fahrenheit', ... 'windspeed'], ... index=pd.date_range(start='2014-02-12', ... end='2014-02-15', freq='D')) >>> df1 temp_celsius temp_fahrenheit windspeed 2014-02-12 24.3 75.7 high 2014-02-13 31.0 87.8 high 2014-02-14 22.0 71.6 medium 2014-02-15 35.0 95.0 medium >>> df2 = pd.DataFrame([[28, 'low'], ... [30, 'low'], ... [35.1, 'medium']], ... columns=['temp_celsius', 'windspeed'], ... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13', ... '2014-02-15'])) >>> df2 temp_celsius windspeed 2014-02-12 28.0 low 2014-02-13 30.0 low 2014-02-15 35.1 medium >>> df2.reindex_like(df1) temp_celsius temp_fahrenheit windspeed 2014-02-12 28.0 NaN low 2014-02-13 30.0 NaN low 2014-02-14 NaN NaN NaN 2014-02-15 35.1 NaN medium )rrrlimit tolerancer)rrreindex)rrrrrrrs r reindex_likeNDFrame.reindex_likesCd  & &"" ' || a  r)rrr(r:rcgrrrrorrr(r:rrs rr3 NDFrame.dropk r)rrr(r:rrcgrrrs rr3ryrrcgrrrs rr3rrrrcV[US5nUb&UcUb [S5eURU5nX0n O*UcUbSU0n URS:XaXIS'O [S5eUn U R 5Hup!UcM U R XXWS9n M U(aUR U 5 gU $)Nrz2Cannot specify both 'labels' and 'index'/'columns'rr+r(z>Need to specify at least one of 'labels', 'index' or 'columns'r:r)rXr r%r,r _drop_axisr) rrorrr(r:rrrDrrs rr3rs&gy9   G$7 !UVV++D1I&D  '"5U#DyyA~")YP  JJLLD!nnVnN)    %Jrc NURU5nURU5nUR(aVUb1[U[5(d [ S5eUR XUS9nOUR XS9nURU5nGO[U5=(d [U[5n [[R"U55nUbk[U[5(d [ S5eURU5RU5)n US:Xa#U R5(a[!US35eO[U[5(a9UR"S:Xa)U (d"URS5RU5)n OOURU5)n UR%U5S :HR'5n US:XaU (a[!US35e[U R"[(5(aU R+[,S 9n U R/5SnUR1U5nUR2U- S - n UR4R7UUU S SUS 9n UR9XR:S9nUR2S :XaUR<UlURAU5$)a Drop labels from specified axis. Used in the ``drop`` method internally. Parameters ---------- labels : single label or list-like axis : int or axis name level : int or level name, default None For MultiIndex errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. only_slice : bool, default False Whether indexing along columns should be view-only. Nzaxis must be a MultiIndexr)rrrrrrrrT)r allow_dupsr only_slicer)!r!r is_uniquerrAssertionErrorr3 get_indexerrnr`r\rsindex_labels_to_arrayr3isinrrrrrrjto_numpyrrbrLr,rreindex_indexerrrr8rr)rrorr:rraxis_numnew_axisris_tuple_labelsmasklabels_missingrrrs rrNDFrame._drop_axissR2((.~~d# >> !$ 33()DEE99V9H99V9;&&x0G2&9VZPU=VO"6#?#?#GHF !$ 33()DEE--e499&AAW$"fX-?#@AA4,,LLH,' --a055f== &))"&"6"6v">""D!I!I!KW$"fX-?#@AA$**n55}}4}0llnQ'Gyy)H))h&*))++  ! , ++G,,+G 99>99FL""4((rcUR5 UR5 URUlURUSS9 g)z Replace self internals with result. Parameters ---------- result : same type as self verify_is_copy : bool, default True Provide is_copy checks. T)rCrN) _reset_cacherwrrD)rrrCs rrNDFrame._update_inplaces:   KK  !!!Nrcz^U4SjnURnUbURU5nXC0nUR"S0UD6$)a Prefix labels with string `prefix`. For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed. Parameters ---------- prefix : str The string to add before each label. axis : {0 or 'index', 1 or 'columns', None}, default None Axis to add prefix on .. versionadded:: 2.0.0 Returns ------- Series or DataFrame New Series or DataFrame with updated labels. See Also -------- Series.add_suffix: Suffix row labels with string `suffix`. DataFrame.add_suffix: Suffix column labels with string `suffix`. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.add_prefix('item_') item_0 1 item_1 2 item_2 3 item_3 4 dtype: int64 >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}) >>> df A B 0 1 3 1 2 4 2 3 5 3 4 6 >>> df.add_prefix('col_') col_A col_B 0 1 3 1 2 4 2 3 5 3 4 6 c>TU3$rr)rr6s rr$NDFrame.add_prefix..`s nrrrr%r)rr6rrrDrs ` r add_prefixNDFrame.add_prefix%sGv %((  ++D1I||%f%%rcz^U4SjnURnUbURU5nXC0nUR"S0UD6$)a Suffix labels with string `suffix`. For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed. Parameters ---------- suffix : str The string to add after each label. axis : {0 or 'index', 1 or 'columns', None}, default None Axis to add suffix on .. versionadded:: 2.0.0 Returns ------- Series or DataFrame New Series or DataFrame with updated labels. See Also -------- Series.add_prefix: Prefix row labels with string `prefix`. DataFrame.add_prefix: Prefix column labels with string `prefix`. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.add_suffix('_item') 0_item 1 1_item 2 2_item 3 3_item 4 dtype: int64 >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}) >>> df A B 0 1 3 1 2 4 2 3 5 3 4 6 >>> df.add_suffix('_col') A_col B_col 0 1 3 1 2 4 2 3 5 3 4 6 c>UT3$rr)rsuffixs rr$NDFrame.add_suffix..s F8nrrr)rrrrrDrs ` r add_suffixNDFrame.add_suffixosGv %((  ++D1I ||%f%%r)r ascendingrr na_position ignore_indexr9cgrrrrrrrrrr9s r sort_valuesNDFrame.sort_valuesrr)rrrrrr9cgrrrs rrrrrcgrrrs rrrrr quicksortlastc[U5e)u Sort by the values along either axis. Parameters ----------%(optional_by)s axis : %(axes_single_arg)s, default 0 Axis to be sorted. ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. `mergesort` and `stable` are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label. na_position : {'first', 'last'}, default 'last' Puts NaNs at the beginning if `first`; `last` puts NaNs at the end. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. key : callable, optional Apply the key function to the values before sorting. This is similar to the `key` argument in the builtin :meth:`sorted` function, with the notable difference that this `key` function should be *vectorized*. It should expect a ``Series`` and return a Series with the same shape as the input. It will be applied to each column in `by` independently. Returns ------- DataFrame or None DataFrame with sorted values or None if ``inplace=True``. See Also -------- DataFrame.sort_index : Sort a DataFrame by the index. Series.sort_values : Similar method for a Series. Examples -------- >>> df = pd.DataFrame({ ... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'], ... 'col2': [2, 1, 9, 8, 7, 4], ... 'col3': [0, 1, 9, 4, 2, 3], ... 'col4': ['a', 'B', 'c', 'D', 'e', 'F'] ... }) >>> df col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F Sort by col1 >>> df.sort_values(by=['col1']) col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 5 C 4 3 F 4 D 7 2 e 3 NaN 8 4 D Sort by multiple columns >>> df.sort_values(by=['col1', 'col2']) col1 col2 col3 col4 1 A 1 1 B 0 A 2 0 a 2 B 9 9 c 5 C 4 3 F 4 D 7 2 e 3 NaN 8 4 D Sort Descending >>> df.sort_values(by='col1', ascending=False) col1 col2 col3 col4 4 D 7 2 e 5 C 4 3 F 2 B 9 9 c 0 A 2 0 a 1 A 1 1 B 3 NaN 8 4 D Putting NAs first >>> df.sort_values(by='col1', ascending=False, na_position='first') col1 col2 col3 col4 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F 2 B 9 9 c 0 A 2 0 a 1 A 1 1 B Sorting with a key function >>> df.sort_values(by='col4', key=lambda col: col.str.lower()) col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F Natural sort with the key argument, using the `natsort ` package. >>> df = pd.DataFrame({ ... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'], ... "value": [10, 20, 30, 40, 50] ... }) >>> df time value 0 0hr 10 1 128hr 20 2 72hr 30 3 48hr 40 4 96hr 50 >>> from natsort import index_natsorted >>> df.sort_values( ... by="time", ... key=lambda x: np.argsort(index_natsorted(df["time"])) ... ) time value 0 0hr 10 3 48hr 40 2 72hr 30 4 96hr 50 1 128hr 20 rrs rrrsl"$''r)rr:rrrsort_remainingrr9c grr rrr:rrrrrrr9s r sort_indexNDFrame.sort_indexz r) rr:rrrrrrr9c grrrs rrrrrc grrrs rrrrrc [US5nURU5n[U5nURU5n [ XX5XgU 5n U cCU(aUn OUR SS9n U(a[ [U55U lU(agU $URU5n URRXSS9nU(dURU R5nO[ [U 55nURX5 URXRS9n U(aUR!U 5$U R#USS9$)NrrFrJrrr~)rXr!rWrrrrrrrrrLr_sort_levels_monotonicrprrr)rrr:rrrrrrr9targetrrbaxisrrs rrrs'&gy9$$T*&y1 %% 9K  ?-,SY7  ,,T299>>'e>D}}U+BBDH$S\2H%*++H==+I ''/ /&&tL&A Ar)roptional_reindex) rr(rrrr: fill_valuerrc ^UbUbUb [S5eUcUbUb [S5eUbUbUnO$UnO!U(aTRU5S:XaUnOUnUUS.n [U5nU(a[5(aSn[ U4SjU R 555(aTR US9$TRXU5(aTRXU5$TRXXXXU5RTSS 9$) a Conform {klass} to new index with optional filling logic. Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and ``copy=False``. Parameters ---------- {optional_reindex} method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default): don't fill gaps * pad / ffill: Propagate last valid observation forward to next valid. * backfill / bfill: Use next valid observation to fill gap. * nearest: Use nearest valid observations to fill gap. copy : bool, default True Return a new object, even if the passed indexes are the same. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level. fill_value : scalar, default np.nan Value to use for missing values. Defaults to NaN, but can be any "compatible" value. limit : int, default None Maximum number of consecutive elements to forward or backward fill. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. Returns ------- {klass} with changed index. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex_like : Change to same indices as other DataFrame. Examples -------- ``DataFrame.reindex`` supports two calling conventions * ``(index=index_labels, columns=column_labels, ...)`` * ``(labels, axis={{'index', 'columns'}}, ...)`` We *highly* recommend using keyword arguments to clarify your intent. Create a dataframe with some fictional data. >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror'] >>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301], ... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}}, ... index=index) >>> df http_status response_time Firefox 200 0.04 Chrome 200 0.02 Safari 404 0.07 IE10 404 0.08 Konqueror 301 1.00 Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned ``NaN``. >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10', ... 'Chrome'] >>> df.reindex(new_index) http_status response_time Safari 404.0 0.07 Iceweasel NaN NaN Comodo Dragon NaN NaN IE10 404.0 0.08 Chrome 200.0 0.02 We can fill in the missing values by passing a value to the keyword ``fill_value``. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword ``method`` to fill the ``NaN`` values. >>> df.reindex(new_index, fill_value=0) http_status response_time Safari 404 0.07 Iceweasel 0 0.00 Comodo Dragon 0 0.00 IE10 404 0.08 Chrome 200 0.02 >>> df.reindex(new_index, fill_value='missing') http_status response_time Safari 404 0.07 Iceweasel missing missing Comodo Dragon missing missing IE10 404 0.08 Chrome 200 0.02 We can also reindex the columns. >>> df.reindex(columns=['http_status', 'user_agent']) http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN Or we can use "axis-style" keyword arguments >>> df.reindex(['http_status', 'user_agent'], axis="columns") http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN To further illustrate the filling functionality in ``reindex``, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates). >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D') >>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}}, ... index=date_index) >>> df2 prices 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 Suppose we decide to expand the dataframe to cover a wider date range. >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D') >>> df2.reindex(date_index2) prices 2009-12-29 NaN 2009-12-30 NaN 2009-12-31 NaN 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN The index entries that did not have a value in the original data frame (for example, '2009-12-29') are by default filled with ``NaN``. If desired, we can fill in the missing values using one of several options. For example, to back-propagate the last valid value to fill the ``NaN`` values, pass ``bfill`` as an argument to the ``method`` keyword. >>> df2.reindex(date_index2, method='bfill') prices 2009-12-29 100.0 2009-12-30 100.0 2009-12-31 100.0 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN Please note that the ``NaN`` value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the ``NaN`` values present in the original dataframe, use the ``fillna()`` method. See the :ref:`user guide ` for more. z3Cannot specify all of 'labels', 'index', 'columns'.rrrFc3r># UH,upUcM TRU5RU5v M. g7fr)r identical)r]rDrrs rr^"NDFrame.reindex..s6 !-  4DNN9 % / / 3 3!-s 7'7rrr~) rr!rrrrr_needs_reindex_multi_reindex_multi _reindex_axesr) rrorr(rrrr:rrrrs ` rrNDFrame.reindexs.H  !49KQR R  '"5P!$$G"E--d3q8 8 +62 '))D  !%   99$9' '  $ $T5 9 9&&t:> >!! 6t ,tI, . /rc UnURHXn Xn U cM URU 5n U RXX4US9upURU 5nUR XU /0UUSS9nSnMZ U$)z%Perform the reindex for all the axes.)r:rrrF)rrr)rrrr!_reindex_with_indexers)rrr:rrrrrrrrorrrrs rrNDFrame._reindex_axess""AWF~"B!#5f"," I((+D,,7+,% -CD%#( rc[R"UR56UR:H=(a" USL=(a USL=(a UR$)z$Check if we do need a multi reindex.N)rscount_not_nonerr_can_fast_transpose)rrrr:s rrNDFrame._needs_reindex_multisN " "DKKM 2dnn D )$ )  ) ((  rc[U5err)rrrrs rrNDFrame._reindex_multirHrc URn[UR55HNnXupxURU5n UcM[ U5nUb [ U5nUR UUU UUUS9nSnMP U(dUc-XPRLa[5(dURUS9nO,[5(aXPRLaURSS9nURXURS9RU5$)z*allow_dups indicates an internal call here)rrrrFrr) rsortedr7rrr]rrrrrr) r reindexersrrrrrrrrs rrNDFrame._reindex_with_indexerss99:??,-D'-NE006E} 'E"-g6 //%% 0HD+.0T\II%'))}}$}/H " "x99'<}}%}0H))()GTT   rcx^^ [R"UTU5nUS:a [S5eUc URnUR U5nUbhUR U5n[ U5RU5n[U5S:XaURUR5nUR"S0Xq0D6$T(a+S U4SjjnURU5n URUS9U $U(aAS U 4Sjjn[R"U5m URU5n URUS9U $[S5e) a Subset the dataframe rows or columns according to the specified index labels. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters ---------- items : list-like Keep labels from axis which are in items. like : str Keep labels from axis for which "like in label == True". regex : str (regular expression) Keep labels from axis for which re.search(regex, label) == True. axis : {0 or 'index', 1 or 'columns', None}, default None The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'columns' for DataFrame. For `Series` this parameter is unused and defaults to `None`. Returns ------- same type as input object See Also -------- DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. Notes ----- The ``items``, ``like``, and ``regex`` parameters are enforced to be mutually exclusive. ``axis`` defaults to the info axis that is used when indexing with ``[]``. Examples -------- >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])), ... index=['mouse', 'rabbit'], ... columns=['one', 'two', 'three']) >>> df one two three mouse 1 2 3 rabbit 4 5 6 >>> # select columns by name >>> df.filter(items=['one', 'three']) one three mouse 1 3 rabbit 4 6 >>> # select columns by regular expression >>> df.filter(regex='e$', axis=1) one three mouse 1 3 rabbit 4 6 >>> # select rows containing 'bbi' >>> df.filter(like='bbi', axis=0) one two three rabbit 4 5 6 rzDKeyword arguments `items`, `like`, or `regex` are mutually exclusiverc*>TceT[U5;$r)r^)rlikes rrNDFrame.filter..fs'''z!},,rrc<>TR[U55SL$r)searchr^)rmatchers rrrs~~jm4D@@rz,Must pass either `items`, `like`, or `regex`rreturnbool_t)rsrrrrr%r~ intersectionrrrrrtrfrecompile) rrrregexrnkwror8rrrs ` @rfilterNDFrame.filterNs#L##E47 7)  <''D%  &&t,D%L--f5E5zQ V\\2<<04-0 0  -ZZ]F888&v. .  Ajj'GZZ]F888&v. .JK Krcx[5(aURSUR5$URSU$)a Return the first `n` rows. This function returns the first `n` rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it. For negative values of `n`, this function returns all rows except the last `|n|` rows, equivalent to ``df[:n]``. If n is larger than the number of rows, this function returns all rows. Parameters ---------- n : int, default 5 Number of rows to select. Returns ------- same type as caller The first `n` rows of the caller object. See Also -------- DataFrame.tail: Returns the last `n` rows. Examples -------- >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the first 5 lines >>> df.head() animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey Viewing the first `n` lines (three in this case) >>> df.head(3) animal 0 alligator 1 bee 2 falcon For negative values of `n` >>> df.head(-3) animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot Nrrrrns rr NDFrame.heads7R  99Ra=%%' 'yy!