MhTSrSSKJr SSKrSSKJr SSKJrJrJrJ r J r SSK r SSK J r SSKJr SSKrSSKrSSKrSSKJr SS KJrJrJrJrJrJr SSKrSSKrSS KJr SS K J!r!J"r"J#r# SS K$J%r% SS K&J'r(J)r)J*r* SSK+J,r, SSK-J.r. SSK/J0r0 SSK1J2r2 SSK3J4r4 SSK5J6r7 SSK8J9r9J:r:J;r;Jr> SSK?J@r@JArAJBrBJCrC SSKDJErEJFrF SSKGJHrHJIrIJJrJ SSKKJLrLJMrMJNrNJOrOJPrPJQrQJRrRJSrSJTrT SSKUJVrVJWrWJXrXJYrYJZrZJ[r[J\r\J]r]J^r^J_r_J`r`JaraJbrbJcrcJdrdJereJfrf SSKgJhrh SSKiJjrjJkrkJlrl SSKmJnrnJoro SSKpJqrqJrrsJtrtJuruJvrv SSKwJxrx SSKyJzrz SS K{J|r| SS!K}J~r~ SS"KJrJrJrJrJr SS#KJr SS$KJrJrJr SS%KJrJr SS&KJr SS'KJrJrJrJrJrJr SS(KJrJr SS)KJrJr SS*KJrJr SS+KJrJrJrJrJrJrJrJrJrJr SS,KJr SS-KJr SS.KJr SS/KJr SS0KJrJrJr SS1KJr SS2KJrJr SS3KJrJrJr SSKr\(aSSKrSS4KJr SS5KJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJr SS6KJr SS7KJr SS8KJr SS9KJr S:S;SS?S@SA.rSBGr"SCS;\\~5rSFSDjGrSGSEjGrg)Ha# DataFrame --------- An efficient 2D container for potentially mixed-type time series or other labeled data series. Similar to its R counterpart, data.frame, except providing automatic data alignment and a host of useful data manipulation methods having to do with the labeling information ) annotationsN)abc)HashableIterableIteratorMappingSequence) signature)StringIO)dedent) TYPE_CHECKINGAnyCallableLiteralcastoverload)ma) get_optionusing_copy_on_writewarn_copy_on_write) _get_option)algoslib properties) duplicated)is_range_indexer)PYPY) REF_COUNT)import_optional_dependency)function)ChainedAssignmentErrorInvalidIndexError_chained_assignment_method_msg_chained_assignment_msg&_chained_assignment_warning_method_msg_chained_assignment_warning_msg)Appender Substitutiondeprecate_nonkeyword_argumentsdoc)find_stack_levelrewrite_warning)validate_ascendingvalidate_bool_kwargvalidate_percentile) LossySetitemErrorcan_hold_element"construct_1d_arraylike_from_scalar"construct_2d_arraylike_from_scalarfind_common_typeinfer_dtype_from_scalarinvalidate_string_dtypesmaybe_box_nativemaybe_downcast_to_dtype)infer_dtype_from_objectis_1d_only_ea_dtype is_array_like is_bool_dtype is_dataclass is_dict_likeis_floatis_float_dtype is_hashable is_integeris_integer_dtype is_iterator is_list_like is_scalar is_sequenceneeds_i8_conversion pandas_dtype) concat_compat) ArrowDtypeBaseMaskedDtypeExtensionDtype)isnanotna) algorithmscommonnanopsops roperator)CachedAccessor)reconstruct_and_relabel_result) take_2d_multi)OpsMixin)BaseMaskedArray DatetimeArrayExtensionArray PeriodArrayTimedeltaArray)SparseFrameAccessor)ensure_wrapped_if_datetimelikesanitize_arraysanitize_masked_array)NDFramemake_doc)check_key_length) DatetimeIndexIndex PeriodIndex default_index ensure_indexensure_index_from_sequences) MultiIndexmaybe_droplevels)check_bool_indexercheck_dict_or_set_indexers) ArrayManager BlockManager) arrays_to_mgrdataclasses_to_dicts dict_to_mgr mgr_to_mgrndarray_to_mgrnested_data_to_arraysrec_array_to_mgrreorder_arrays to_arraystreat_as_nested)selectn)melt)Series) _shared_docs)get_group_indexlexsort_indexernargsort) get_handle)consoleformat)INFO_DOCSTRING DataFrameInfoframe_sub_kwargs)BlockValuesRefs)0 AggFuncTypeAnyAll AnyArrayLike ArrayLikeAxesAxisAxisIntColspaceArgTypeCompressionOptionsCorrelationMethodDropKeepDtypeDtypeObjFilePathFloatFormatTypeFormattersType FrequencyFromDictOrient IgnoreRaise IndexKeyFunc IndexLabel JoinValidateLevelMergeHow MergeValidateMutableMappingTNaAction NaPositionNsmallestNlargestKeepPythonFuncTypeQuantileInterpolation ReadBuffer ReindexMethodRenamerScalarSelfSequenceNotStrSortKindStorageOptionsSuffixes ToGbqIfexistToStataByteorderToTimestampHow UpdateJoin ValueKeyFunc WriteBuffer XMLParsersnptDataFrameGroupBy DataFrame)SingleDataManagerStylerzindex, columnsrz{0 or 'index', 1 or 'columns'}zaxis : {0 or 'index', 1 or 'columns'}, default 0 If 0 or 'index': apply function to each column. If 1 or 'columns': apply function to each row.zj inplace : bool, default False Whether to modify the DataFrame rather than creating