}rc[5(aAUS:XaURSSR5$URU*SR5$US:XaURSS$URU*S$)a Return the last `n` rows. This function returns last `n` rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows. For negative values of `n`, this function returns all rows except the first `|n|` rows, equivalent to ``df[|n|:]``. If n is larger than the number of rows, this function returns all rows. Parameters ---------- n : int, default 5 Number of rows to select. Returns ------- type of caller The last `n` rows of the caller object. See Also -------- DataFrame.head : The first `n` rows of the caller object. Examples -------- >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the last 5 lines >>> df.tail() animal 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the last `n` lines (three in this case) >>> df.tail(3) animal 6 shark 7 whale 8 zebra For negative values of `n` >>> df.tail(-3) animal 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra rNrrs rtail NDFrame.tailsrR  Avyy1~**,,99aRS>&&( ( 699Qq> !yy!~rreplacecUcSnURU5nURUn[R"U5n [R "XU5n U cUce[ X(-5n Ub[R"XU5n[R"XX4U 5n URXS9n U(a[[U 55U l U $)u[ Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional Number of items from axis to return. Cannot be used with `frac`. Default = 1 if `frac` = None. frac : float, optional Fraction of axis items to return. Cannot be used with `n`. replace : bool, default False Allow or disallow sampling of the same row more than once. weights : str or ndarray-like, optional Default 'None' results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. .. versionchanged:: 1.4.0 np.random.Generator objects now accepted axis : {0 or 'index', 1 or 'columns', None}, default None Axis to sample. Accepts axis number or name. Default is stat axis for given data type. For `Series` this parameter is unused and defaults to `None`. ignore_index : bool, default False If True, the resulting index will be labeled 0, 1, …, n - 1. .. versionadded:: 1.3.0 Returns ------- Series or DataFrame A new object of same type as caller containing `n` items randomly sampled from the caller object. See Also -------- DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object. SeriesGroupBy.sample: Generates random samples from each group of a Series object. numpy.random.choice: Generates a random sample from a given 1-D numpy array. Notes ----- If `frac` > 1, `replacement` should be set to `True`. Examples -------- >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0], ... 'num_wings': [2, 0, 0, 0], ... 'num_specimen_seen': [10, 2, 1, 8]}, ... index=['falcon', 'dog', 'spider', 'fish']) >>> df num_legs num_wings num_specimen_seen falcon 2 2 10 dog 4 0 2 spider 8 0 1 fish 0 0 8 Extract 3 random elements from the ``Series`` ``df['num_legs']``: Note that we use `random_state` to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 Name: num_legs, dtype: int64 A random 50% sample of the ``DataFrame`` with replacement: >>> df.sample(frac=0.5, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 An upsample sample of the ``DataFrame`` with replacement: Note that `replace` parameter has to be `True` for `frac` parameter > 1. >>> df.sample(frac=2, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 falcon 2 2 10 falcon 2 2 10 fish 0 0 8 dog 4 0 2 fish 0 0 8 dog 4 0 2 Using a DataFrame column as weights. Rows with larger value in the `num_specimen_seen` column are more likely to be sampled. >>> df.sample(n=2, weights='num_specimen_seen', random_state=1) num_legs num_wings num_specimen_seen falcon 2 2 10 fish 0 0 8 rr) r!rars random_staterwprocess_sampling_sizerpreprocess_weightsrLrrr) rrfracr weightsrrrobj_lenrsrjsampled_indicesrs rrwNDFrame.sampleXsz <D$$T***T"  .++AW= <# ##(D  //tDG --wL?6 (V5FL rc[5(a([R"URSS9U/UQ70UD6$[R"X/UQ70UD6$)a3 Apply chainable functions that expect Series or DataFrames. Parameters ---------- func : function Function to apply to the {klass}. ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the {klass}. *args : iterable, optional Positional arguments passed into ``func``. **kwargs : mapping, optional A dictionary of keyword arguments passed into ``func``. Returns ------- the return type of ``func``. See Also -------- DataFrame.apply : Apply a function along input axis of DataFrame. DataFrame.map : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:`~pandas.Series`. Notes ----- Use ``.pipe`` when chaining together functions that expect Series, DataFrames or GroupBy objects. Examples -------- Constructing a income DataFrame from a dictionary. >>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]] >>> df = pd.DataFrame(data, columns=['Salary', 'Others']) >>> df Salary Others 0 8000 1000.0 1 9500 NaN 2 5000 2000.0 Functions that perform tax reductions on an income DataFrame. >>> def subtract_federal_tax(df): ... return df * 0.9 >>> def subtract_state_tax(df, rate): ... return df * (1 - rate) >>> def subtract_national_insurance(df, rate, rate_increase): ... new_rate = rate + rate_increase ... return df * (1 - new_rate) Instead of writing >>> subtract_national_insurance( ... subtract_state_tax(subtract_federal_tax(df), rate=0.12), ... rate=0.05, ... rate_increase=0.02) # doctest: +SKIP You can write >>> ( ... df.pipe(subtract_federal_tax) ... .pipe(subtract_state_tax, rate=0.12) ... .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02) ... ) Salary Others 0 5892.48 736.56 1 6997.32 NaN 2 3682.80 1473.12 If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``national_insurance`` takes its data as ``df`` in the second argument: >>> def subtract_national_insurance(rate, df, rate_increase): ... new_rate = rate + rate_increase ... return df * (1 - new_rate) >>> ( ... df.pipe(subtract_federal_tax) ... .pipe(subtract_state_tax, rate=0.12) ... .pipe( ... (subtract_national_insurance, 'df'), ... rate=0.05, ... rate_increase=0.02 ... ) ... ) Salary Others 0 5892.48 736.56 1 6997.32 NaN 2 3682.80 1473.12 Nr)rrspiper)rfuncargsrs rr NDFrame.pipesMN  ;;tyydy3TKDKFK K{{47777rc ^[U[5(aUR(a[UR5UlURR URl[ UR5[ UR5-H:n[U[5(de[RX[XS55 M< US:Xa[SUR55(aRURSRm[U4SjURSS55nU(a[T5Ul[SUR55nX`RlU$)a| Propagate metadata from other to self. Parameters ---------- other : the object from which to get the attributes that we are going to propagate method : str, optional A passed method name providing context on where ``__finalize__`` was called. .. warning:: The value passed as `method` are not currently considered stable across pandas releases. Nrc3L# UHn[UR5v M g7fr)rr)r]rs rr^'NDFrame.__finalize__..~s9js4 ??j"$rc3@># UHoRT:Hv M g7fr)r)r]rrs rr^rs%SNSii5&8Nrc3L# UHoRRv M g7fr)rrr]rs rr^rs*9CA//r )rrrrrrrhrrrrr0robjs)rrrrr8have_same_attrsrrs @rrNDFrame.__finalize__\s$ eW % %{{ &ekk2 161T1TDJJ .DNN+c%//.BB!$,,,,""4wuD/IJC X 9ejj999 1 ++"%%SEJJqrN%S"S"!)%DJ&)*9>*' #2IJJ . rcXR;aBXR;a3XR;a$URR U5(aX$[ R X5$)zt After regular attribute access, try looking up the name This allows simpler access to columns for interactive use. )rrrrR$_can_hold_identifiers_and_holds_namer__getattribute__)rr8s r __getattr__NDFrame.__getattr__sR 00 0NN*OO+DDTJJ: &&t22rc[RX5 [RXU5$![a Of=fXR;a[RXU5 gXR ;a[RXU5 g[ X5n[U[5(a[RXU5 gXR;aX U'g[RXU5 g![[4a[ [U[5(a-[U5(a[R"S[5S9 [RXU5 gf=f)zq After regular attribute access, try setting the name This allows simpler access to columns for interactive use. zPandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-accessrN)rr*rAttributeErrorrrr0rr~rRrrkrcrr rU)rr8rexistings rrNDFrame.__setattr__s   # #D /%%d%8 8    ++ +   t5 1 ^^ #   t5 1 6"4.h..&&t59__,!&J&&t59"I. 6dL11|E7J7JMM@$4#5 ""4u5 6s+*- :: 6C,C,C,,A(EEc>[TU]5nURR(a%UR URR 5 U$)zs add the string-like attributes from the info_axis. If info_axis is a MultiIndex, its first level values are used. )super_dir_additionsrR_can_hold_stringsr_dir_additions_for_owner)r additions __class__s rr3NDFrame._dir_additionss= G*, ?? , ,   T__EE Frc[UR[[45(aU"5$[ URR 5nU"5n[ URR 5U:waUR 5 U$)zF Consolidate _mgr -- if the blocks have changed, then clear the cache )rrrrrrrw)rr blocks_beforers r_protect_consolidateNDFrame._protect_consolidatese dii,0B!C D D3JDII,,-  tyy M 1  " " $ rc8^SU4SjjnTRU5 g)z)Consolidate data in place and return NonecD>TRR5Tlgrr consolidatersrr'NDFrame._consolidate_inplace..fs --/DIrNrNone)r;)rrs` r_consolidate_inplaceNDFrame._consolidate_inplaces 0 !!!$rc^U4SjnTRU5nTRX"RS9RT5$)z Compute NDFrame with "consolidated" internals (data of each dtype grouped together in a single ndarray). Returns ------- consolidated : same type as caller c8>TRR5$rr?rsrr&NDFrame._consolidate..sDII))+rr)r;rrr)rr cons_datas` r _consolidateNDFrame._consolidatesB ,--a0 )))..)IVV   rcURR(agURR(agURR 5S:$)NFTr)rrany_extension_typesrNnuniquers r_is_mixed_typeNDFrame._is_mixed_types> 99 $ $ 99 ( ({{""$q((rcURR5nURXRS9R U5$Nr)rget_numeric_datarrrrrs r_get_numeric_dataNDFrame._get_numeric_data s8)),,.))' )ERRSWXXrcURR5nURXRS9R U5$rR)r get_bool_datarrrrTs r_get_bool_dataNDFrame._get_bool_datas8))))+))' )ERRSWXXrc[U5errrs rrNDFrame.valuess !$''rc[U5e)zinternal implementationrrs rrNDFrame._valuess"$''rcURR5nURXR[R S9$)a Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See :ref:`the User Guide ` for more. Returns ------- pandas.Series The data type of each column. Examples -------- >>> df = pd.DataFrame({'float': [1.0], ... 'int': [1], ... 'datetime': [pd.Timestamp('20180310')], ... 'string': ['foo']}) >>> df.dtypes float float64 int int64 datetime datetime64[ns] string object dtype: object )rr)r get_dtypes_constructor_slicedrRrhobject_rs rrNNDFrame.dtypes s48yy##%''OO2::'VVrc ^U(a[5(aSn[T5(Ga'URS:XaK[T5S:dURT;a [ S5eTURnUR XBU5$SSKJn U"T[S9nURHnXp;dM [ SUS35e URURS SS 9n/n[UR55HYun upzURU n [!U 5(aU R#US 9n OU R XUS 9n UR)U 5 M[ O[+T5(aURS:a[-T5m[/T[05(a=[3U4SjUR4R655(aUR#US 9$UR5VVs/sHupUR TX#S9PM nnnODUR4R TX#S 9nUR9UUR:S9nUR=USS9$U(dUR#S S 9$[?USSS9nURAU5nURUl UR=USS9n[C[DU5$![$an U S US34U leS n A ff=fs snnf)a Cast a pandas object to a specified dtype ``dtype``. Parameters ---------- dtype : str, data type, Series or Mapping of column name -> data type Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types. copy : bool, default True Return a copy when ``copy=True`` (be very careful setting ``copy=False`` as changes to values then may propagate to other pandas objects). .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` errors : {'raise', 'ignore'}, default 'raise' Control raising of exceptions on invalid data for provided dtype. - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress exceptions. On error return original object. Returns ------- same type as caller See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type. Notes ----- .. versionchanged:: 2.0.0 Using ``astype`` to convert from timezone-naive dtype to timezone-aware dtype will raise an exception. Use :meth:`Series.dt.tz_localize` instead. Examples -------- Create a DataFrame: >>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> df = pd.DataFrame(data=d) >>> df.dtypes col1 int64 col2 int64 dtype: object Cast all columns to int32: >>> df.astype('int32').dtypes col1 int32 col2 int32 dtype: object Cast col1 to int32 using a dictionary: >>> df.astype({'col1': 'int32'}).dtypes col1 int32 col2 int64 dtype: object Create a series: >>> ser = pd.Series([1, 2], dtype='int32') >>> ser 0 1 1 2 dtype: int32 >>> ser.astype('int64') 0 1 1 2 dtype: int64 Convert to categorical type: >>> ser.astype('category') 0 1 1 2 dtype: category Categories (2, int32): [1, 2] Convert to ordered categorical type with custom ordering: >>> from pandas.api.types import CategoricalDtype >>> cat_dtype = CategoricalDtype( ... categories=[2, 1], ordered=True) >>> ser.astype(cat_dtype) 0 1 1 2 dtype: category Categories (2, int64): [2 < 1] Create a series of dates: >>> ser_date = pd.Series(pd.date_range('20200101', periods=3)) >>> ser_date 0 2020-01-01 1 2020-01-02 2 2020-01-03 dtype: datetime64[ns] FrzFOnly the Series name can be used for the key in Series dtype mappings.rrJrzJOnly a column name can be used for the key in a dtype mappings argument. 'z' not found in columns.N)rrr)rrrz': Error while type casting for column 'r{c3@># UHoRT:Hv M g7frr)r]rYrs rr^!NDFrame.astype..s9.>s U".>r")rrrrr~rl)#rrar,rr8rrpandasrrrrr(r1riatrorr rrrbrhrrjrrarraysrrrrrr r8)rrrrnew_typer dtype_sercol_nameresultsr7rcdtres_colex_serrrrs ` rrNDFrame.astype?sr '))D   yyA~u:>TYYe%;"<!+{{86:: &uF3I%OO'"$:%<>,"))$,,4e)TIG&/ &="?HmmA&99!hhDh1G"%**3&*"Q w''>&e , ,Q 'E%00S9.2ii.>.>966yydy++KO**,JV 5t ;, G yy''e$'NH,,XHMM,JC##D#: :99$9' 'ae4""6*$$T($;D&!!O&!d"I(STU#  s,J)"K ) K3KKcURRUS9nUR5 URX"RS9R USS9$)a  Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When ``deep=False``, a new object will be created without copying the calling object's data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa). .. note:: The ``deep=False`` behaviour as described above will change in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that the "shallow" copy is that is returned with ``deep=False`` will still avoid making an eager copy, but changes to the data of the original will *no* longer be reflected in the shallow copy (or vice versa). Instead, it makes use of a lazy (deferred) copy mechanism that will copy the data only when any changes to the original or shallow copy is made. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` Parameters ---------- deep : bool, default True Make a deep copy, including a copy of the data and the indices. With ``deep=False`` neither the indices nor the data are copied. Returns ------- Series or DataFrame Object type matches caller. Notes ----- When ``deep=True``, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to `copy.deepcopy` in the Standard Library, which recursively copies object data (see examples below). While ``Index`` objects are copied when ``deep=True``, the underlying numpy array is not copied for performance reasons. Since ``Index`` is immutable, the underlying data can be safely shared and a copy is not needed. Since pandas is not thread safe, see the :ref:`gotchas ` when copying in a threading environment. When ``copy_on_write`` in pandas config is set to ``True``, the ``copy_on_write`` config takes effect even when ``deep=False``. This means that any changes to the copied data would make a new copy of the data upon write (and vice versa). Changes made to either the original or copied variable would not be reflected in the counterpart. See :ref:`Copy_on_Write ` for more information. Examples -------- >>> s = pd.Series([1, 2], index=["a", "b"]) >>> s a 1 b 2 dtype: int64 >>> s_copy = s.copy() >>> s_copy a 1 b 2 dtype: int64 **Shallow copy versus default (deep) copy:** >>> s = pd.Series([1, 2], index=["a", "b"]) >>> deep = s.copy() >>> shallow = s.copy(deep=False) Shallow copy shares data and index with original. >>> s is shallow False >>> s.values is shallow.values and s.index is shallow.index True Deep copy has own copy of data and index. >>> s is deep False >>> s.values is deep.values or s.index is deep.index False Updates to the data shared by shallow copy and original is reflected in both (NOTE: this will no longer be true for pandas >= 3.0); deep copy remains unchanged. >>> s.iloc[0] = 3 >>> shallow.iloc[1] = 4 >>> s a 3 b 4 dtype: int64 >>> shallow a 3 b 4 dtype: int64 >>> deep a 1 b 2 dtype: int64 Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy. >>> s = pd.Series([[1, 2], [3, 4]]) >>> deep = s.copy() >>> s[0][0] = 10 >>> s 0 [10, 2] 1 [3, 4] dtype: object >>> deep 0 [10, 2] 1 [3, 4] dtype: object **Copy-on-Write is set to true**, the shallow copy is not modified when the original data is changed: >>> with pd.option_context("mode.copy_on_write", True): ... s = pd.Series([1, 2], index=["a", "b"]) ... copy = s.copy(deep=False) ... s.iloc[0] = 100 ... s a 100 b 2 dtype: int64 >>> copy a 1 b 2 dtype: int64 rrrr~)rrrwrrr)rrrs rr NDFrame.copysUjyy~~4~(  ))$YY)?LL M  rc URUS9$rsr\)rrs r__copy__NDFrame.__copy__syydy##rc URSS9$)zI Parameters ---------- memo, default None Standard signature. Unused Trr\)rmemos r __deepcopy__NDFrame.__deepcopy__syydy##rcURRUS9nURX"RS9nUR USS9$)a Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction. Parameters ---------- copy : bool, default True Whether to make a copy for non-object or non-inferable columns or Series. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` Returns ------- same type as input object See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype. Examples -------- >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]}) >>> df = df.iloc[1:] >>> df A 1 1 2 2 3 3 >>> df.dtypes A object dtype: object >>> df.infer_objects().dtypes A int64 dtype: object r\r infer_objectsr~)rconvertrrr)rrrrs rr~NDFrame.infer_objectssIr))###.((||(D_==rc [U5 URRUUUUUUS9nURXwRS9nUR USS9$)a Convert columns to the best possible dtypes using dtypes supporting ``pd.NA``. Parameters ---------- infer_objects : bool, default True Whether object dtypes should be converted to the best possible types. convert_string : bool, default True Whether object dtypes should be converted to ``StringDtype()``. convert_integer : bool, default True Whether, if possible, conversion can be done to integer extension types. convert_boolean : bool, defaults True Whether object dtypes should be converted to ``BooleanDtypes()``. convert_floating : bool, defaults True Whether, if possible, conversion can be done to floating extension types. If `convert_integer` is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers. dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable' Back-end data type applied to the resultant :class:`DataFrame` (still experimental). Behaviour is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` (default). * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` DataFrame. .. versionadded:: 2.0 Returns ------- Series or DataFrame Copy of input object with new dtype. See Also -------- infer_objects : Infer dtypes of objects. to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. Notes ----- By default, ``convert_dtypes`` will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options ``convert_string``, ``convert_integer``, ``convert_boolean`` and ``convert_floating``, it is possible to turn off individual conversions to ``StringDtype``, the integer extension types, ``BooleanDtype`` or floating extension types, respectively. For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer or floating extension type, otherwise leave as ``object``. If the dtype is integer, convert to an appropriate integer extension type. If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type. In the future, as new dtypes are added that support ``pd.NA``, the results of this method will change to support those new dtypes. Examples -------- >>> df = pd.DataFrame( ... { ... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), ... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")), ... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")), ... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")), ... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")), ... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")), ... } ... ) Start with a DataFrame with default dtypes. >>> df a b c d e f 0 1 x True h 10.0 NaN 1 2 y False i NaN 100.5 2 3 z NaN NaN 20.0 200.0 >>> df.dtypes a int32 b object c object d object e float64 f float64 dtype: object Convert the DataFrame to use best possible dtypes. >>> dfn = df.convert_dtypes() >>> dfn a b c d e f 0 1 x True h 10 1 2 y False i 100.5 2 3 z 20 200.0 >>> dfn.dtypes a Int32 b string[python] c boolean d string[python] e Int64 f Float64 dtype: object Start with a Series of strings and missing data represented by ``np.nan``. >>> s = pd.Series(["a", "b", np.nan]) >>> s 0 a 1 b 2 NaN dtype: object Obtain a Series with dtype ``StringDtype``. >>> s.convert_dtypes() 0 a 1 b 2 dtype: string )r~convert_stringconvert_integerconvert_booleanconvert_floating dtype_backendrconvert_dtypesr~)rVrrrrr) rr~rrrrrrrs rrNDFrame.convert_dtypesshT M*))**')++-' + ((||(D-=>>rcU[RLa([R"SUS3[[ 5S9 U$SnU$)NThe 'downcast' keyword in z is deprecated and will be removed in a future version. Use res.infer_objects(copy=False) to infer non-object dtype, or pd.to_numeric with the 'downcast' keyword to downcast numeric results.r)rrrr rrU)rdowncast method_names r_deprecate_downcastNDFrame._deprecate_downcastsI 3>> ) MM,[M: +- Hrrrr limit_arearc UcSnURU5n[U5nURR(d=US:Xa7U(a [ 5eUR R XUS9R nU$URRUURU5UUUUS9nURXRS9nU(aURU5$URUSS9$)Nrr)rrrrrrrrrrfillnar~) r!rrrrr=_pad_or_backfillpad_or_backfillrrrrr) rrrrrrrrrs rrNDFrame._pad_or_backfills <D$$T*"6*yy((TQY)++VV,,z-a M))++--d3! , ++G,,+G ''/ /&&tH&= =r)rrrrrcgrrrrrrrrrs rrNDFrame.fillnarr)rrrrcgrrrs rrrrrcgrrrs rrrrrr)rrcX [US5nU(a[(dL[5(a=[R"U5[ ::a[ R"[[SS9 O[(d[5(d~UR5(ai[R"U5n[ n[U[5(a[U5(aUS- nXx::a[ R"[[SS9 [!X5upUb8[ R"[#U5R$S3[['5S9 U[(R*Ln UR-US5nUcSnUR/U5nUcUR1UUUUUS 9$UR2S:Xa[U[4[45(a\[7U5(dU(agUR9SS 9$SS KJn U "U5nUR?UR@S S 9nURBnO3[EU5(dO"[GS[#U5R$S35eURHRKXXFS9n GOz[U[4[45(GaUS:Xa [MS5e[5(aUR9SS 9n OU(aUOUR95n [U[45n URO5GHupX;aM U (a[(R*nOU (dUOURQU5nXRKXUS9nU(dUX'M^[U[5(a7URRXRR:XaUU RTSS2U4'MUX'MU RVRYU5n[U[Z5(a'[\R^"UR`S5UnO[U[\Rb5(a.URRRdS:XaURg5SnOD[U[\Rb5(aURRRdS:Xd [MS5e[iU5HmunnURjSS2U4nURjSS2U4nURRURR:XaUU RjSS2U4'M[U RmUU5 Mo GM U(aURoU 5$U $[EU5(dQUS:Xa0URpRKXS9Rpn U RHn OURHRKXXFS9n Og[U[r5(a;UR2S:Xa+URuURw5U5RHn O[yS[#U535eUR{XR|S9n U(aURoU 5$U RUSS9$)aw Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. method : {{'backfill', 'bfill', 'ffill', None}}, default None Method to use for filling holes in reindexed Series: * ffill: propagate last valid observation forward to next valid. * backfill / bfill: use next valid observation to fill gap. .. deprecated:: 2.1.0 Use ffill or bfill instead. axis : {axes_single_arg} Axis along which to fill missing values. For `Series` this parameter is unused and defaults to 0. inplace : bool, default False If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame). limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. downcast : dict, default is None A dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). .. deprecated:: 2.2.0 Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. See Also -------- ffill : Fill values by propagating the last valid observation to next valid. bfill : Fill values by using the next valid observation to fill the gap. interpolate : Fill NaN values using interpolation. reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency. Examples -------- >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0], ... [3, 4, np.nan, 1], ... [np.nan, np.nan, np.nan, np.nan], ... [np.nan, 3, np.nan, 4]], ... columns=list("ABCD")) >>> df A B C D 0 NaN 2.0 NaN 0.0 1 3.0 4.0 NaN 1.0 2 NaN NaN NaN NaN 3 NaN 3.0 NaN 4.0 Replace all NaN elements with 0s. >>> df.fillna(0) A B C D 0 0.0 2.0 0.0 0.0 1 3.0 4.0 0.0 1.0 2 0.0 0.0 0.0 0.0 3 0.0 3.0 0.0 4.0 Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively. >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}} >>> df.fillna(value=values) A B C D 0 0.0 2.0 2.0 0.0 1 3.0 4.0 2.0 1.0 2 0.0 1.0 2.0 3.0 3 0.0 3.0 2.0 4.0 Only replace the first NaN element. >>> df.fillna(value=values, limit=1) A B C D 0 0.0 2.0 2.0 0.0 1 3.0 4.0 NaN 1.0 2 NaN 1.0 NaN 3.0 3 NaN 3.0 NaN 4.0 When filling using a DataFrame, replacement happens along the same column names and same indices >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE")) >>> df.fillna(df2) A B C D 0 0.0 2.0 0.0 0.0 1 3.0 4.0 0.0 1.0 2 0.0 0.0 0.0 NaN 3 0.0 3.0 0.0 4.0 Note that column D is not affected since it is not present in df2. rr+rrNzo.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.rr)rrrrrrJFr\zF"value" parameter must be a scalar, dict or Series, but you passed a "")rrrrz9Currently only can fill with dict/Series column by column)rrbr7zVUnexpected get_loc result, please report a bug at https://github.com/pandas-dev/pandas)rrzinvalid fill value with a rr~)@rXrGrsys getrefcountrHrr rPrLrWrrlrRrQrrYr rrUrrrr!rr,rrrrgrrrrrcrrrrrrrrfr(r`rrhrPrararrbr1risetitemrr=rkwhererpr rrr)rrrrrrrctr ref_countwas_no_defaultrrris_dictrErF downcast_kres_klocsr7rfres_locrs rrrs8z&gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..=3F3FNI#MM>%#$ /u=    MM:&&'(+-  "S^^3++Hh? <D$$T* =(( ")  yyA~edI%677u::"#'#yydy33-"5ME!MM$**5MAE!MME%e,,# K0014  99++g,ED)#45519-$ '((!YYDY1F%,T$))+F$Xt4!KKMDA %%(^^ $+%"*a##I,,Qj,QE"$) &eY77${{fioo=38 1a4 0-2 $*>>#9#9!#dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). .. deprecated:: 2.2.0 Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. Examples -------- >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0], ... [3, 4, np.nan, 1], ... [np.nan, np.nan, np.nan, np.nan], ... [np.nan, 3, np.nan, 4]], ... columns=list("ABCD")) >>> df A B C D 0 NaN 2.0 NaN 0.0 1 3.0 4.0 NaN 1.0 2 NaN NaN NaN NaN 3 NaN 3.0 NaN 4.0 >>> df.ffill() A B C D 0 NaN 2.0 NaN 0.0 1 3.0 4.0 NaN 1.0 2 3.0 4.0 NaN 1.0 3 3.0 3.0 NaN 4.0 >>> ser = pd.Series([1, np.nan, 2, 3]) >>> ser.ffill() 0 1.0 1 1.0 2 2.0 3 3.0 dtype: float64 rrr+rrrrrXrGrrrrHrr rPrLrWrrlrRrQrrrrrrrrrrs rrr<sj++Hg>%gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..=3F3FNI#MM>%#$ $$ !%  rrrrrcf[R"S[[5S9 UR XX4S9$)a] Fill NA/NaN values by propagating the last valid observation to next valid. .. deprecated:: 2.0 {klass}.pad is deprecated. Use {klass}.ffill instead. Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. Examples -------- Please see examples for :meth:`DataFrame.ffill` or :meth:`Series.ffill`. zPDataFrame.pad/Series.pad is deprecated. Use DataFrame.ffill/Series.ffill insteadrr)rr rrUrrrrrrs rpad NDFrame.pad34   3 ')  zztEzUUrcgrrrs rbfill NDFrame.bfillrr)rrrcgrrrs rrrs rcgrrrs rrrrrc RURUS5n[US5nU(a[(dL[5(a=[R "U5[ ::a[R"[[SS9 O[(d[5(d~UR5(ai[R "U5n[ n[U[5(a[U5(aUS- nXg::a[R"[[ SS9 UR#SUUUUUS9$)a Fill NA/NaN values by using the next valid observation to fill the gap. Parameters ---------- axis : {axes_single_arg} Axis along which to fill missing values. For `Series` this parameter is unused and defaults to 0. inplace : bool, default False If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame). limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. limit_area : {{`None`, 'inside', 'outside'}}, default None If limit is specified, consecutive NaNs will be filled with this restriction. * ``None``: No fill restriction. * 'inside': Only fill NaNs surrounded by valid values (interpolate). * 'outside': Only fill NaNs outside valid values (extrapolate). .. versionadded:: 2.2.0 downcast : dict, default is None A dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). .. deprecated:: 2.2.0 Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. Examples -------- For Series: >>> s = pd.Series([1, None, None, 2]) >>> s.bfill() 0 1.0 1 2.0 2 2.0 3 2.0 dtype: float64 >>> s.bfill(limit=1) 0 1.0 1 NaN 2 2.0 3 2.0 dtype: float64 With DataFrame: >>> df = pd.DataFrame({{'A': [1, None, None, 4], 'B': [None, 5, None, 7]}}) >>> df A B 0 1.0 NaN 1 NaN 5.0 2 NaN NaN 3 4.0 7.0 >>> df.bfill() A B 0 1.0 5.0 1 4.0 5.0 2 4.0 7.0 3 4.0 7.0 >>> df.bfill(limit=1) A B 0 1.0 5.0 1 NaN 5.0 2 4.0 7.0 3 4.0 7.0 rrr+rrrrrs rrrs@++Hg>%gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..=3F3FNI#MM>%#$ $$ !%  rcf[R"S[[5S9 UR XX4S9$)a^ Fill NA/NaN values by using the next valid observation to fill the gap. .. deprecated:: 2.0 {klass}.backfill is deprecated. Use {klass}.bfill instead. Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. Examples -------- Please see examples for :meth:`DataFrame.bfill` or :meth:`Series.bfill`. zZDataFrame.backfill/Series.backfill is deprecated. Use DataFrame.bfill/Series.bfill insteadrr)rr rrUrrs rbackfillNDFrame.backfillrr)rrrrcgrrr to_replacerrrrrs rr NDFrame.replacerr)rrrcgrrrs rr rrrcgrrrs rr rrr)rrc<U[RLa:[R"S[ U5R S3[ [5S9 O5(a,S SK J!n UREU RFXX44S9n U(agU $URGXX45$[U5(d[U5(d [S5eUnSn[=URI55n U (a [KU 6upO//pU Vs/sHn[U5PM nn[MU5(aw[OU5(d [S5e0n0nU HNunn[=[KURI565=(d //4up[=U 5UU'[=U 5UU'MP UUp!OXp!URQXX4US9$URR(dU(agURUSS9$[U5(a[U5(aYURW5Vs0sH,nUURW5;dMUU;dM!UUUUU4_M. nnURYUX55$[U5(dUURZS :Xa [9S5eURI5VVs0sH unnUUU4_M nnnURYUX55$[S5e[U5(ay[U5(dU/[]U5-n[]U5[]U5:wa$[9S[]U5S[]U5S35eUR^RaUUUUS9nGOVUck[U5(dJ[U5(d:[U5(d*[S[[ U5R 535eURQXRX4SS9$[U5(aUURZS :Xa [9S5eURI5VVs0sH unnUUU4_M nnnURYUX55$[U5(dI[cXQ5nU(aUR^ReUUUS9nOEUR^RQXUS9nO*[S[[ U5R 535eURgUURhS9n U(aURkU 5$U RmUS S!9$s snfs snfs snnfs snnf)"NzThe 'method' keyword in z?.replace is deprecated and will be removed in a future version.rzThe 'limit' keyword in Fz.replace without 'value' and with non-dict-like 'to_replace' is deprecated and will raise in a future version. Explicitly specify the new values instead.zYExpecting 'to_replace' to be either a scalar, array-like, dict or None, got invalid type rr+rz4'to_replace' must be 'None' if 'regex' is not a boolrrrJ)rzfIf "to_replace" and "value" are both None and "to_replace" is not a list, then regex must be a mappingTzSIf a nested mapping is passed, all values of the top level mapping must be mappings)rrrrzASeries.replace cannot use dict-like to_replace and non-None valuez.value argument must be scalar, dict, or Seriesz2Replacement lists must match in length. Expecting z got r#)src_list dest_listrrz|'regex' must be a string or a compiled regular expression or a list or dict of strings or regular expressions, you passed a z+>*?@FF+-    MM)$t**=*=)>?FF+-   S^^ ##..( ,, MM:&&'(==+-  j ! ! ++J''2Z(11235  &gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..=3F3F NI#MM>%#$ u~~*"8ST T CNN "fCNN&B ' ++L4G4G(\ *udm44dL11-!ZZ..('A(F#!M++JOO ++#E**#2 # ))+,E"E{ f!#Bf5; >C=RbUs$%^0^  ^ ^ 5^^)rrrlimit_directionrrc grr rrrrrrrrrs r interpolateNDFrame.interpolate r)rrrrrc grrrs rrrrrc grrrs rrrrrc V U[RLa:[R"S[ U5R S3[ [5S9 OSnUbUS:wa [S5e[US5nU(a[(dL[5(a=[R"U5[::a[R"[[ SS9 O[(d[5(d~UR#5(ai[R"U5n [n [%U[&5(a[)U5(aU S - n X::a[R"[*[ SS9 UR-U5nUR.(aU(agUR15$[%U[25(d [S 5e/S Qn UR55U ;a?