a new one.a by : str or list of str Name or list of names to sort by. - if `axis` is 0 or `'index'` then `by` may contain index levels and/or column labels. - if `axis` is 1 or `'columns'` then `by` may contain column levels and/or index labels.a labels : array-like, optional New labels / index to conform the axis specified by 'axis' to. index : array-like, optional New labels for the index. Preferably an Index object to avoid duplicating data. columns : array-like, optional New labels for the columns. Preferably an Index object to avoid duplicating data. axis : int or str, optional Axis to target. Can be either the axis name ('index', 'columns') or number (0, 1).)axesklassaxes_single_argaxisinplace optional_byoptional_reindexa Merge DataFrame or named Series objects with a database-style join. A named Series object is treated as a DataFrame with a single named column. The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed. .. warning:: If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results. Parameters ----------%s right : DataFrame or named Series Object to merge with. how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'inner' Type of merge to be performed. * left: use only keys from left frame, similar to a SQL left outer join; preserve key order. * right: use only keys from right frame, similar to a SQL right outer join; preserve key order. * outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically. * inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys. * cross: creates the cartesian product from both frames, preserves the order of the left keys. on : label or list Column or index level names to join on. These must be found in both DataFrames. If `on` is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames. left_on : label or list, or array-like Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns. right_on : label or list, or array-like Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns. left_index : bool, default False Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels. right_index : bool, default False Use the index from the right DataFrame as the join key. Same caveats as left_index. sort : bool, default False Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword). suffixes : list-like, default is ("_x", "_y") A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in `left` and `right` respectively. Pass a value of `None` instead of a string to indicate that the column name from `left` or `right` should be left as-is, with no suffix. At least one of the values must not be None. copy : bool, default True If False, avoid copy if possible. .. 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`` indicator : bool or str, default False If True, adds a column to the output DataFrame called "_merge" with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of "left_only" for observations whose merge key only appears in the left DataFrame, "right_only" for observations whose merge key only appears in the right DataFrame, and "both" if the observation's merge key is found in both DataFrames. validate : str, optional If specified, checks if merge is of specified type. * "one_to_one" or "1:1": check if merge keys are unique in both left and right datasets. * "one_to_many" or "1:m": check if merge keys are unique in left dataset. * "many_to_one" or "m:1": check if merge keys are unique in right dataset. * "many_to_many" or "m:m": allowed, but does not result in checks. Returns ------- DataFrame A DataFrame of the two merged objects. See Also -------- merge_ordered : Merge with optional filling/interpolation. merge_asof : Merge on nearest keys. DataFrame.join : Similar method using indices. Examples -------- >>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'], ... 'value': [1, 2, 3, 5]}) >>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'], ... 