[R"[ U5R S US 3[ [5S9 USpOUS :XaUR6S4OUS4up[8R:"U R<[>:H5(atU R@S:Xa,[8RB"U R<[>:H5(d8[R"[ U5R S3[ [5S9 X;a*SU;a$[S[ U5R SU 35e[%U RD[F5(aUS:wa [S5e[HRJ"XQ5nU R@S:Xa7[8RB"U R<[>:H5(a [MS5eUR55U ;arURNRP(d)US :Xa#U(a [S5eUR6S U- Spn U RNRUUURWU5UUUUS9nOD[HRX"XRD5nU RNRZ"SUUUUUUUS.UD6nUR]XR^S9nU (a UR6nU(aURaU5$URcUSS9$)a+ Fill NaN values using an interpolation method. Please note that only ``method='linear'`` is supported for DataFrame/Series with a MultiIndex. Parameters ---------- method : str, default 'linear' Interpolation technique to use. One of: * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. * 'time': Works on daily and higher resolution data to interpolate given length of interval. * 'index', 'values': use the actual numerical values of the index. * 'pad': Fill in NaNs using existing values. * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial': Passed to `scipy.interpolate.interp1d`, whereas 'spline' is passed to `scipy.interpolate.UnivariateSpline`. These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify an `order` (int), e.g. ``df.interpolate(method='polynomial', order=5)``. Note that, `slinear` method in Pandas refers to the Scipy first order `spline` instead of Pandas first order `spline`. * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 'cubicspline': Wrappers around the SciPy interpolation methods of similar names. See `Notes`. * 'from_derivatives': Refers to `scipy.interpolate.BPoly.from_derivatives`. axis : {{0 or 'index', 1 or 'columns', None}}, default None Axis to interpolate along. For `Series` this parameter is unused and defaults to 0. limit : int, optional Maximum number of consecutive NaNs to fill. Must be greater than 0. inplace : bool, default False Update the data in place if possible. limit_direction : {{'forward', 'backward', 'both'}}, Optional Consecutive NaNs will be filled in this direction. If limit is specified: * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'. * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be 'backwards'. If 'limit' is not specified: * If 'method' is 'backfill' or 'bfill', the default is 'backward' * else the default is 'forward' raises ValueError if `limit_direction` is 'forward' or 'both' and method is 'backfill' or 'bfill'. raises ValueError if `limit_direction` is 'backward' or 'both' and method is 'pad' or 'ffill'. limit_area : {{`None`, 'inside', 'outside'}}, default None If limit is specified, consecutive NaNs will be filled with this restriction. * ``None``: No fill restriction. * 'inside': Only fill NaNs surrounded by valid values (interpolate). * 'outside': Only fill NaNs outside valid values (extrapolate). downcast : optional, 'infer' or None, defaults to None Downcast dtypes if possible. .. deprecated:: 2.1.0 ``**kwargs`` : optional Keyword arguments to pass on to the interpolating function. Returns ------- Series or DataFrame or None Returns the same object type as the caller, interpolated at some or all ``NaN`` values or None if ``inplace=True``. See Also -------- fillna : Fill missing values using different methods. scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials (Akima interpolator). scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the Bernstein basis. scipy.interpolate.interp1d : Interpolate a 1-D function. scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh interpolator). scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic interpolation. scipy.interpolate.CubicSpline : Cubic spline data interpolator. Notes ----- The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the `SciPy documentation `__. Examples -------- Filling in ``NaN`` in a :class:`~pandas.Series` via linear interpolation. >>> s = pd.Series([0, 1, np.nan, 3]) >>> s 0 0.0 1 1.0 2 NaN 3 3.0 dtype: float64 >>> s.interpolate() 0 0.0 1 1.0 2 2.0 3 3.0 dtype: float64 Filling in ``NaN`` in a Series via polynomial interpolation or splines: Both 'polynomial' and 'spline' methods require that you also specify an ``order`` (int). >>> s = pd.Series([0, 2, np.nan, 8]) >>> s.interpolate(method='polynomial', order=2) 0 0.000000 1 2.000000 2 4.666667 3 8.000000 dtype: float64 Fill the DataFrame forward (that is, going down) along each column using linear interpolation. Note how the last entry in column 'a' is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column 'b' remains ``NaN``, because there is no entry before it to use for interpolation. >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0), ... (np.nan, 2.0, np.nan, np.nan), ... (2.0, 3.0, np.nan, 9.0), ... (np.nan, 4.0, -4.0, 16.0)], ... columns=list('abcd')) >>> df a b c d 0 0.0 NaN -1.0 1.0 1 NaN 2.0 NaN NaN 2 2.0 3.0 NaN 9.0 3 NaN 4.0 -4.0 16.0 >>> df.interpolate(method='linear', limit_direction='forward', axis=0) a b c d 0 0.0 NaN -1.0 1.0 1 1.0 2.0 -2.0 5.0 2 2.0 3.0 -3.0 9.0 3 2.0 4.0 -4.0 16.0 Using polynomial interpolation. >>> df['d'].interpolate(method='polynomial', order=2) 0 1.0 1 4.0 2 9.0 3 16.0 Name: d, dtype: float64 rz.interpolate is deprecated and will be removed in a future version. Call result.infer_objects(copy=False) on the result instead.rNrz'downcast must be either None or 'infer'rr+rz&'method' should be a string, not None.)rrrrz.interpolate with method=zZ is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.FTz.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.rz('fill_value' is not a valid keyword for z.interpolate with method from linearz@Only `method=linear` interpolation is supported on MultiIndexes.zvCannot interpolate with all object-dtype columns in the DataFrame. Try setting at least one column to a numeric dtype.r)rrrrrrrrrr~r)2rrrr r rrrUr rXrGrrrrHrPrLrWrrlrRrQr!rRrrlowerr=rhrrNrr,rrrruinfer_limit_directionrrrrrrget_interp_indexrrrrr)rrrrrrrrrrrfillna_methodsrshould_transposerrrs rrr sl 3>> ) MM,T$Z-@-@,ABOO+-  H  H$7FG G%gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..=3F3FNI#MM>%#$ $$T* ::99; &#&&EF F> <<>^ + MM:&&''@I::+-  %)%!6:aiTVVTNdE] !CvvcjjF*++A "&&v1E*F*FMM:../0VV#5   # (>::&&''E!"$  cii , ,81CR "77P 88q=RVVCJJ&$899-  <<>^ +99,,-//.2ffa$h+xx//11$7%! 0H,,VYY?Exx++  /%!  H++H==+I XXF ''/ /&&tM&B Brc[U[5(a [U5nURR(d [ S5e[U[ 5nU(aUb [ S5eO"Uc URn[U5(dU/n[U5nU(GdURSn[UR[5(a[XRRS9nX:a@U(d)URURU[RS9$[R$U(alURR!USS9nUS:aUS-nUR"nUS:a1[%Xv5(aUS-nUS:a[%Xv5(aMXv$[U[&5(dU(a ['U5O ['U/5nU(aUR%5OXR%5R)SS 9nUR+5(aU(a4[-S U5nUR/[RXR0S 9$U(a4[-S U5nUR/[RXRS 9$[-S U5nUR[RURUSS 9$URR3XR")5n U S:Hn UR5U 5n XlU R)5(a[RU R6U 'U(aU $U R8S$)a Return the last row(s) without any NaNs before `where`. The last row (for each element in `where`, if list) without any NaN is taken. In case of a :class:`~pandas.DataFrame`, the last row without NaN considering only the subset of columns (if not `None`) If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame Parameters ---------- where : date or array-like of dates Date(s) before which the last row(s) are returned. subset : str or array-like of str, default `None` For DataFrame, if not `None`, only use these columns to check for NaNs. Returns ------- scalar, Series, or DataFrame The return can be: * scalar : when `self` is a Series and `where` is a scalar * Series: when `self` is a Series and `where` is an array-like, or when `self` is a DataFrame and `where` is a scalar * DataFrame : when `self` is a DataFrame and `where` is an array-like See Also -------- merge_asof : Perform an asof merge. Similar to left join. Notes ----- Dates are assumed to be sorted. Raises if this is not the case. Examples -------- A Series and a scalar `where`. >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40]) >>> s 10 1.0 20 2.0 30 NaN 40 4.0 dtype: float64 >>> s.asof(20) 2.0 For a sequence `where`, a Series is returned. The first value is NaN, because the first element of `where` is before the first index value. >>> s.asof([5, 20]) 5 NaN 20 2.0 dtype: float64 Missing values are not considered. The following is ``2.0``, not NaN, even though NaN is at the index location for ``30``. >>> s.asof(30) 2.0 Take all columns into consideration >>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.], ... 'b': [None, None, None, None, 500]}, ... index=pd.DatetimeIndex(['2018-02-27 09:01:00', ... '2018-02-27 09:02:00', ... '2018-02-27 09:03:00', ... '2018-02-27 09:04:00', ... '2018-02-27 09:05:00'])) >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30'])) a b 2018-02-27 09:03:30 NaN NaN 2018-02-27 09:04:30 NaN NaN Take a single column into consideration >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30']), ... subset=['a']) a b 2018-02-27 09:03:30 30.0 NaN 2018-02-27 09:04:30 40.0 NaN zasof requires a sorted indexzsubset is not valid for Seriesr)freq)rr8rrightsiderrr)rr8rrr)rrrris_monotonic_increasingr rlr(rcrrrrarhfloat64nan searchsortedrror~rrr rr8 asof_locsrLrfr) rrr is_seriesis_listrQrfrnullsrrrs rasof NDFrame.asofI!s~ eS ! !e$Ezz11;< <tY/ ! !ABB"~'' u%JJqME$**k22u::??;} 33"llbjj4vv jj--e'-B71HCAg$v{"3"31HCAg$v{"3"3{"%''$+E%LwE( dl.?.?.A.E.E1.E.M 99;;Hd+((u99(MMK.((ull(SSK.//FF$,,U1X0zz##E]]+;<rzyy 88:: VVDHHTNt1DIIbM1rc4[U5RUSS9$)a Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:`numpy.NaN`, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). Returns ------- {klass} Mask of bool values for each element in {klass} that indicates whether an element is an NA value. See Also -------- {klass}.isnull : Alias of isna. {klass}.notna : Boolean inverse of isna. {klass}.dropna : Omit axes labels with missing values. isna : Top-level isna. Examples -------- Show which entries in a DataFrame are NA. >>> df = pd.DataFrame(dict(age=[5, 6, np.nan], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False Show which entries in a Series are NA. >>> ser = pd.Series([5, 6, np.nan]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.isna() 0 False 1 False 2 True dtype: bool ror~rorrs rro NDFrame.isna!szDz&&tF&;;rc4[U5RUSS9$)Nisnullr~rrs rrNDFrame.isnull4"sDz&&tH&==rc4[U5RUSS9$)a Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). NA values, such as None or :attr:`numpy.NaN`, get mapped to False values. Returns ------- {klass} Mask of bool values for each element in {klass} that indicates whether an element is not an NA value. See Also -------- {klass}.notnull : Alias of notna. {klass}.isna : Boolean inverse of notna. {klass}.dropna : Omit axes labels with missing values. notna : Top-level notna. Examples -------- Show which entries in a DataFrame are not NA. >>> df = pd.DataFrame(dict(age=[5, 6, np.nan], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True Show which entries in a Series are not NA. >>> ser = pd.Series([5, 6, np.nan]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.notna() 0 True 1 True 2 False dtype: bool rpr~rprrs rrp NDFrame.notna8"szT{''W'==rc4[U5RUSS9$)Nnotnullr~rrs rrNDFrame.notnullw"sT{''Y'??rcZUb$[R"[U55(d'Ub/[R"[U55(a [S5eUnUR5nUbXPU:-nUR XaUS9nUb"XPU:*-nU(aUOUnUR XbUS9nU$)Nz*Cannot use an NA value as a clip thresholdr)rhrror r)rrupperrrrconds r_clip_with_scalarNDFrame._clip_with_scalar{"s  "&&e"5"5  "&&e"5"5IJ Jyy{  5=)D\\W"F  5=)D$T&F\\W"F rcUbURU5n[U5(aA[U5(a1URS:XaUR SXS9$UR USUS9$[ U[ 5(dR[U5(aB[ U[ 5(aURXRS9nOURXSS9Sn[U5(aCURS:Xa[RO[R*nURU5nOUnU"XcS9[U5-nURXqX4S9$)Nler)r)flexrrr4)r!rgrdrrrrlrcrr _align_for_oprhinfrror)r thresholdrrrr threshold_infrs r_clip_with_one_boundNDFrame._clip_with_one_bound"s&  ((.D Y  Ii$8$8$&--dI-OO)))T7)K K 9i00l96M6M$ ** --izz-J  ..yT.J1M   " "#)??d#:J%,,Z8M%M 1DJ>zz&$zHHrr4c grrrrrrrrs rclip NDFrame.clip"rrrc grrrs rrr"rrc grrrs rrr"rrc ([US5nU(a[(dL[5(a=[R"U5[ ::a[ R"[[SS9 O[(d[5(dUR5(aj[R"U5n[ n[U[5(a[US5(aUS- nXg::a[ R"[[SS9 [ R""USU5nUbUR%U5n['U5n[)U5(d[*R,"U5(aSnO[*R."U5(aSn['U5n [)U5(d[*R,"U 5(aSnO[*R."U 5(aSnUb8Ub5[1U5(a%[1U5(a[3X5[5X5p!Ub[7U5(a#Ub[7U5(aUR9XUS9$Un UbU R;XR<X4S 9n Ub#U(aUn U R;X R>X4S 9n U $) a Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis. Parameters ---------- lower : float or array-like, default None Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g `NA`) will not clip the value. upper : float or array-like, default None Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g `NA`) will not clip the value. axis : {{0 or 'index', 1 or 'columns', None}}, default None Align object with lower and upper along the given axis. For `Series` this parameter is unused and defaults to `None`. inplace : bool, default False Whether to perform the operation in place on the data. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with numpy. Returns ------- Series or DataFrame or None Same type as calling object with the values outside the clip boundaries replaced or None if ``inplace=True``. See Also -------- Series.clip : Trim values at input threshold in series. DataFrame.clip : Trim values at input threshold in dataframe. numpy.clip : Clip (limit) the values in an array. Examples -------- >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]} >>> df = pd.DataFrame(data) >>> df col_0 col_1 0 9 -2 1 -3 -7 2 0 6 3 -1 8 4 5 -5 Clips per column using lower and upper thresholds: >>> df.clip(-4, 6) col_0 col_1 0 6 -2 1 -3 -4 2 0 6 3 -1 6 4 5 -4 Clips using specific lower and upper thresholds per column: >>> df.clip([-2, -1], [4, 5]) col_0 col_1 0 4 -1 1 -2 -1 2 0 5 3 -1 5 4 4 -1 Clips using specific lower and upper thresholds per column element: >>> t = pd.Series([2, -4, -1, 6, 3]) >>> t 0 2 1 -4 2 -1 3 6 4 3 dtype: int64 >>> df.clip(t, t + 4, axis=0) col_0 col_1 0 6 2 1 -3 -4 2 0 3 3 6 8 4 5 3 Clips using specific lower threshold per column element, with missing values: >>> t = pd.Series([2, -4, np.nan, 6, 3]) >>> t 0 2.0 1 -4.0 2 NaN 3 6.0 4 3.0 dtype: float64 >>> df.clip(t, axis=0) col_0 col_1 0 9 2 1 -3 -4 2 0 6 3 6 8 4 5 3 rr+rrrrNr)rrr) rXrGrrrrHrr rPrLrWrrlhasattrrQrrMvalidate_clip_with_axisr!rorcrhrrrgminmaxrdrrger) rrrrrrrr isna_lower isna_upperrs rrr"s(l&gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..743K3KNI#MM>%#$ ))$F;  ((.D%[ E""vvj!! VVJ  E%[ E""vvj!! VVJ  E  !%  %  u,c%.?5 MYu--EMYuEUEU))%)H H  00ggD1F  00ggD1F rc $SSKJn U"UUUUUUS9$)a Convert time series to specified frequency. Returns the original data conformed to a new index with the specified frequency. If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index is the result of transforming the original index with :meth:`PeriodIndex.asfreq ` (so the original index will map one-to-one to the new index). Otherwise, the new index will be equivalent to ``pd.date_range(start, end, freq=freq)`` where ``start`` and ``end`` are, respectively, the first and last entries in the original index (see :func:`pandas.date_range`). The values corresponding to any timesteps in the new index which were not present in the original index will be null (``NaN``), unless a method for filling such unknowns is provided (see the ``method`` parameter below). The :meth:`resample` method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency. Parameters ---------- freq : DateOffset or str Frequency DateOffset or string. method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): * 'pad' / 'ffill': propagate last valid observation forward to next valid * 'backfill' / 'bfill': use NEXT valid observation to fill. how : {{'start', 'end'}}, default end For PeriodIndex only (see PeriodIndex.asfreq). normalize : bool, default False Whether to reset output index to midnight. fill_value : scalar, optional Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present). Returns ------- {klass} {klass} object reindexed to the specified frequency. See Also -------- reindex : Conform DataFrame to new index with optional filling logic. Notes ----- To learn more about the frequency strings, please see `this link `__. Examples -------- Start by creating a series with 4 one minute timestamps. >>> index = pd.date_range('1/1/2000', periods=4, freq='min') >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index) >>> df = pd.DataFrame({{'s': series}}) >>> df s 2000-01-01 00:00:00 0.0 2000-01-01 00:01:00 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:03:00 3.0 Upsample the series into 30 second bins. >>> df.asfreq(freq='30s') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 NaN 2000-01-01 00:03:00 3.0 Upsample again, providing a ``fill value``. >>> df.asfreq(freq='30s', fill_value=9.0) s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 9.0 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 9.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 9.0 2000-01-01 00:03:00 3.0 Upsample again, providing a ``method``. >>> df.asfreq(freq='30s', method='bfill') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 2.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 3.0 2000-01-01 00:03:00 3.0 r)asfreq)rhow normalizer)pandas.core.resampler)rrrr r!rrs rrNDFrame.asfreq#s'f 0  !   rcUcSnURU5nURU5n[U[5(d [ S5eUR XS9nUR XSS9$)a Select values at particular time of day (e.g., 9:30AM). Parameters ---------- time : datetime.time or str The values to select. axis : {0 or 'index', 1 or 'columns'}, default 0 For `Series` this parameter is unused and defaults to 0. Returns ------- Series or DataFrame Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- between_time : Select values between particular times of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='12h') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-09 12:00:00 2 2018-04-10 00:00:00 3 2018-04-10 12:00:00 4 >>> ts.at_time('12:00') A 2018-04-09 12:00:00 2 2018-04-10 12:00:00 4 rIndex must be DatetimeIndex)rr)r!rrr}rindexer_at_timerX)rtimerrrrs rat_timeNDFrame.at_time$smZ <D$$T*t$%//9: :'''8&&w&::rcUcSnURU5nURU5n[U[5(d [ S5e[ U5upgUR UUUUS9nURXS9$)a Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting ``start_time`` to be later than ``end_time``, you can get the times that are *not* between the two times. Parameters ---------- start_time : datetime.time or str Initial time as a time filter limit. end_time : datetime.time or str End time as a time filter limit. inclusive : {"both", "neither", "left", "right"}, default "both" Include boundaries; whether to set each bound as closed or open. axis : {0 or 'index', 1 or 'columns'}, default 0 Determine range time on index or columns value. For `Series` this parameter is unused and defaults to 0. Returns ------- Series or DataFrame Data from the original object filtered to the specified dates range. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- at_time : Select values at a particular time of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 2018-04-12 01:00:00 4 >>> ts.between_time('0:15', '0:45') A 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 You get the times that are *not* between two times by setting ``start_time`` later than ``end_time``: >>> ts.between_time('0:45', '0:15') A 2018-04-09 00:00:00 1 2018-04-12 01:00:00 4 rr%) include_start include_endr)r!rrr}rrZindexer_between_timerX) r start_timeend_time inclusiverrleft_inclusiveright_inclusivers r between_timeNDFrame.between_timeQ$sH <D$$T*t$%//9: :*` for examples). .. versionchanged:: 2.0.0 ``group_keys`` now defaults to ``False``. Returns ------- pandas.api.typing.Resampler :class:`~pandas.core.Resampler` object. See Also -------- Series.resample : Resample a Series. DataFrame.resample : Resample a DataFrame. groupby : Group {klass} by mapping, function, label, or list of labels. asfreq : Reindex a {klass} with the given frequency without grouping. Notes ----- See the `user guide `__ for more. To learn more about the offset strings, please see `this link `__. Examples -------- Start by creating a series with 9 one minute timestamps. >>> index = pd.date_range('1/1/2000', periods=9, freq='min') >>> series = pd.Series(range(9), index=index) >>> series 2000-01-01 00:00:00 0 2000-01-01 00:01:00 1 2000-01-01 00:02:00 2 2000-01-01 00:03:00 3 2000-01-01 00:04:00 4 2000-01-01 00:05:00 5 2000-01-01 00:06:00 6 2000-01-01 00:07:00 7 2000-01-01 00:08:00 8 Freq: min, dtype: int64 Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin. >>> series.resample('3min').sum() 2000-01-01 00:00:00 3 2000-01-01 00:03:00 12 2000-01-01 00:06:00 21 Freq: 3min, dtype: int64 Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed value in the resampled bucket with the label ``2000-01-01 00:03:00`` does not include 3 (if it did, the summed value would be 6, not 3). >>> series.resample('3min', label='right').sum() 2000-01-01 00:03:00 3 2000-01-01 00:06:00 12 2000-01-01 00:09:00 21 Freq: 3min, dtype: int64 To include this value close the right side of the bin interval, as shown below. >>> series.resample('3min', label='right', closed='right').sum() 2000-01-01 00:00:00 0 2000-01-01 00:03:00 6 2000-01-01 00:06:00 15 2000-01-01 00:09:00 15 Freq: 3min, dtype: int64 Upsample the series into 30 second bins. >>> series.resample('30s').asfreq()[0:5] # Select first 5 rows 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 1.0 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 Freq: 30s, dtype: float64 Upsample the series into 30 second bins and fill the ``NaN`` values using the ``ffill`` method. >>> series.resample('30s').ffill()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 2000-01-01 00:01:30 1 2000-01-01 00:02:00 2 Freq: 30s, dtype: int64 Upsample the series into 30 second bins and fill the ``NaN`` values using the ``bfill`` method. >>> series.resample('30s').bfill()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 1 2000-01-01 00:01:00 1 2000-01-01 00:01:30 2 2000-01-01 00:02:00 2 Freq: 30s, dtype: int64 Pass a custom function via ``apply`` >>> def custom_resampler(arraylike): ... return np.sum(arraylike) + 5 ... >>> series.resample('3min').apply(custom_resampler) 2000-01-01 00:00:00 8 2000-01-01 00:03:00 17 2000-01-01 00:06:00 26 Freq: 3min, dtype: int64 For DataFrame objects, the keyword `on` can be used to specify the column instead of the index for resampling. >>> d = {{'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}} >>> df = pd.DataFrame(d) >>> df['week_starting'] = pd.date_range('01/01/2018', ... periods=8, ... freq='W') >>> df price volume week_starting 0 10 50 2018-01-07 1 11 60 2018-01-14 2 9 40 2018-01-21 3 13 100 2018-01-28 4 14 50 2018-02-04 5 18 100 2018-02-11 6 17 40 2018-02-18 7 19 50 2018-02-25 >>> df.resample('ME', on='week_starting').mean() price volume week_starting 2018-01-31 10.75 62.5 2018-02-28 17.00 60.0 For a DataFrame with MultiIndex, the keyword `level` can be used to specify on which level the resampling needs to take place. >>> days = pd.date_range('1/1/2000', periods=4, freq='D') >>> d2 = {{'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}} >>> df2 = pd.DataFrame( ... d2, ... index=pd.MultiIndex.from_product( ... [days, ['morning', 'afternoon']] ... ) ... ) >>> df2 price volume 2000-01-01 morning 10 50 afternoon 11 60 2000-01-02 morning 9 40 afternoon 13 100 2000-01-03 morning 14 50 afternoon 18 100 2000-01-04 morning 17 40 afternoon 19 50 >>> df2.resample('D', level=0).sum() price volume 2000-01-01 21 110 2000-01-02 22 140 2000-01-03 32 150 2000-01-04 36 90 If you want to adjust the start of the bins based on a fixed timestamp: >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00' >>> rng = pd.date_range(start, end, freq='7min') >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng) >>> ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7min, dtype: int64 >>> ts.resample('17min').sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17min, dtype: int64 >>> ts.resample('17min', origin='epoch').sum() 2000-10-01 23:18:00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17min, dtype: int64 >>> ts.resample('17min', origin='2000-01-01').sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17min, dtype: int64 If you want to adjust the start of the bins with an `offset` Timedelta, the two following lines are equivalent: >>> ts.resample('17min', origin='start').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64 >>> ts.resample('17min', offset='23h30min').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64 If you want to take the largest Timestamp as the end of the bins: >>> ts.resample('17min', origin='end').sum() 2000-10-01 23:35:00 0 2000-10-01 23:52:00 18 2000-10-02 00:09:00 27 2000-10-02 00:26:00 63 Freq: 17min, dtype: int64 In contrast with the `start_day`, you can use `end_day` to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data: >>> ts.resample('17min', origin='end_day').sum() 2000-10-01 23:38:00 3 2000-10-01 23:55:00 15 2000-10-02 00:12:00 45 2000-10-02 00:29:00 45 Freq: 17min, dtype: int64 r) get_resamplerrz^DataFrame.resample with axis=1 is deprecated. Do `frame.T.resample(...)` without axis instead.rThe 'axis' keyword in z@.resample is deprecated and will be removed in a future version.zThe 'kind' keyword in zv.resample is deprecated and will be removed in a future version. Explicitly cast the index to the desired type insteadNzThe 'convention' keyword in z.resample is deprecated and will be removed in a future version. Explicitly cast PeriodIndex to DatetimeIndex before resampling instead.rQzSeries | DataFrame) rrclosedrr conventionr9r:originoffset group_keys) r"r7rrr!rr rrUr rr ) rrulerr9rr:ronr:r;r<r=r7s rresampleNDFrame.resample$sKp 7 s~~ %((.Dqy D!/1  ,T$Z-@-@,ABJJ!/1 D s~~ % MM(d)<)<(=>HH+-  D S^^ + MM.tDz/B/B.CD+-  !J %t ,!!  rc[R"S[[5S9 [ UR [ 5(d [S5e[UR 5S:XaURSS9$[U5n[ U[5(dDURUR S5(a!UR SUR- U-=p#OUR SU-=p#[ U[5(a8X R ;a)UR RUSS9nURS U$UR S U$) aX Select initial periods of time series data based on a date offset. .. deprecated:: 2.1 :meth:`.first` is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` instead. For a DataFrame with a sorted DatetimeIndex, this function can select the first few rows based on a date offset. Parameters ---------- offset : str, DateOffset or dateutil.relativedelta The offset length of the data that will be selected. For instance, '1ME' will display all the rows having their index within the first month. Returns ------- Series or DataFrame A subset of the caller. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- last : Select final periods of time series based on a date offset. at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the first 3 days: >>> ts.first('3D') A 2018-04-09 1 2018-04-11 2 Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned. zqfirst is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` insteadrz+'first' only supports a DatetimeIndex indexrFrleftrN)rr rrUrrr}rrrrr is_on_offsetbaserrrf)rr<end_dateends rfirst NDFrame.first:&sp   C ')  $**m44IJ J tzz?a 99%9( (6"&$''F,?,? 1 ,N,N"ZZ]V[[86A AHs!ZZ]V3 3H fd # #JJ(>**))()@C99Tc? "xx~rcv[R"S[[5S9 [ UR [ 5(d [S5e[UR 5S:XaURSS9$[U5nUR SU- nUR RUSS 9nURUS $) a Select final periods of time series data based on a date offset. .. deprecated:: 2.1 :meth:`.last` is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` instead. For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset. Parameters ---------- offset : str, DateOffset, dateutil.relativedelta The offset length of the data that will be selected. For instance, '3D' will display all the rows having their index within the last 3 days. Returns ------- Series or DataFrame A subset of the caller. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- first : Select initial periods of time series based on a date offset. at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day. Notes ----- .. deprecated:: 2.1.0 Please create a mask and filter using `.loc` instead Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the last 3 days: >>> ts.last('3D') # doctest: +SKIP A 2018-04-13 3 2018-04-15 4 Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned. zplast is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` insteadrz*'last' only supports a DatetimeIndex indexrFrrrrN) rr rrUrrr}rrrrrr)rr< start_daterQs rr NDFrame.last&sz   C ')  $**m44HI I tzz?a 99%9( (6"ZZ^f,  '' 'Ayy  rc^^^^^^ TRU5m TS;a Sn[U5eUU UUUU4SjnU(aFTRS:Xa%[TR5(d [ S5eTR 5n OTn U"U 5$)aD Compute numerical data ranks (1 through n) along axis. By default, equal values are assigned a rank that is the average of the ranks of those values. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Index to direct ranking. For `Series` this parameter is unused and defaults to 0. method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' How to rank the group of records that have the same value (i.e. ties): * average: average rank of the group * min: lowest rank in the group * max: highest rank in the group * first: ranks assigned in order they appear in the array * dense: like 'min', but rank always increases by 1 between groups. numeric_only : bool, default False For DataFrame objects, rank only numeric columns if set to True. .. versionchanged:: 2.0.0 The default value of ``numeric_only`` is now ``False``. na_option : {'keep', 'top', 'bottom'}, default 'keep' How to rank NaN values: * keep: assign NaN rank to NaN values * top: assign lowest rank to NaN values * bottom: assign highest rank to NaN values ascending : bool, default True Whether or not the elements should be ranked in ascending order. pct : bool, default False Whether or not to display the returned rankings in percentile form. Returns ------- same type as caller Return a Series or DataFrame with data ranks as values. See Also -------- core.groupby.DataFrameGroupBy.rank : Rank of values within each group. core.groupby.SeriesGroupBy.rank : Rank of values within each group. Examples -------- >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog', ... 'spider', 'snake'], ... 'Number_legs': [4, 2, 4, 8, np.nan]}) >>> df Animal Number_legs 0 cat 4.0 1 penguin 2.0 2 dog 4.0 3 spider 8.0 4 snake NaN Ties are assigned the mean of the ranks (by default) for the group. >>> s = pd.Series(range(5), index=list("abcde")) >>> s["d"] = s["b"] >>> s.rank() a 1.0 b 2.5 c 4.0 d 2.5 e 5.0 dtype: float64 The following example shows how the method behaves with the above parameters: * default_rank: this is the default behaviour obtained without using any parameter. * max_rank: setting ``method = 'max'`` the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.) * NA_bottom: choosing ``na_option = 'bottom'``, if there are records with NaN values they are placed at the bottom of the ranking. * pct_rank: when setting ``pct = True``, the ranking is expressed as percentile rank. >>> df['default_rank'] = df['Number_legs'].rank() >>> df['max_rank'] = df['Number_legs'].rank(method='max') >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom') >>> df['pct_rank'] = df['Number_legs'].rank(pct=True) >>> df Animal Number_legs default_rank max_rank NA_bottom pct_rank 0 cat 4.0 2.5 3.0 2.5 0.625 1 penguin 2.0 1.0 1.0 1.0 0.250 2 dog 4.0 2.5 3.0 2.5 0.625 3 spider 8.0 4.0 4.0 4.0 1.000 4 snake NaN NaN NaN 5.0 NaN >topkeepbottomz3na_option must be one of 'keep', 'top', or 'bottom'c <>URS:Xa URnO URn[U[5(aUR TTTTTS9nO[ R"UTTTTTS9nT R"U40UR5D6nURT SS9$)Nr+)rrr na_optionpctrankr~) r,rrrry_rankalgosrTrrr) rrranks ranks_objrr rrRrSrs rrankerNDFrame.rank..rankerP'syyA~&.11 !!'' % !!'' ))%O43L3L3NOI))$v)> >rrzDSeries.rank does not allow numeric_only=True with non-numeric dtype.)r!r r,rerrrU) rrr numeric_onlyrRrrSr(rYrr s ` ` ``` @rrT NDFrame.rank&sZ((. 