'value': [5, 6, 7, 8]}) >>> df1 lkey value 0 foo 1 1 bar 2 2 baz 3 3 foo 5 >>> df2 rkey value 0 foo 5 1 bar 6 2 baz 7 3 foo 8 Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended. >>> df1.merge(df2, left_on='lkey', right_on='rkey') lkey value_x rkey value_y 0 foo 1 foo 5 1 foo 1 foo 8 2 bar 2 bar 6 3 baz 3 baz 7 4 foo 5 foo 5 5 foo 5 foo 8 Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns. >>> df1.merge(df2, left_on='lkey', right_on='rkey', ... suffixes=('_left', '_right')) lkey value_left rkey value_right 0 foo 1 foo 5 1 foo 1 foo 8 2 bar 2 bar 6 3 baz 3 baz 7 4 foo 5 foo 5 5 foo 5 foo 8 Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns. >>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False)) Traceback (most recent call last): ... ValueError: columns overlap but no suffix specified: Index(['value'], dtype='object') >>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]}) >>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]}) >>> df1 a b 0 foo 1 1 bar 2 >>> df2 a c 0 foo 3 1 baz 4 >>> df1.merge(df2, how='inner', on='a') a b c 0 foo 1 3 >>> df1.merge(df2, how='left', on='a') a b c 0 foo 1 3.0 1 bar 2 NaN >>> df1 = pd.DataFrame({'left': ['foo', 'bar']}) >>> df2 = pd.DataFrame({'right': [7, 8]}) >>> df1 left 0 foo 1 bar >>> df2 right 0 7 1 8 >>> df1.merge(df2, how='cross') left right 0 foo 7 1 foo 8 2 bar 7 3 bar 8 c4^\rSrSr%SrSS1\R -rSr\\ \ \ R4r S1rS\S'\R \"/5-rS \S 'S \S 'S r\GSSj5rGSSjr\rS\S'GSSjrGSGSSjjrGSGSSjjrSS.GSSjjrGSSjr\GSSj5r\GSSj5r\GSSj5r\GSS j5r\GSS!j5r GSS"jr!GSS#jr"GSS$jr#GSS%jr$GSS&jr%\&GSGSS)jj5r'\&GSGSS*jj5r'\("S+S,S(/S-S.9\)"S/S0S1S2S39\)"\*RV\*RXS49GSGSS5jj555r'GSS6jr-\GSS7j5r.S8\/S9'\0"\/S95GSS:j5r1GSS;jr2GSGSS<jjr3GSS=jr4\&GSS>j5r5\&GSS?j5r5GSS@jr5\&GSSAj5r6\&GSSBj5r6GSSCjr6GSSDjr7\8GSGSSGjj5r9SS\:Rv4GSSHjjr<GSSIjr=\&GSS'SJ.GSSKjjj5r>\&S'SJ.GSSLjj5r>\&GSS'S'SM.GSSNjjj5r>\&S'S'SM.GSSOjj5r>\("S+S,SF/SPS.9SE\?S4GSSQjj5r>\("S+S,SR/SSS.9GSGSSTjj5r@\8GSGSSUjj5rAGSGSSVjjrB\8GSGSSWjj5rC\D"\/SX\/SYSZ-S[9SSSSSSS\SS]SSS^. GSS_jj5rEGSS`jrF\("S+S,S(/SaS.9\D"\R\HSb\/SXScSd9GSGSSejj55rG\&GSGSSfjj5rI\&GSGSSgjj5rI\("S+S,SZ/ShS.9\D"\/SXSi9GSGSSjjj55rIGSSkSSSl.GSSmjjjrJ\&GSGSSnjj5rK\&GSGSSojj5rK\("S+S,S(/SpS.9\)"SSqSrSsS39\)"\*RV\*RXS49GSGSStjj555rK\&GSS'S'S'S'S'S'S'S'S'S'S'S'S'S'S'Su.GSSwjjj5rL\&S'S'S'S'S'S'S'S'S'S'S'S'S'S'S'Su.GSSxjj5rL\("S+S,Sv/SyS.9\D"\/SX\/SYSv-S[9GSGSSzjj55rL\D"\M40\ND6GSGSS{jj5rOGSGSS|jjrPSS}.GSS~jjrQ\GSSj5rRGSGSSjjrSGSSjrTGSSjrUGSSjrVSrWSrXSrYGSGSSjjrZGSSjr[GSSjr\GSSjr]Sr^Sr_Sr`GSSjraGSGSSjjrbGSGSSjjrcGSGSSjjrdGSSjreGSGSSjjrfGSSjrgGSSjrhGSSjriGSSjrjGSSjrkGSSjrl\&S'S.GSSjj5rm\&GS Sj5rm\&S'S.GS Sjj5rmSS.GS Sjjrm\&S'S.GS Sjj5rn\&GS Sj5rnSS.GS SjjrnGS GSSjjro\:Rv4GSSjjrpGSSjrqGSSjrr\S5rsGSSjrt\0"S5\)"\HSb\HSSSSS9\0"\RR5SSS.GSU4Sjjj555ru\D"\R\HSb\HSS9GSSSSSSS\ RSSS. GSU4Sjjjj5rv\&GSS'S'S'S'S'S.GSSjjj5rx\&GSS'S'S'S'S'S'S.GSSjjj5rx\&GSS'S'S'S'S'S'S.GSSjjj5rxGSSSSSSSS.GSU4Sjjjjrx\&GSS'S'S'S'S'S'S.GSSjjj5ry\&GSS'S'S'S'S'S'S'S.GSSjjj5ry\&GSS'S'S'S'S'S'S'S.GSSjjj5ryGSSSSSSSSS.GSU4SjjjjryGSU4SjjrzGSSjr{\D"\R\HSbS9SSS\:RvS4GSU4Sjjj5r|\&S'S'S'S'S.GSSjj5r}\&S'S'S'S.GSSjj5r}SSSSS.GS Sjjr}\&GSS'S'S'S'S'SS.GS!Sjjj5r~\&GSS'S'S'S'SS.GS"Sjjj5r~\&GSS'S'S'S'S'SS.GS#Sjjj5r~GSSSSS\:RvSS.GS$Sjjjr~\D"\R\HSbS9GSSj5r\D"\R\HSbS9GSSj5r\D"\GR\HSbS9GSSj5r\D"\GR\HSbS9GSSj5r\&S'S'S'S'S'S'S.GS%Sjj5r\&S'S'S'S'S'S.GS&Sjj5rS\:Rv\:RvSSSS.GS'Sjjr\&GSS'S'S.GS(Sjjj5r\&GSS'S'S'S.GS)Sjjj5r\&GSS'S'S'S.GS*Sjjj5rGSSSSS.GS*SjjjrGS+GS,Sjjr\&S'S'S'S'S'S'S'S.GS-Sjj5r\&S'S'S'S'S'S'S.GS.Sjj5rSSSSSSSS.GS/Sjjr\&S'S'S'S'S'S'S'S'S.GS0Sjj5r\&S'S'S'S'S'S'S'S'S'S. GS1Sjj5r\&S'S'S'S'S'S'S'S'S'S. GS2Sjj5rSSSSSSSSSS. GS3U4SjjjrGS4GS5SjjrGS6GS7SjjrGS6GS7Sjjr\D"\GR\HSb\"S5\"S5S9GS8GS9Sjj5rGSGS:SjjrSrSr\rGSGS;SjjrGSGSGSjrGSGS?GSjjrGS@GSjrSSSGS.GSAGSjjrGSGSjrGSBGSjrGSBGSjrSSGS .GSAGS jjr\0"\GR:"GS S55GSCGSDGS jj5r\0"\GR:"GS S55GSCGSDGSjj5r\0"\GR:"GSS55GSCGSDGSjj5r\0"\GR:"GSS55GSCGSDGSjj5r\0"\GR:"GSS55GSCGSDGSjj5r\0"\GR:"GSS55GSCGSDGSjj5r\0"\GR:"GSS55GSGSDGSjj5r\0"\GR:"GSS55GSGSDGSjj5r\0"\GR:"GSS55GSGSDGSjj5r\r\0"\GR:"GSS55GSGSDGSjj5r\0"\GR:"GSS55GSGSDGS jj5r\r\0"\GR:"GS!S55GSGSDGS"jj5r\0"\GR:"GS#S55GSGSDGS$jj5r\r\r\0"\GR:"GS%S55GSGSDGS&jj5r\r\0"\GR:"GS'S55GSGSDGS(jj5r\0"\GR:"GS)S55GSGSDGS*jj5r\0"\GR:"GS+S55GSGSDGS,jj5r\0"\GR:"GS-S55GSGSDGS.jj5r\0"\GR:"GS/S55GSGSDGS0jj5r\0"\GR:"GS1S55GSGSDGS2jj5r\D"\/GS3\"GS45\HSbS9GSEGSFU4GS5jjj5rGSGSGGS6jjrGSHGS7jrGSIGSJGS8jjr\0"\"GS955\0"\/GS:\H-5S\:RvSSSS\:RvS4GSKGS;jj55rGS<\/GS='\)"S5\0"\/GS=5\:Rv\:RvGS>.GSGS?jj55rGS@\/GSA'\)"S5\0"\/GSA5SSSGSBSSSGSC\:RvS4 GSLGSDjj55rS\:Rv\:RvS4GSMGSEjjrGSGSNGSFjjrGSOGSPGSGjjr\0"\/GSHGSIGSHGSJ.-5GSQGSRGSKjj5r\D"\GRSGSLS\"GSM5GSN9GSSGSTGSOjj5rGSGSUGSPjjr\"GSQ5r\"GSR5r\D"\/GSS\HSb\HS\\GST9GSVGSAGSUjj5r\r\D"\/GSV\HSb\HSGSW9GSGSWGSXjj5rGSXGSYGSYjjrGSGSZGSZjjrGSGS[GS[jjrGS\GS]GS\jjrGS^GS_GS]jjr\)"S5\0"\GS^GS_9GS`GSaGS`jj55rGSGSbGSajjrGScGSdGSbjjrGSeGSfGScjjrGSgGShGSdjjrGSiGSjGSejjrSSSSGSf.GSkGSgjjrGSlGShjr\D"\"GSiGS^GSj95SSSGSk.GSmGSljj5r\D"\"GSmGS^GSj95GSnGSmGSnjj5r\D"\"GSoGS^GSj95GSoGSpU4GSpjjj5r\D"\"GSqGS^GSj95GSoGSpU4GSrjjj5r\D"\"GSsGS^GSj95GSqGSrU4GStjjj5r\D"\"GSuGS^GSj95GSqGSrU4GSvjjj5r\D"\"GSBGS^GSj95GSoGSpU4GSwjjj5r\D"\"GSxGS^GSj95GSoGSpU4GSyjjj5r\D"\"GSzGS^GSj95GSsGStU4GS{jjj5r\D"\"GS|GS^GSj95GSsGStU4GS}jjj5r\D"\"GS~GS^GSj95GSsGStU4GSjjj5r\D"\"GSGS^GSj95GSoGSpU4GSjjj5r\D"\"GSGS^GSj95GSoGSpU4GSjjj5r\r\r\D"\"GSGS^GSj95GSGSuGSjj5r\D"\"GSGS^GSj95GSGSuGSjj5r\D"\"GSGS^GSj95GSGSuGSjj5r\D"\"GSGS^55GSGSuGSjj5rGSvGSwGSjjr\D"\/GSGSGS9GSoGSxGSjj5r\D"\/GSGSGS9GSoGSxGSjj5rGSyGSjrGSnGSzGSjjr\&GSGS{GSjj5r\&GS|GS}GSjj5r\&GSGS~GSjj5rGSGS~GSjjrGSGSGSjjrGSGSGSjjrGSGSjrSS/rGS\GS'0\GRESSGS.ErGS\GS'\"\5rSrGS\GS'SrGS\GS'\GR"SGSGS9r\GR"S\"GS5GS9r\"GS\GRGR5Gr\GRGRGr\GRGR Gr\"SG\5GrGSGr \GSGSj5Gr GSGr U=Gr $(ria Two-dimensional, size-mutable, potentially heterogeneous tabular data. Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure. Parameters ---------- data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame Dict can contain Series, arrays, constants, dataclass or list-like objects. If data is a dict, column order follows insertion-order. If a dict contains Series which have an index defined, it is aligned by its index. This alignment also occurs if data is a Series or a DataFrame itself. Alignment is done on Series/DataFrame inputs. If data is a list of dicts, column order follows insertion-order. index : Index or array-like Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided. columns : Index or array-like Column labels to use for resulting frame when data does not have them, defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels, will perform column selection instead. dtype : dtype, default None Data type to force. Only a single dtype is allowed. If None, infer. copy : bool or None, default None Copy data from inputs. For dict data, the default of None behaves like ``copy=True``. For DataFrame or 2d ndarray input, the default of None behaves like ``copy=False``. If data is a dict containing one or more Series (possibly of different dtypes), ``copy=False`` will ensure that these inputs are not copied. .. versionchanged:: 1.3.0 See Also -------- DataFrame.from_records : Constructor from tuples, also record arrays. DataFrame.from_dict : From dicts of Series, arrays, or dicts. read_csv : Read a comma-separated values (csv) file into DataFrame. read_table : Read general delimited file into DataFrame. read_clipboard : Read text from clipboard into DataFrame. Notes ----- Please reference the :ref:`User Guide ` for more information. Examples -------- Constructing DataFrame from a dictionary. >>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> df = pd.DataFrame(data=d) >>> df col1 col2 0 1 3 1 2 4 Notice that the inferred dtype is int64. >>> df.dtypes col1 int64 col2 int64 dtype: object To enforce a single dtype: >>> df = pd.DataFrame(data=d, dtype=np.int8) >>> df.dtypes col1 