5 5GCS/ ! ? ?: yyA~&6tzz&B&B)))+DDd|rcomparerc `[U5[U5La>[U5R[U5Rpv[SUSUSUS35eX:HUR5UR5--)nUR SSS9 U(d"UR U5nUR U5nU(d][ U[5(a@UR5n URSS9n URX4nURX4nOXnXn[ U[5(d[S [U5S 35eUS ;aSn OURU5n [X/U US 9n XR:aU $U RU 5n [R "U R"5n[R$"['U55U l[)[+SU R,55S /-n[ U [5(aU R/XS9n OU R/U5n XU RU S9l[R$"U R0U 5R3SU R0U S-/5R4R75nU R9UU S9n U $)Nzcan only compare 'z' (not 'z ') with 'r{TrrrzPassing 'result_names' as a z= is not supported. Provide 'result_names' as a tuple instead.)rr()rr7rr+)r rrrorrrrkrrfr`r!rr,rrharrayr2rPrrirnlevelsreorder_levelsrareshaper=flattenrL)rr align_axis keep_shape keep_equal result_namescls_self cls_otherrcmaskrmaskrdiffrax_namesorderrTs rr]NDFrame.comparez'sy :T%[ ("&t*"5"5tE{7K7Ki$XJhyk8*TUV -DIIK%**,$>?@ D$ '::d#DKK%E$ -- a(xx - %,/z ,...tL/A.BCHH   'D((4D M  99 K ^^D !88BHH%99S]+U1bjj)*aS0 dL ) )&&u&8D&&u-D+3/D!' IIdjj& ' / /DJJt4D4I0J K M M U U W yyty, routerc U[RLd&U[RLdU [RLa9[R"S[ U5R S3[ [5S9 U [RLaSn U[RLaSnU[RLaSnUb [U5nU [RLaS[ U5R S3n U bKURS:XaURS :XaU S - n O%URS :XaURS:XaU S - n [R"U [ [5S9 OSn U S:XaURUR:wa[U[5(aXURn U "URV s0sHoU_M sn 40UR5D6nURUUUUUUUUU S 9 SS $[U[5(aXURn U "URV s0sHoU_M sn 40UR5D6nURUUUUUUUUU S 9 SS $UbUR!U5n[U["5(aURUUUUUUUUU S 9 unnnOH[U[5(aUR%UUUUUUUUU S 9 unnnO['S [ U535e[)[*U5nURS:XdUS:Xa[UR,R.[05(a]UR,R2UR,R2:wa/Ub,UR5SS9nUR5SS9nUUlUUlUR7U5nUR7U5nUU4$s sn fs sn f)a Align two objects on their axes with the specified join method. Join method is specified for each axis Index. Parameters ---------- other : DataFrame or Series join : {{'outer', 'inner', 'left', 'right'}}, default 'outer' Type of alignment to be performed. * left: use only keys from left frame, preserve key order. * right: use only keys from right frame, preserve key order. * outer: use union of keys from both frames, sort keys lexicographically. * inner: use intersection of keys from both frames, preserve the order of the left keys. axis : allowed axis of the other object, default None Align on index (0), columns (1), or both (None). level : int or level name, default None Broadcast across a level, matching Index values on the passed MultiIndex level. copy : bool, default True Always returns new objects. If copy=False and no reindexing is required then original objects are returned. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` fill_value : scalar, default np.nan Value to use for missing values. Defaults to NaN, but can be any "compatible" value. method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None Method to use for filling holes in reindexed Series: - pad / ffill: propagate last valid observation forward to next valid. - backfill / bfill: use NEXT valid observation to fill gap. .. deprecated:: 2.1 limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. .. deprecated:: 2.1 fill_axis : {axes_single_arg}, default 0 Filling axis, method and limit. .. deprecated:: 2.1 broadcast_axis : {axes_single_arg}, default None Broadcast values along this axis, if aligning two objects of different dimensions. .. deprecated:: 2.1 Returns ------- tuple of ({klass}, type of other) Aligned objects. Examples -------- >>> df = pd.DataFrame( ... [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2] ... ) >>> other = pd.DataFrame( ... [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]], ... columns=["A", "B", "C", "D"], ... index=[2, 3, 4], ... ) >>> df D B E A 1 1 2 3 4 2 6 7 8 9 >>> other A B C D 2 10 20 30 40 3 60 70 80 90 4 600 700 800 900 Align on columns: >>> left, right = df.align(other, join="outer", axis=1) >>> left A B C D E 1 4 2 NaN 1 3 2 9 7 NaN 6 8 >>> right A B C D E 2 10 20 30 40 NaN 3 60 70 80 90 NaN 4 600 700 800 900 NaN We can also align on the index: >>> left, right = df.align(other, join="outer", axis=0) >>> left D B E A 1 1.0 2.0 3.0 4.0 2 6.0 7.0 8.0 9.0 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN >>> right A B C D 1 NaN NaN NaN NaN 2 10.0 20.0 30.0 40.0 3 60.0 70.0 80.0 90.0 4 600.0 700.0 800.0 900.0 Finally, the default `axis=None` will align on both index and columns: >>> left, right = df.align(other, join="outer", axis=None) >>> left A B C D E 1 4.0 2.0 NaN 1.0 3.0 2 9.0 7.0 NaN 6.0 8.0 3 NaN NaN NaN NaN NaN 4 NaN NaN NaN NaN NaN >>> right A B C D E 1 NaN NaN NaN NaN NaN 2 10.0 20.0 30.0 40.0 NaN 3 60.0 70.0 80.0 90.0 NaN 4 600.0 700.0 800.0 900.0 NaN z3The 'method', 'limit', and 'fill_axis' keywords in zt.align are deprecated and will be removed in a future version. Call fillna directly on the returned objects instead.rrNz The 'broadcast_axis' keyword in z=.align is deprecated and will be removed in a future version.rr+zz Use left = DataFrame({col: left for col in right.columns}, index=right.index) before calling `left.align(right)` instead.zy Use right = DataFrame({col: right for col in left.columns}, index=left.index) before calling `left.align(right)` instead)rsrr:rrrr fill_axiszunsupported type: Fr)rrrr r rrrUrr,rrl_constructor_expanddimr(r _align_framer!rk _align_seriesrr r2rrritzrr)rrrsrr:rrrrrrbroadcast_axisr(consrrrC_right join_indexrs ralign NDFrame.align's| #.. (CNN*. MME:&&'(+-    &I S^^ #F CNN "E  &v.F  /34:3F3F2GHFF )99>ejjAoYCYY!^ aWC MM#}9I9K L!N Q 499 #:$ **22&+mm4mWm48=8R8R8T )!'' 1  E9--33'+||4|!X|48<8Q8Q8S (()!') 1    ((.D e\ * *'+'8'8%#(9 ( $D&*y ) )'+'9'9%#(: ( $D&*0e >? ?Xv& 99>TQY$****O<<::==EKKNN2!- $yyey4 %  6%/ &0   &""5)U{g5(5s 0 N8 N=c |SupSupSup[U[5nUbUS:XaRURRUR5(d(URR URX$SS9upn UbUS:XaYU(dRUR RUR 5(d(UR R UR X$SS9upnU(aSX/0nOX/X/S.nUR UXVSS9nUR X/X/S.UUSS9nUb URXyUS9nURXyUS9nUUU 4$) NNNrTr r:return_indexersr)rr)rrrrr)rrlrrrsr(rr)rrrsrr:rrrrrrrz join_columnsilidxiridxclidxcridxrrrCrs rrtNDFrame._align_frame(sm$. ! ! tY/ LDAItzz/@/@/M/M'+zz D(7( $Ju \TQYLL'' 66)-):): 4d*;* &L j01J(0l5JKJ** TT+ ,,#(= >! -   ((u(MD**6*OEUJ&&rc  [U[5n U(a[5(aSnU (dUbUS;a [S5eU (aUS:Xa [S5eU(dURR UR5(aSupn O(URR URX$SS9upn U (aURXU5nOJU bU cURUS 9nO4URRXSUS 9nURXRS 9nURXU5nOURnURSn S upU R UR5(dU R URX$SS9upn U b!URS5nURXUS 9nU(aUURLaUR5nURUURS 9nU cURUS 9nOURXS9n[U5=(d USLnU(aO[!Xg5upgUb UR#XxU S9nUR#XxS9nOUR%XhU S9nUR%XhS9nUUU 4$)NF)NrrzMust specify axis=0 or 1rz1cannot align series to a series other than axis 0NNNTrrrlrr~rr)rr)r)rrlrr rrrs_reindex_indexerrrrrrrrrprYrr)rrrsrr:rrrrrrrrzlidxridxrCrrfdatarfill_nas rruNDFrame._align_series')sstY/ '))Ddlt #;d(: !7 !K J!,,Vy,Q..v.C{{:{K Z =UJ&&rc  [US5nUbURU5n[R"X5n[ U[ 5(aUR S:Xa^UR S:XaNUR[[UR55Vs0sHowU_M snSS9nURUl URUSSS9S nOq[US 5(d[R"U5nURUR:wa [!S 5eUR""U40UR%5DS S0D6n['U5n[(R*"5 [(R,"S S[.S9 UR1U5nSSS5 UR3SS9nSn UR4(d[ U[65(d3[9U5(d"[!U R;UR<S95eOUR>H+n [9U 5(aM[!U R;U S95e UR@RB(a3UR""URE[&US940UR%5D6nOURG[&5nU(aU*OUnURIURJURLSS9n[ U[ 5(aUR UR ::aURUSUUSSS9SnUcUROU5(d[PeUR UR :agURRnUS :Xa[RT"US5nOUS:Xa[RT"US5n[RV"X R5nO0[YS5e[ U[Z[ 45(d []USS9n[ U[R^[`45(a[URUR:waUR S:wa [!S5eO%UR""U40UR%5DS S0D6nUcS nUR [cUSS 5:XaSn OURU5S:Hn U(aDUR@ReXXS9n URgXRhS9n URkU 5$UR@RmUUU S9n URgXRhS9n U RoU5$s snf!,(df  GN=f) z~ Equivalent to public method `where`, except that `other` is not applied as a function even if callable. Used in __setitem__. rNrr+Fr\r)rsrrraz,Array conditional must be same shape as selfrrzDowncasting object dtype arrays)categoryz5Boolean array expected for the condition, not {dtype}r)rna_valuerlrC)rsrr:rr)rr)rrz.cannot align with a higher dimensional NDFrameT) extract_numpyz4other must be the same shape as self when an ndarrayr,)rnewr{r r)rrr{)8rXr!rsapply_if_callablerrr,rsrrr(r{rrh asanyarrayrar rrrrcatch_warningsfilterwarningsrrr~rRrkr`rrrNrrMrrrrRrrrMrrb broadcast_torrr{raryr0putmaskrrrrr)rrrrrr:r r7rr(_dtr{rrs r_whereNDFrame._wherew)s&gy9  ((.D''3 dG $ $yyA~$))q.22&+C ,=&>?&>W&>?3 $|| ::du:=a@D4))}}T*zzTZZ' !OPP$$TUT-F-F-HUuUD']  $ $ &  # #1&  ;;z*D '!!u!-EzzdL11$T**$SZZdjjZ%ABB+ ;;C(--(#)>??'9900,, D: F335D ;;t$DuT||DOO$2H2Hu|U eW % %zzTYY& # #<(;(;D(A(A++:: )!MMEqy " 5' : " 5' :OOE::>E*DEJ#899!%t %N "))!668?D <D 99vq1 1E))$/14E yy((dU(VH//}}/MF''/ /yy'H //}}/MF&&t, ,{@' &s T, ,T11 U)rrr:cgrrrrrrrr:s rr NDFrame.where*rr)rr:cgrrrs rrr*rrcgrrrs rrr'*rrTrueFalserr)rrcond_revr8 name_othercZ[US5nU(a[(dL[5(a=[R"U5[ ::a[ R"[[SS9 O[(d[5(dUR5(aj[R"U5n[ n[U[5(a[US5(aUS- nXg::a[ R"[[SS9 [ R""X 5nUR%XX4U5$)a Replace values where the condition is {cond_rev}. Parameters ---------- cond : bool {klass}, array-like, or callable Where `cond` is {cond}, keep the original value. Where {cond_rev}, replace with corresponding value from `other`. If `cond` is callable, it is computed on the {klass} and should return boolean {klass} or array. The callable must not change input {klass} (though pandas doesn't check it). other : scalar, {klass}, or callable Entries where `cond` is {cond_rev} are replaced with corresponding value from `other`. If other is callable, it is computed on the {klass} and should return scalar or {klass}. The callable must not change input {klass} (though pandas doesn't check it). If not specified, entries will be filled with the corresponding NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension dtypes). inplace : bool, default False Whether to perform the operation in place on the data. axis : int, default None Alignment axis if needed. For `Series` this parameter is unused and defaults to 0. level : int, default None Alignment level if needed. Returns ------- Same type as caller or None if ``inplace=True``. See Also -------- :func:`DataFrame.{name_other}` : Return an object of same shape as self. Notes ----- The {name} method is an application of the if-then idiom. For each element in the calling DataFrame, if ``cond`` is ``{cond}`` the element is used; otherwise the corresponding element from the DataFrame ``other`` is used. If the axis of ``other`` does not align with axis of ``cond`` {klass}, the misaligned index positions will be filled with {cond_rev}. The signature for :func:`DataFrame.where` differs from :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to ``np.where(m, df1, df2)``. For further details and examples see the ``{name}`` documentation in :ref:`indexing `. The dtype of the object takes precedence. The fill value is casted to the object's dtype, if this can be done losslessly. Examples -------- >>> s = pd.Series(range(5)) >>> s.where(s > 0) 0 NaN 1 1.0 2 2.0 3 3.0 4 4.0 dtype: float64 >>> s.mask(s > 0) 0 0.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 >>> s = pd.Series(range(5)) >>> t = pd.Series([True, False]) >>> s.where(t, 99) 0 0 1 99 2 99 3 99 4 99 dtype: int64 >>> s.mask(t, 99) 0 99 1 1 2 99 3 99 4 99 dtype: int64 >>> s.where(s > 1, 10) 0 10 1 10 2 2 3 3 4 4 dtype: int64 >>> s.mask(s > 1, 10) 0 0 1 1 2 10 3 10 4 10 dtype: int64 >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B']) >>> df A B 0 0 1 1 2 3 2 4 5 3 6 7 4 8 9 >>> m = df % 3 == 0 >>> df.where(m, -df) A B 0 0 -1 1 -2 3 2 -4 -5 3 6 -7 4 -8 9 >>> df.where(m, -df) == np.where(m, df, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True >>> df.where(m, -df) == df.mask(~m, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True rr+rrr)rXrGrrrrHrr rPrLrWrrlrrQrrsrrrrrrrr:rrs rrr3*st&gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..743K3KNI#MM>%#$ ((5{{4u==rcgrrrs rr NDFrame.mask*rrcgrrrs rrr*rrcgrrrs rrr+rrc[US5nU(a[(dL[5(a=[R"U5[ ::a[ R"[[SS9 O[(d[5(dUR5(aj[R"U5n[ n[U[5(a[US5(aUS- nXg::a[ R"[[SS9 [ R""X5n[ R""X 5n[US5(d[$R&"U5nUR)U)UUUUS9$)Nrr+rrrr)rrrr:)rXrGrrrrHrr rPrLrWrrlrrQrrsrrhr_rrs rrr+s$&gy9 4/11??4(I5MM6.#$ D+--1133ood+% dI..743K3KNI#MM>%#$ ''3((5t\**88D>D{{ E   rc`URU5nUbEU[RLa2[R"S[ [ 5S9 [RnUS:XaURSS9$[U5(a3[U[5(aUR5RXX4S9$[[U5nUcYURU5nUS:XdeURRXS9nUR!XfR"S9R%US S 9$UR'XU5$) aa Shift index by desired number of periods with an optional time `freq`. When `freq` is not passed, shift the index without realigning the data. If `freq` is passed (in this case, the index must be date or datetime, or it will raise a `NotImplementedError`), the index will be increased using the periods and the `freq`. `freq` can be inferred when specified as "infer" as long as either freq or inferred_freq attribute is set in the index. Parameters ---------- periods : int or Sequence Number of periods to shift. Can be positive or negative. If an iterable of ints, the data will be shifted once by each int. This is equivalent to shifting by one value at a time and concatenating all resulting frames. The resulting columns will have the shift suffixed to their column names. For multiple periods, axis must not be 1. freq : DateOffset, tseries.offsets, timedelta, or str, optional Offset to use from the tseries module or time rule (e.g. 'EOM'). If `freq` is specified then the index values are shifted but the data is not realigned. That is, use `freq` if you would like to extend the index when shifting and preserve the original data. If `freq` is specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown. axis : {{0 or 'index', 1 or 'columns', None}}, default None Shift direction. For `Series` this parameter is unused and defaults to 0. fill_value : object, optional The scalar value to use for newly introduced missing values. the default depends on the dtype of `self`. For numeric data, ``np.nan`` is used. For datetime, timedelta, or period data, etc. :attr:`NaT` is used. For extension dtypes, ``self.dtype.na_value`` is used. suffix : str, optional If str and periods is an iterable, this is added after the column name and before the shift value for each shifted column name. Returns ------- {klass} Copy of input object, shifted. See Also -------- Index.shift : Shift values of Index. DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. Examples -------- >>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45], ... "Col2": [13, 23, 18, 33, 48], ... "Col3": [17, 27, 22, 37, 52]}}, ... index=pd.date_range("2020-01-01", "2020-01-05")) >>> df Col1 Col2 Col3 2020-01-01 10 13 17 2020-01-02 20 23 27 2020-01-03 15 18 22 2020-01-04 30 33 37 2020-01-05 45 48 52 >>> df.shift(periods=3) Col1 Col2 Col3 2020-01-01 NaN NaN NaN 2020-01-02 NaN NaN NaN 2020-01-03 NaN NaN NaN 2020-01-04 10.0 13.0 17.0 2020-01-05 20.0 23.0 27.0 >>> df.shift(periods=1, axis="columns") Col1 Col2 Col3 2020-01-01 NaN 10 13 2020-01-02 NaN 20 23 2020-01-03 NaN 15 18 2020-01-04 NaN 30 33 2020-01-05 NaN 45 48 >>> df.shift(periods=3, fill_value=0) Col1 Col2 Col3 2020-01-01 0 0 0 2020-01-02 0 0 0 2020-01-03 0 0 0 2020-01-04 10 13 17 2020-01-05 20 23 27 >>> df.shift(periods=3, freq="D") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52 >>> df.shift(periods=3, freq="infer") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52 >>> df['Col1'].shift(periods=[0, 1, 2]) Col1_0 Col1_1 Col1_2 2020-01-01 10 NaN NaN 2020-01-02 20 10.0 NaN 2020-01-03 15 20.0 10.0 2020-01-04 30 15.0 20.0 2020-01-05 45 30.0 15.0 NzPassing a 'freq' together with a 'fill_value' silently ignores the fill_value and is deprecated. This will raise in a future version.rrr)periodsrrr)rrrshiftr~)r!rrrr rrUrrcrrlrrr rrrrr_shift_with_freq)rrrrrrrs rr NDFrame.shiftI+s$r$$T*   #.. @ MM+-  J a<99$9' '  Zi%@%@==?(() sG$ <((.D19 9yywNH--}}.l4l0 1$$WD99rc nURU5nUS:Xa.[USS5nUc [USS5nUc Sn[U5eO.[U[5(a[U[ 5n[ X6S9n[U[ 5(a~[ UR5nX7:waRUce[S[URUR5S[URUR535eURU5nOURX5nURXS9n U RUS S 9$) Nrr inferred_freqz6Freq was not set in the index hence cannot be inferred) is_periodz Given freq z! does not match PeriodIndex freq rrr~)rr0r rrrrrrrr8rrpr) rrrrrr(r orig_freqrgrs rrNDFrame._shift_with_freq+s/t$ 7?5&$/D|uot<|N o%c " ""5+6IT7D e[ ) )!