int8 col2 int8 dtype: object Constructing DataFrame from a dictionary including Series: >>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])} >>> pd.DataFrame(data=d, index=[0, 1, 2, 3]) col1 col2 0 0 NaN 1 1 NaN 2 2 2.0 3 3 3.0 Constructing DataFrame from numpy ndarray: >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), ... columns=['a', 'b', 'c']) >>> df2 a b c 0 1 2 3 1 4 5 6 2 7 8 9 Constructing DataFrame from a numpy ndarray that has labeled columns: >>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], ... dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")]) >>> df3 = pd.DataFrame(data, columns=['c', 'a']) ... >>> df3 c a 0 3 1 1 6 4 2 9 7 Constructing DataFrame from dataclass: >>> from dataclasses import make_dataclass >>> Point = make_dataclass("Point", [("x", int), ("y", int)]) >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)]) x y 0 0 0 1 0 3 2 2 3 Constructing DataFrame from Series/DataFrame: >>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"]) >>> df = pd.DataFrame(data=ser, index=["a", "c"]) >>> df 0 a 1 c 3 >>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"]) >>> df2 = pd.DataFrame(data=df1, index=["a", "c"]) >>> df2 x a 1 c 3 columnsindex dataframesparsezset[str] _accessorszfrozenset[str] _hidden_attrszBlockManager | ArrayManager_mgric[$Nrselfs C/var/www/html/env/lib/python3.13/site-packages/pandas/core/frame.py _constructorDataFrame._constructorsc[RXS9n[U5[LaU$[U5RS:XaUR U5$UR U5$)Nr GeoDataFrame)r _from_mgrtype__name__r)rmgrrdfs r_constructor_from_mgrDataFrame._constructor_from_mgrs`   0 : "I $Z N 2$$S) )  $$rzCallable[..., Series]_constructor_slicedr}c[R"X5nSUl[U5[LaU$UR U5$r)r}r_namerrr)rrrsers r_constructor_sliced_from_mgr&DataFrame._constructor_sliced_from_mgrs?s) : "J'',,rNc & SnUbURU5n[U[5(a$URnSnU(dUR SS9n[U[ [ 45(aU(dG[R"S[U5RS[U5RS3[SS9 [5(aUR SS9nUc$Uc!UcU(d[R"X5 g[S SS 9n[U[ ["[$45n['US S5n Un [U[(5(a [+S 5e[U[(5(a [+S 5eUc[U[,5(aSnOrUS:Xa8[U[.R0[$45(aUR2S:XaSnO4[5(a#[U["[[ 45(dSnOSnUc6UbUO [5S5nUbUO [5S5nUbUO[7[85n/n[U[ [ 45(aUR;XUS.XES9n GO-[U[,5(a [=XX4XWS9n GO [U[>R@5(aFSSK!J"n [XRF5(a [IS5e[KU5n[MUUUUUUS9n GO[U[.R0[ ["[$45(aURNRP(a*[S[.R0U5n[UUUUUUUS9n GO2['USS5b/[5(aUOSn [=URVU0UUUUU S9n GO[MUUUUUUS9n GO[YU5(Ga[U[ZR\5(d3[_US5(a[.R`"U5nO [cU5n[eU5S:a[gUS5(a [iU5n[U[.R05(d=[kU5(a-Ub [mU5n[oUUUU5upn[qUUUUUS9n O[MUUUUUUS9n O[=0UUbUO [5S5UUS9n OUbUc [+S5e[mU5n[mU5nU(d [sU5upO[U[t5(aE[w[eU55Vs/sHn[yU[eU5U5PM nn[qUX2SUS9n O9[{U[eU5[eU5UU5n[MUUUURNSUS9n [}XS9n [R"X 5 U cVU(aNU [.R~:Xa9URRSU :wa[R"S[SS9 gggggs snf)NFTdeepz Passing a z to zK is deprecated and will raise in a future version. Use public APIs instead. stacklevelmode.data_managersilentdtypezindex cannot be a setzcolumns cannot be a setarrayrrr)rrcopy)rrtyp)mrecordsz]MaskedRecords are not supported. Pass {name: data[name] for name in data.dtype.names} insteadrname)rrr __array__)rrz*DataFrame constructor not properly called!zDtype inference on a pandas object (Series, Index, ExtensionArray) is deprecated. The DataFrame constructor will keep the original dtype in the future. Call `infer_objects` on the result to get the old behavior.)C_validate_dtype isinstancerrrrprowarningswarnrrDeprecationWarningrrb__init__rr}rfr[getattrset ValueErrordictnpndarrayndimrhrIobject _init_mgrrsr MaskedArraynumpy.mar MaskedRecords TypeErrorrarurnamesrrwrrErr hasattrasarraylistlenr=rrrzrirvrqr5rMranger2r3rtobject_dtypesiloc FutureWarning)rdatarrrr allow_mgrmanageris_pandas_object data_dtypeoriginal_dtyperr_copyarrays_valuesarr2ds rrDataFrame.__init__s  ((/E dI & &99DIyyey, d\<8 9 9  d!4!4 5T$t*:M:M9NO//' #$$yyey,}U]4  ,1$?%dVUN,KLT7D1  eS ! !45 5 gs # #67 7 <$%%7"tbjj.%ABBIIN$&&zui000 <".EM!4DE!(!4g-:JG".EL4HED d\<8 9 9..w?u!Cd # #d7dXC bnn - - )$ 6 677).D  Crzz65.I J JzzBJJ-& vt,8 3 5 54!YY% % $  dCLL114--::d+D:D4y1}Q((/5D!$ 338M8M*".w"7-B .*FU(## C)#!# C"&2G a8H } !MNN 'E"7+G248%00 #3w<007tSZO0$FG$GT:JL %++ *#  !&6:;S{{"j0  " 1>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) >>> interchange_object = df_not_necessarily_pandas.__dataframe__() >>> interchange_object.column_names() Index(['A', 'B'], dtype='object') >>> df_pandas = (pd.api.interchange.from_dataframe ... (interchange_object.select_columns_by_name(['A']))) >>> df_pandas A 0 1 1 2 These methods (``column_names``, ``select_columns_by_name``) should work for any dataframe library which implements the interchange protocol. r)PandasDataFrameXchg) allow_copy)!pandas.core.interchange.dataframer%)r nan_as_nullr&r%s r __dataframe__DataFrame.__dataframe__sT J"4??r api_versioncP[S5nURRnU"XS9$)z Provide entry point to the Consortium DataFrame Standard API. This is developed and maintained outside of pandas. Please report any issues to https://github.com/data-apis/dataframe-api-compat. dataframe_api_compatr+)rpandas_standard'convert_to_standard_compliant_dataframe)rr,r.r0s r!__dataframe_consortium_standard__+DataFrame.__dataframe_consortium_standard__s1 ::PQ 0 0 X X 07tUUrc[SSS9nUbURRU5nURR XS9nUR 5$)aH Export the pandas DataFrame as an Arrow C stream PyCapsule. This relies on pyarrow to convert the pandas DataFrame to the Arrow format (and follows the default behaviour of ``pyarrow.Table.from_pandas`` in its handling of the index, i.e. store the index as a column except for RangeIndex). This conversion is not necessarily zero-copy. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the dataframe should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Returns ------- PyCapsule pyarrowz14.0.0) min_version)schema)rSchema_import_from_c_capsuleTable from_pandas__arrow_c_stream__)rrequested_schemapatables rr;DataFrame.__arrow_c_stream__sR*( x H  '!yy??@PQ $$T$C''))rc2URUR/$)aF Return a list representing the axes of the DataFrame. It has the row axis labels and column axis labels as the only members. They are returned in that order. Examples -------- >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.axes [RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'], dtype='object')] rrs rrDataFrame.axess DLL))rcV[UR5[UR54$)aS Return a tuple representing the dimensionality of the DataFrame. See Also -------- ndarray.shape : Tuple of array dimensions. Examples -------- >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.shape (2, 2) >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4], ... 'col3': [5, 6]}) >>> df.shape (2, 3) )rrrrs rshapeDataFrame.shapes (4::DLL 111rc[URRVs1sHoRiM sn5S:*$s snf)a Whether all the columns in a DataFrame have the same type. Returns ------- bool Examples -------- >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type True >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type False Items with the same type but different sizes are considered different types. >>> DataFrame({ ... "A": np.array([1, 2], dtype=np.int32), ... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type False r)rrrr)rarrs r_is_homogeneous_typeDataFrame._is_homogeneous_type-s42)9)9:)9#II)9:;q@@:s=c[UR[5(agURRn[ U5S:wagUSR n[ U5(+$)zI Can we transpose this DataFrame without creating any new array objects. Frr)rrroblocksrrr:)rrJrs r_can_fast_transposeDataFrame._can_fast_transposeHsQ dii . .!! v;! q &u---rcURn[U[5(at[UR5S:XaF[ URSR 5(dURSRSS5$[UR5$URn[U5S:wa[UR5$USRnURS:Xa UR$[SU5nUR$)z; Analogue to ._values that may return a 2D ExtensionArray. rr9np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray)rrrorrr:rreshaper_r rJrrT)rrrJrFs r_valuesDataFrame._valuesWs ii c< ( (3::!#,? 1 @S@S,T,Tzz!},,R331$++> > v;! 