%**-I  ,,, !"8"K!LM7-ikk9>>JKM [[)F[[/Fv1""4"88rcUcSnURU5nURU5nUR(dUR(d [ S5eUR (aSSKJn U"U5nU"U5nUbUbX:a[ SUSU35e[U5S:a'UR(aUR5S:aX!p![SS5/UR-n[X5Xs'UR[U5n[U[5(a*[!XR#U5UR%X55 UR'U=(a [)5(+S9nU$) a Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Parameters ---------- before : date, str, int Truncate all rows before this index value. after : date, str, int Truncate all rows after this index value. axis : {0 or 'index', 1 or 'columns'}, optional Axis to truncate. Truncates the index (rows) by default. For `Series` this parameter is unused and defaults to 0. copy : bool, default is True, Return a copy of the truncated section. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` Returns ------- type of caller The truncated Series or DataFrame. See Also -------- DataFrame.loc : Select a subset of a DataFrame by label. DataFrame.iloc : Select a subset of a DataFrame by position. Notes ----- If the index being truncated contains only datetime values, `before` and `after` may be specified as strings instead of Timestamps. Examples -------- >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'], ... 'B': ['f', 'g', 'h', 'i', 'j'], ... 'C': ['k', 'l', 'm', 'n', 'o']}, ... index=[1, 2, 3, 4, 5]) >>> df A B C 1 a f k 2 b g l 3 c h m 4 d i n 5 e j o >>> df.truncate(before=2, after=4) A B C 2 b g l 3 c h m 4 d i n The columns of a DataFrame can be truncated. >>> df.truncate(before="A", after="B", axis="columns") A B 1 a f 2 b g 3 c h 4 d i 5 e j For Series, only rows can be truncated. >>> df['A'].truncate(before=2, after=4) 2 b 3 c 4 d Name: A, dtype: object The index values in ``truncate`` can be datetimes or string dates. >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s') >>> df = pd.DataFrame(index=dates, data={'A': 1}) >>> df.tail() A 2016-01-31 23:59:56 1 2016-01-31 23:59:57 1 2016-01-31 23:59:58 1 2016-01-31 23:59:59 1 2016-02-01 00:00:00 1 >>> df.truncate(before=pd.Timestamp('2016-01-05'), ... after=pd.Timestamp('2016-01-10')).tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1 Because the index is a DatetimeIndex containing only dates, we can specify `before` and `after` as strings. They will be coerced to Timestamps before truncation. >>> df.truncate('2016-01-05', '2016-01-10').tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1 Note that ``truncate`` assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates. >>> df.loc['2016-01-05':'2016-01-10', :].tail() A 2016-01-10 23:59:55 1 2016-01-10 23:59:56 1 2016-01-10 23:59:57 1 2016-01-10 23:59:58 1 2016-01-10 23:59:59 1 Nrz truncate requires a sorted index) to_datetimez Truncate: z must be after rr)r!rris_monotonic_decreasingr  _is_all_datespandas.core.tools.datetimesrrrNrrrfr`rrrtr%truncaterr) rbeforeafterrrrrslicerrs rrNDFrame.truncate,s<T <D$$T* ^^D !))"2L2L?@ @    ? (F&E  %"3z%xHI I r7Q;255"**,:J!Ed#$t~~5V+ %-( b* % % F//5r{{67Q R$"D/B/D+DE rc^^TRT5mTRT5nUU4Sjn[U[5(a7UR U5nU"UR UU5nUR XsS9nO*USSUR4;a[SUS35eU"XQ5nTRU=(a [5(+S9nURUTSS 9nURTS S 9$) aE Convert tz-aware axis to target time zone. Parameters ---------- tz : str or tzinfo object or None Target time zone. Passing ``None`` will convert to UTC and remove the timezone information. axis : {{0 or 'index', 1 or 'columns'}}, default 0 The axis to convert level : int, str, default None If axis is a MultiIndex, convert a specific level. Otherwise must be None. copy : bool, default True Also make a copy of the underlying data. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` Returns ------- {klass} Object with time zone converted axis. Raises ------ TypeError If the axis is tz-naive. Examples -------- Change to another time zone: >>> s = pd.Series( ... [1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']), ... ) >>> s.tz_convert('Asia/Shanghai') 2018-09-15 07:30:00+08:00 1 dtype: int64 Pass None to convert to UTC and get a tz-naive index: >>> s = pd.Series([1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00'])) >>> s.tz_convert(None) 2018-09-14 23:30:00 1 dtype: int64 c>[US5(d:[U5S:aTRT5n[US35e[ /US9nU$UR U5nU$)N tz_convertr, is not a valid DatetimeIndex or PeriodIndexrv)rrr%rr}r)rrvax_namerrs r _tz_convert'NDFrame.tz_convert.._tz_convert,sm2|,,r7Q;"11$7G#")#OP#2"-I]]2&IrrNr The level is not validrFrlrr~) r!rrrrlevels set_levelsr8r rrrpr) rrvrr:rrr new_levelrs ` ` rrNDFrame.tz_convert,s~$$T* ^^D !  b* % %((/E#BIIe$4b9Iy6BT1bgg.. :eWM!BCCR$B B-@-B)BC$U;""4 "==rc6^^SnXg;a*[U[R5(d [S5eTR T5mTR T5nUU4Sjn [U[ 5(a8URU5nU "URUXU5n URXS9nO+USSUR4;a[SUS35eU "XXV5nTRU=(a [5(+S 9n U RUTS S 9n U RTS S 9$)a Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use :meth:`Series.dt.tz_localize`. Parameters ---------- tz : str or tzinfo or None Time zone to localize. Passing ``None`` will remove the time zone information and preserve local time. axis : {{0 or 'index', 1 or 'columns'}}, default 0 The axis to localize level : int, str, default None If axis ia a MultiIndex, localize a specific level. Otherwise must be None. copy : bool, default True Also make a copy of the underlying data. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : str, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are: - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- {klass} Same type as the input. Raises ------ TypeError If the TimeSeries is tz-aware and tz is not None. Examples -------- Localize local times: >>> s = pd.Series( ... [1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00']), ... ) >>> s.tz_localize('CET') 2018-09-15 01:30:00+02:00 1 dtype: int64 Pass None to convert to tz-naive index and preserve local time: >>> s = pd.Series([1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00'])) >>> s.tz_localize(None) 2018-09-15 01:30:00 1 dtype: int64 Be careful with DST changes. When there is sequential data, pandas can infer the DST time: >>> s = pd.Series(range(7), ... index=pd.DatetimeIndex(['2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.tz_localize('CET', ambiguous='infer') 2018-10-28 01:30:00+02:00 0 2018-10-28 02:00:00+02:00 1 2018-10-28 02:30:00+02:00 2 2018-10-28 02:00:00+01:00 3 2018-10-28 02:30:00+01:00 4 2018-10-28 03:00:00+01:00 5 2018-10-28 03:30:00+01:00 6 dtype: int64 In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly >>> s = pd.Series(range(3), ... index=pd.DatetimeIndex(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.tz_localize('CET', ambiguous=np.array([True, True, False])) 2018-10-28 01:20:00+02:00 0 2018-10-28 02:36:00+02:00 1 2018-10-28 03:46:00+01:00 2 dtype: int64 If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or `'shift_forward'` or `'shift_backward'`. >>> s = pd.Series(range(2), ... index=pd.DatetimeIndex(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 2015-03-29 03:00:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 2015-03-29 01:59:59.999999999+01:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h')) 2015-03-29 03:30:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 )rNaT shift_forwardshift_backwardzoThe nonexistent argument must be one of 'raise', 'NaT', 'shift_forward', 'shift_backward' or a timedelta objectc>[US5(d:[U5S:aTRT5n[US35e[ /US9nU$UR XUS9nU$)N tz_localizerrr) ambiguous nonexistent)rrr%rr}r)rrvrrrrrs r _tz_localize)NDFrame.tz_localize.._tz_localize-sq2}--r7Q;"11$7G#")#OP#2"-I^^B^UIrrNrrrrFrlrr~)rdt timedeltar r!rrrrrr8rrrpr) rrvrr:rrrnonexistent_optionsrrrrs ` ` rrNDFrame.tz_localize-s tR  1* ; ; %  $$T* ^^D !  b* % %((/E$RYYu%5rkRIy6BT1bgg.. :eWM!BCCbi=B B-@-B)BC$U;""4 ">>rc6[UUUUS9RUSS9$)ac Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. Analyzes both numeric and object series, as well as ``DataFrame`` column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Parameters ---------- percentiles : list-like of numbers, optional The percentiles to include in the output. All should fall between 0 and 1. The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and 75th percentiles. include : 'all', list-like of dtypes or None (default), optional A white list of data types to include in the result. Ignored for ``Series``. Here are the options: - 'all' : All columns of the input will be included in the output. - A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit ``numpy.number``. To limit it instead to object columns submit the ``numpy.object`` data type. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To select pandas categorical columns, use ``'category'`` - None (default) : The result will include all numeric columns. exclude : list-like of dtypes or None (default), optional, A black list of data types to omit from the result. Ignored for ``Series``. Here are the options: - A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit ``numpy.number``. To exclude object columns submit the data type ``numpy.object``. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(exclude=['O'])``). To exclude pandas categorical columns, use ``'category'`` - None (default) : The result will exclude nothing. Returns ------- Series or DataFrame Summary statistics of the Series or Dataframe provided. See Also -------- DataFrame.count: Count number of non-NA/null observations. DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype. Notes ----- For numeric data, the result's index will include ``count``, ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and upper percentiles. By default the lower percentile is ``25`` and the upper percentile is ``75``. The ``50`` percentile is the same as the median. For object data (e.g. strings or timestamps), the result's index will include ``count``, ``unique``, ``top``, and ``freq``. The ``top`` is the most common value. The ``freq`` is the most common value's frequency. Timestamps also include the ``first`` and ``last`` items. If multiple object values have the highest count, then the ``count`` and ``top`` results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a ``DataFrame``, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If ``include='all'`` is provided as an option, the result will include a union of attributes of each type. The `include` and `exclude` parameters can be used to limit which columns in a ``DataFrame`` are analyzed for the output. The parameters are ignored when analyzing a ``Series``. Examples -------- Describing a numeric ``Series``. >>> s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64 Describing a categorical ``Series``. >>> s = pd.Series(['a', 'a', 'b', 'c']) >>> s.describe() count 4 unique 3 top a freq 2 dtype: object Describing a timestamp ``Series``. >>> s = pd.Series([ ... np.datetime64("2000-01-01"), ... np.datetime64("2010-01-01"), ... np.datetime64("2010-01-01") ... ]) >>> s.describe() count 3 mean 2006-09-01 08:00:00 min 2000-01-01 00:00:00 25% 2004-12-31 12:00:00 50% 2010-01-01 00:00:00 75% 2010-01-01 00:00:00 max 2010-01-01 00:00:00 dtype: object Describing a ``DataFrame``. By default only numeric fields are returned. >>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']), ... 'numeric': [1, 2, 3], ... 'object': ['a', 'b', 'c'] ... }) >>> df.describe() numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Describing all columns of a ``DataFrame`` regardless of data type. >>> df.describe(include='all') # doctest: +SKIP categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN a freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 3.0 NaN Describing a column from a ``DataFrame`` by accessing it as an attribute. >>> df.numeric.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Name: numeric, dtype: float64 Including only numeric columns in a ``DataFrame`` description. >>> df.describe(include=[np.number]) numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Including only string columns in a ``DataFrame`` description. >>> df.describe(include=[object]) # doctest: +SKIP object count 3 unique 3 top a freq 1 Including only categorical columns from a ``DataFrame`` description. >>> df.describe(include=['category']) categorical count 3 unique 3 top d freq 1 Excluding numeric columns from a ``DataFrame`` description. >>> df.describe(exclude=[np.number]) # doctest: +SKIP categorical object count 3 3 unique 3 3 top f a freq 1 1 Excluding object columns from a ``DataFrame`` description. >>> df.describe(exclude=[object]) # doctest: +SKIP categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0 )rrexclude percentilesdescriber~)rr)rrrrs rrNDFrame.describe-s.d #  ,tJ, /  0rc U[RS4;dU[RLa9[R"S[ U5R S3[ [5S9 U[RLaU[RLaURS:XaUR5OSU4/nUHupx[U5S:dMUR5Rn U [R"U )5Sn U R5(dMa[R"S[ U5R S3[ [5S9 O S nU[RLaSnUR!UR#S S 55n UcUn OUR%X*US 9n U R&"SXU S .UD6n X- S- n Ub9U R(U R*R-5)n U R/U 5n U R1USS9$)a Fractional change between the current and a prior element. Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements. .. note:: Despite the name of this method, it calculates fractional change (also known as per unit change or relative change) and not percentage change. If you need the percentage change, multiply these values by 100. Parameters ---------- periods : int, default 1 Periods to shift for forming percent change. fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad' How to handle NAs **before** computing percent changes. .. deprecated:: 2.1 All options of `fill_method` are deprecated except `fill_method=None`. limit : int, default None The number of consecutive NAs to fill before stopping. .. deprecated:: 2.1 freq : DateOffset, timedelta, or str, optional Increment to use from time series API (e.g. 'ME' or BDay()). **kwargs Additional keyword arguments are passed into `DataFrame.shift` or `Series.shift`. Returns ------- Series or DataFrame The same type as the calling object. See Also -------- Series.diff : Compute the difference of two elements in a Series. DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods. Examples -------- **Series** >>> s = pd.Series([90, 91, 85]) >>> s 0 90 1 91 2 85 dtype: int64 >>> s.pct_change() 0 NaN 1 0.011111 2 -0.065934 dtype: float64 >>> s.pct_change(periods=2) 0 NaN 1 NaN 2 -0.055556 dtype: float64 See the percentage change in a Series where filling NAs with last valid observation forward to next valid. >>> s = pd.Series([90, 91, None, 85]) >>> s 0 90.0 1 91.0 2 NaN 3 85.0 dtype: float64 >>> s.ffill().pct_change() 0 NaN 1 0.011111 2 0.000000 3 -0.065934 dtype: float64 **DataFrame** Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01. >>> df = pd.DataFrame({ ... 'FR': [4.0405, 4.0963, 4.3149], ... 'GR': [1.7246, 1.7482, 1.8519], ... 'IT': [804.74, 810.01, 860.13]}, ... index=['1980-01-01', '1980-02-01', '1980-03-01']) >>> df FR GR IT 1980-01-01 4.0405 1.7246 804.74 1980-02-01 4.0963 1.7482 810.01 1980-03-01 4.3149 1.8519 860.13 >>> df.pct_change() FR GR IT 1980-01-01 NaN NaN NaN 1980-02-01 0.013810 0.013684 0.006549 1980-03-01 0.053365 0.059318 0.061876 Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns. >>> df = pd.DataFrame({ ... '2016': [1769950, 30586265], ... '2015': [1500923, 40912316], ... '2014': [1371819, 41403351]}, ... index=['GOOG', 'APPL']) >>> df 2016 2015 2014 GOOG 1769950 1500923 1371819 APPL 30586265 40912316 41403351 >>> df.pct_change(axis='columns', periods=-1) 2016 2015 2014 GOOG 0.179241 0.094112 NaN APPL -0.252395 -0.011860 NaN NzDThe 'fill_method' keyword being not None and the 'limit' keyword in z.pct_change are deprecated and will be removed in a future version. 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M!