1$++> >Qi 88q=;; NPSTuu rc4[S5n[U5U:*$)z Check length against max_rows. display.max_rows)rr)rmax_rowss r_repr_fits_vertical_DataFrame._repr_fits_vertical_us014yH$$rc:[R"5up[S5n[UR5nU(aXC:dU(a XAS-:agUb[R "5(dg[S5c[R "5(aSnO [S5n[5nUnUb#URS[U[U55nOgURUS 9 UR5n[S URS 555n X:$) zh Check if full repr fits in horizontal boundaries imposed by the display options width and max_columns. display.max_columnsrFNTz display.widthrrUbufc38# UHn[U5v M g7fr)r).0lines r 3DataFrame._repr_fits_horizontal_..sA/@tT/@ )rget_console_sizerrrin_interactive_sessionin_ipython_frontendr rmin to_stringgetvaluemaxsplit) rwidthheight max_columns nb_columnsrVr\dvalue repr_widths r_repr_fits_horizontal_ DataFrame._repr_fits_horizontal_|s  002  !67 &  J4 jQJ/ = > > @ @ o & 2g6Q6Q6S6SH!"45H j   .Xs1v./A   Au{{4/@AA !!rc[S5S:HnU=(a, UR5=(a UR5(+$)z- True if the repr should show the info view. zdisplay.large_reprinfo)rrsrW)rinfo_repr_options r _info_reprDataFrame._info_reprsA&&:;vE  ' ' ) Id.G.G.I%  rcUR5(a)[5nURUS9 UR5$[R "5nUR "S0UD6$)z< Return a string representation for a particular DataFrame. r[)rxr rvrifmtget_dataframe_repr_paramsrh)rr\ repr_paramss r__repr__DataFrame.__repr__sR ??  *C II#I <<> !335 ~~, ,,rc"UR5(aS[5nURUS9 UR5R SSS5nUR SSS5nSUS3$[ S 5(a[ S 5n[ S 5n[ S 5n[ S 5n[ R"U40SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SU_SU_SU_S U_S!S"_6n[ R"U5RSS#9$g)$zX Return a html representation for a particular DataFrame. Mainly for IPython notebook. r[z>z
z
zdisplay.notebook_repr_htmlrUzdisplay.min_rowsrZzdisplay.show_dimensionsrN col_spacena_repNaN formatters float_formatsparsifyjustify index_namesTheaderr bold_rowsescaperVmin_rowsmax_colsshow_dimensionsdecimal.)notebook) rxr rvrireplacerr|DataFrameFormatterDataFrameRendererto_html)rr\valrVrrr formatters r _repr_html_DataFrame._repr_html_sx ??  *C II#I ,,.((gq9C++c7A.C3%v& & 2 3 3!"45H!"45H!"78H()BCO..    " !"" "!"!0#$%I(((3;;T;J Jr.r\cgrr{rr\rrrrrrrrrrrVrrr line_widthr max_colwidthencodings rrhDataFrame.to_string. rcgrr{rs rrhrrrz3.0rrh)version allowed_argsrzbool or list of strzkWrite out the column names. If a list of columns is given, it is assumed to be aliases for the column nameszint, list or dict of intzThe minimum width of each column. If a list of ints is given every integers corresponds with one column. If a dict is given, the key references the column, while the value defines the space to use.) header_typercol_space_typer) shared_paramsreturnscSSKJn U"SU5 [R"U4UUUUUU U U UUUU U UUS.6n[R"U5R UUUS9sSSS5 $!,(df  g=f)a Render a DataFrame to a console-friendly tabular output. %(shared_params)s line_width : int, optional Width to wrap a line in characters. min_rows : int, optional The number of rows to display in the console in a truncated repr (when number of rows is above `max_rows`). max_colwidth : int, optional Max width to truncate each column in characters. By default, no limit. encoding : str, default "utf-8" Set character encoding. %(returns)s See Also -------- to_html : Convert DataFrame to HTML. Examples -------- >>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]} >>> df = pd.DataFrame(d) >>> print(df.to_string()) col1 col2 0 1 4 1 2 5 2 3 6 r)option_contextzdisplay.max_colwidth)rrrrrrrrrrrrVrrr)r\rrN)pandasrr|rrrh)rr\rrrrrrrrrrrVrrrrrrrrrs rrhrs~ * 2L A..#%)!'!!! /!I$((3==!%>'B A As A A$$ A2cnURRUUUUUS9nURXfRS9$)N)r date_formatrrquotingr)rget_values_for_csvrr)rrrrrrrs r_get_values_for_csvDataFrame._get_values_for_csvxsFii**%# + ))#HH)==rcSSKJn U"U5$)a Returns a Styler object. Contains methods for building a styled HTML representation of the DataFrame. See Also -------- io.formats.style.Styler : Helps style a DataFrame or Series according to the data with HTML and CSS. Examples -------- >>> df = pd.DataFrame({'A': [1, 2, 3]}) >>> df.style # doctest: +SKIP Please see `Table Visualization <../../user_guide/style.ipynb>`_ for more examples. rr)pandas.io.formats.styler)rrs rstyleDataFrame.styles( 3d|ra Iterate over (column name, Series) pairs. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. Yields ------ label : object The column names for the DataFrame being iterated over. content : Series The column entries belonging to each label, as a Series. See Also -------- DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values. Examples -------- >>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'], ... 