^ EN$6< ZZCZ Z 4 Z  ZZ Z Zx i (:7(CD""!2 LL L  L  LEL\  )*+<=" ""&*7:~~,/NN*-..69nn___ _  _  _$_5_*_(_4_ _  _B  " " 5'5'5' 5'  5'5'5' .5' 5'n " " M'M'M' M'  M'M'M' +M' M'^ nn U- U-  U-U- U-n  #&                                              )   ffm>   "m>  m>  m>m> m> m>^  #&                                               )   nn0   "0  0  0 0  0  0 d !'*+() "~~! W:$W: W:  W:  W: W:,W:r !9 !9F  " j j  j  j jX !'*+DHY>Y>4AY> Y>, Y>v !'*+"#*'.~?~?  ~? ! ~?%~? ~?, ~?F  v0  v0 v0p  > ? ? " "& '!3G!<=H3> H3T  V3Eg3NO2P 2rrac {desc} Parameters ---------- axis : {axis_descr} Axis for the function to be applied on. For `Series` this parameter is unused and defaults to 0. For DataFrames, specifying ``axis=None`` will apply the aggregation across both axes. .. versionadded:: 2.0.0 skipna : bool, default True Exclude NA/null values when computing the result. numeric_only : bool, default False Include only float, int, boolean columns. Not implemented for Series. {min_count}**kwargs Additional keyword arguments to be passed to the function. Returns ------- {name1} or scalar{see_also}{examples} a {desc} Parameters ---------- axis : {axis_descr} Axis for the function to be applied on. For `Series` this parameter is unused and defaults to 0. .. warning:: The behavior of DataFrame.{name} with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis). .. versionadded:: 2.0.0 skipna : bool, default True Exclude NA/null values when computing the result. numeric_only : bool, default False Include only float, int, boolean columns. Not implemented for Series. {min_count}**kwargs Additional keyword arguments to be passed to the function. Returns ------- {name1} or scalar{see_also}{examples} a! {desc} Parameters ---------- axis : {axis_descr} For `Series` this parameter is unused and defaults to 0. .. warning:: The behavior of DataFrame.{name} with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis). skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. numeric_only : bool, default False Include only float, int, boolean columns. Not implemented for Series. Returns ------- {name1} or {name2} (if level specified) {notes}{examples} zg Notes ----- To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the default `ddof=1`)ay Examples -------- >>> df = pd.DataFrame({'person_id': [0, 1, 2, 3], ... 'age': [21, 25, 62, 43], ... 'height': [1.61, 1.87, 1.49, 2.01]} ... ).set_index('person_id') >>> df age height person_id 0 21 1.61 1 25 1.87 2 62 1.49 3 43 2.01 The standard deviation of the columns can be found as follows: >>> df.std() age 18.786076 height 0.237417 dtype: float64 Alternatively, `ddof=0` can be set to normalize by N instead of N-1: >>> df.std(ddof=0) age 16.269219 height 0.205609 dtype: float64a? Examples -------- >>> df = pd.DataFrame({'person_id': [0, 1, 2, 3], ... 'age': [21, 25, 62, 43], ... 'height': [1.61, 1.87, 1.49, 2.01]} ... ).set_index('person_id') >>> df age height person_id 0 21 1.61 1 25 1.87 2 62 1.49 3 43 2.01 >>> df.var() age 352.916667 height 0.056367 dtype: float64 Alternatively, ``ddof=0`` can be set to normalize by N instead of N-1: >>> df.var(ddof=0) age 264.687500 height 0.042275 dtype: float64aA {desc} Parameters ---------- axis : {{0 or 'index', 1 or 'columns', None}}, default 0 Indicate which axis or axes should be reduced. For `Series` this parameter is unused and defaults to 0. * 0 / 'index' : reduce the index, return a Series whose index is the original column labels. * 1 / 'columns' : reduce the columns, return a Series whose index is the original index. * None : reduce all axes, return a scalar. bool_only : bool, default False Include only boolean columns. Not implemented for Series. skipna : bool, default True Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be {empty_value}, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero. **kwargs : any, default None Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- {name1} or {name2} If level is specified, then, {name2} is returned; otherwise, {name1} is returned. {see_also} {examples}zReturn whether all elements are True, potentially over an axis. Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).aExamples -------- **Series** >>> pd.Series([True, True]).all() True >>> pd.Series([True, False]).all() False >>> pd.Series([], dtype="float64").all() True >>> pd.Series([np.nan]).all() True >>> pd.Series([np.nan]).all(skipna=False) True **DataFrames** Create a dataframe from a dictionary. >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) >>> df col1 col2 0 True True 1 True False Default behaviour checks if values in each column all return True. >>> df.all() col1 True col2 False dtype: bool Specify ``axis='columns'`` to check if values in each row all return True. >>> df.all(axis='columns') 0 True 1 False dtype: bool Or ``axis=None`` for whether every value is True. >>> df.all(axis=None) False zSee Also -------- Series.all : Return True if all elements are True. DataFrame.any : Return True if one (or more) elements are True. aZ Return cumulative {desc} over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative {desc}. Parameters ---------- axis : {{0 or 'index', 1 or 'columns'}}, default 0 The index or the name of the axis. 0 is equivalent to None or 'index'. For `Series` this parameter is unused and defaults to 0. skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- {name1} or {name2} Return cumulative {desc} of {name1} or {name2}. See Also -------- core.window.expanding.Expanding.{accum_func_name} : Similar functionality but ignores ``NaN`` values. {name2}.{accum_func_name} : Return the {desc} over {name2} axis. {name2}.cummax : Return cumulative maximum over {name2} axis. {name2}.cummin : Return cumulative minimum over {name2} axis. {name2}.cumsum : Return cumulative sum over {name2} axis. {name2}.cumprod : Return cumulative product over {name2} axis. {examples}aExamples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummin() 0 2.0 1 NaN 2 2.0 3 -1.0 4 -1.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummin(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the minimum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0 To iterate over columns and find the minimum in each row, use ``axis=1`` >>> df.cummin(axis=1) A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 aExamples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumsum() 0 2.0 1 NaN 2 7.0 3 6.0 4 6.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumsum(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the sum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumsum() A B 0 2.0 1.0 1 5.0 NaN 2 6.0 1.0 To iterate over columns and find the sum in each row, use ``axis=1`` >>> df.cumsum(axis=1) A B 0 2.0 3.0 1 3.0 NaN 2 1.0 1.0 aExamples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumprod(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the product in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0 To iterate over columns and find the product in each row, use ``axis=1`` >>> df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0 aExamples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummax() 0 2.0 1 NaN 2 5.0 3 5.0 4 5.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummax(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the maximum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummax() A B 0 2.0 1.0 1 3.0 NaN 2 3.0 1.0 To iterate over columns and find the maximum in each row, use ``axis=1`` >>> df.cummax(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 1.0 a2See Also -------- numpy.any : Numpy version of this method. Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis. zReturn whether any element is True, potentially over an axis. Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).a\Examples -------- **Series** For Series input, the output is a scalar indicating whether any element is True. >>> pd.Series([False, False]).any() False >>> pd.Series([True, False]).any() True >>> pd.Series([], dtype="float64").any() False >>> pd.Series([np.nan]).any() False >>> pd.Series([np.nan]).any(skipna=False) True **DataFrame** Whether each column contains at least one True element (the default). >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) >>> df A B C 0 1 0 0 1 2 2 0 >>> df.any() A True B True C False dtype: bool Aggregating over the columns. >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]}) >>> df A B 0 True 1 1 False 2 >>> df.any(axis='columns') 0 True 1 True dtype: bool >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]}) >>> df A B 0 True 1 1 False 0 >>> df.any(axis='columns') 0 True 1 False dtype: bool Aggregating over the entire DataFrame with ``axis=None``. >>> df.any(axis=None) True `any` for an empty DataFrame is an empty Series. >>> pd.DataFrame([]).any() Series([], dtype: bool) a Examples -------- >>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 >>> s.{stat_func}() {default_output}stat_func_examplerQSum) stat_funcverbdefault_outputlevel_output_0level_output_1a By default, the sum of an empty or all-NA Series is ``0``. >>> pd.Series([], dtype="float64").sum() # min_count=0 is the default 0.0 This can be controlled with the ``min_count`` parameter. For example, if you'd like the sum of an empty series to be NaN, pass ``min_count=1``. >>> pd.Series([], dtype="float64").sum(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).sum() 0.0 >>> pd.Series([np.nan]).sum(min_count=1) nanrMaxr _max_examplesrMinr+ _min_examplesa See Also -------- Series.sum : Return the sum. Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.a Examples -------- By default, the product of an empty or all-NA Series is ``1`` >>> pd.Series([], dtype="float64").prod() 1.0 This can be controlled with the ``min_count`` parameter >>> pd.Series([], dtype="float64").prod(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).prod() 1.0 >>> pd.Series([np.nan]).prod(min_count=1) nanzmin_count : int, default 0 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. c @US:XaSnSnSnOSnSnSnUS:Xa[n[n[n[nSS 0n GOUS :Xa[n[n[ n[ nSS 0n GOUS :Xa[nS n[n[nSS0n GOUS:Xa[nSn[n[nSS0n GOhUS:Xa[nSn[n[nS[0n GODUS:Xa[nSn[n[nS[0n GO US:Xa[nSnSnSnSS0n GOUS:Xa[nSnSnSnSS0n OUS:Xa[nSn[ nSnSS0n OUS:Xa[nS n["nSnS[$0n OUS!:Xa[nS"nS#nSnSS0n OUS$:Xa[nS%nSnS&nSS0n OUS':Xa[nS(nSnS)nSS0n OrUS*:Xa[&nSnSn[(nS+S0n OWUS,:Xa[&nS-nSn[*nS+S0n O>> s = pd.Series([1, 2, 3]) >>> s.median() 2.0 With a DataFrame >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra']) >>> df a b tiger 1 2 zebra 2 3 >>> df.median() a 1.5 b 2.5 dtype: float64 Using axis=1 >>> df.median(axis=1) tiger 1.5 zebra 2.5 dtype: float64 In this case, `numeric_only` should be set to `True` to avoid getting an error. >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']}, ... index=['tiger', 'zebra']) >>> df.median(numeric_only=True) a 1.5 dtype: float64r5z6Return the mean of the values over the requested axis.aw Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.mean() 2.0 With a DataFrame >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra']) >>> df a b tiger 1 2 zebra 2 3 >>> df.mean() a 1.5 b 2.5 dtype: float64 Using axis=1 >>> df.mean(axis=1) tiger 1.5 zebra 2.5 dtype: float64 In this case, `numeric_only` should be set to `True` to avoid getting an error. >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']}, ... index=['tiger', 'zebra']) >>> df.mean(numeric_only=True) a 1.5 dtype: float64r,zyReturn unbiased variance over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument.notesr0zReturn sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument.r&zReturn unbiased standard error of the mean over requested axis. Normalized by N-1 by default. This can be changed using the ddof argumenta Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.sem().round(6) 0.57735 With a DataFrame >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra']) >>> df a b tiger 1 2 zebra 2 3 >>> df.sem() a 0.5 b 0.5 dtype: float64 Using axis=1 >>> df.sem(axis=1) tiger 0.5 zebra 0.5 dtype: float64 In this case, `numeric_only` should be set to `True` to avoid getting an error. >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']}, ... index=['tiger', 'zebra']) >>> df.sem(numeric_only=True) a 0.5 dtype: float64r7z=Return unbiased skew over requested axis. Normalized by N-1.a- Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.skew() 0.0 With a DataFrame >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]}, ... index=['tiger', 'zebra', 'cow']) >>> df a b c tiger 1 2 1 zebra 2 3 3 cow 3 4 5 >>> df.skew() a 0.0 b 0.0 c 0.0 dtype: float64 Using axis=1 >>> df.skew(axis=1) tiger 1.732051 zebra -1.732051 cow 0.000000 dtype: float64 In this case, `numeric_only` should be set to `True` to avoid getting an error. >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']}, ... index=['tiger', 'zebra', 'cow']) >>> df.skew(numeric_only=True) a 0.0 dtype: float64r6zReturn unbiased kurtosis over requested axis. Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.a6 Examples -------- >>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse']) >>> s cat 1 dog 2 dog 2 mouse 3 dtype: int64 >>> s.kurt() 1.5 With a DataFrame >>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]}, ... index=['cat', 'dog', 'dog', 'mouse']) >>> df a b cat 1 3 dog 2 4 dog 2 4 mouse 3 4 >>> df.kurt() a 1.5 b 4.0 dtype: float64 With axis=None >>> df.kurt(axis=None).round(6) -0.988693 Using axis=1 >>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]}, ... index=['cat', 'dog']) >>> df.kurt(axis=1) cat -6.0 dog -6.0 dtype: float64raccum_func_namerrrrr r)descr8name1name2 axis_descrsee_alsoexamplesr) _bool_doc _any_desc _any_see_also _any_examples _all_desc _all_see_also _all_examples_num_doc_stat_func_see_alsorr _sum_prod_doc _sum_examples_min_count_stub_prod_examples _num_ddof_doc _var_examples _std_examples _std_notes _cnum_doc_cumsum_examples_cumprod_examples_cummin_examples_cummax_examplesrr) r8r,rrrbase_docrrrrdocstrs rmake_docr,+5s- qy" /  u}  )   (  I ' r"  I ' r"   > ' /  J&!/  I"Fr" G"Fr"   K !2    !:&   & "F2 P&Nr"   )Tr"  ##U+  $#V,  ##U+  ##U+"! __      F Mr)r8rr,rrr) __future__rrrrdatetimer functoolsrrrrrrrrtypingrrr r r r r rrrr{numpyrhpandas._configrrr pandas._libsrpandas._libs.librpandas._libs.tslibsrrrrpandas._libs.tslibs.dtypesrpandas._typingrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErF pandas.compatrGpandas.compat._constantsrHpandas.compat._optionalrIpandas.compat.numpyrJrM pandas.errorsrKrLrMrNrOrPrQrRpandas.util._decoratorsrSrTpandas.util._exceptionsrUpandas.util._validatorsrVrWrXrYrZpandas.core.dtypes.astyper[pandas.core.dtypes.commonr\r]r^r_r`rarbrcrdrerfrgrhpandas.core.dtypes.dtypesrirjpandas.core.dtypes.genericrkrlpandas.core.dtypes.inferencermrnpandas.core.dtypes.missingrorp pandas.corerqrVrrrsrtrurvrwpandas.core.array_algos.replacerxpandas.core.arraysrypandas.core.baserzpandas.core.constructionr{pandas.core.flagsr|pandas.core.indexes.apir}r~rrrrrpandas.core.internalsrrr"pandas.core.internals.constructionrrpandas.core.methods.describerpandas.core.missingrrrpandas.core.reshape.concatrpandas.core.shared_docsrpandas.core.sortingrpandas.core.windowrrrrpandas.io.formats.formatrrpandas.io.formats.printingrcollections.abcrrrrrrgrrrrpandas.core.indexers.objectsrr"rrrr IndexingMixinrrrr!r$r#r"rrrrr%r(r&r'r)rrrrrrrrrr rr,rrrrZs*"      - >............^.>.   55 =-)2# : .034/ 9. ,  `A0  SE2lH22SE2lJ < B <  < 8!  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