'population': [1864, 22000, 80000]}, ... index=['panda', 'polar', 'koala']) >>> df species population panda bear 1864 polar bear 22000 koala marsupial 80000 >>> for label, content in df.items(): ... print(f'label: {label}') ... print(f'content: {content}', sep='\n') ... label: species content: panda bear polar bear koala marsupial Name: species, dtype: object label: population content: panda 1864 polar 22000 koala 80000 Name: population, dtype: int64 itemsc## URR(a9[US5(a(URHnXRU54v M g[ UR5Hup!XR USS94v M g7f)N _item_cacherr)r is_uniquer_get_item_cache enumerate_ixs)rkis rrDataFrame.itemssm << ! !gdM&B&B\\--a000""$,,/11---0sBB c#f# URnURn[5n[URUR 5HgupEU"XQUS9R U5nU(a@URR(a%URRUR5 XF4v Mi g7f)a Iterate over DataFrame rows as (index, Series) pairs. Yields ------ index : label or tuple of label The index of the row. A tuple for a `MultiIndex`. data : Series The data of the row as a Series. See Also -------- DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values. DataFrame.items : Iterate over (column name, Series) pairs. Notes ----- 1. Because ``iterrows`` returns a Series for each row, it does **not** preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). To preserve dtypes while iterating over the rows, it is better to use :meth:`itertuples` which returns namedtuples of the values and which is generally faster than ``iterrows``. 2. You should **never modify** something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect. Examples -------- >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float']) >>> row = next(df.iterrows())[1] >>> row int 1.0 float 1.5 Name: 0, dtype: float64 >>> print(row['int'].dtype) float64 >>> print(df['int'].dtype) int64 rrN) rrrziprr  __finalize__ris_single_blockadd_references)rrr using_cowrvss riterrowsDataFrame.iterrowssZ,,((')  DKK0DAaQ/<>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]}, ... index=['dog', 'hawk']) >>> df num_legs num_wings dog 4 0 hawk 2 2 >>> for row in df.itertuples(): ... print(row) ... Pandas(Index='dog', num_legs=4, num_wings=0) Pandas(Index='hawk', num_legs=2, num_wings=2) By setting the `index` parameter to False we can remove the index as the first element of the tuple: >>> for row in df.itertuples(index=False): ... print(row) ... Pandas(num_legs=4, num_wings=0) Pandas(num_legs=2, num_wings=2) With the `name` parameter set we set a custom name for the yielded namedtuples: >>> for row in df.itertuples(name='Animal'): ... print(row) ... Animal(Index='dog', num_legs=4, num_wings=0) Animal(Index='hawk', num_legs=2, num_wings=2) rrfc3J># UHnTRSS2U4v M g7fr)r)r^rrs rr`'DataFrame.itertuples..^sH/G!dii1o/Gs #T)rename) rrappendrinsertextendrr collections namedtuplemap_maker)rrrrfields itertuples` r itertuplesDataFrame.itertupless@dll#  MM$** % MM!W %  HuS5F/GHH  $..TIyV 5 5F|rc,[UR5$)z9 Returns length of info axis, but here we use the index. )rrrs r__len__DataFrame.__len__ks4::rcgrr{rothers rdot DataFrame.dotq rcgrr{rs rrrurrcz[U[[45(aURR UR 5n[ U5[ UR5:d"[ U5[ UR 5:a [S5eURUSS9nURUSS9nURnURnOiUnURn[R"U5nURSURS:wa%[SURSUR35e[U[5(ao[[UR 5[UR 5-5nUR#[R$"XV5UR URSUS 9$[U[5(a\[[UR 5UR /-5nUR'[R$"XV5UR SUS 9$[U[R([*45(aZ[R$"XV5nUR,S :XaUR#XR SS9$UR'XR SS9$[/S [1U535e) a Compute the matrix multiplication between the DataFrame and other. This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array. It can also be called using ``self @ other``. Parameters ---------- other : Series, DataFrame or array-like The other object to compute the matrix product with. Returns ------- Series or DataFrame If other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array. See Also -------- Series.dot: Similar method for Series. Notes ----- The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication. The dot method for Series computes the inner product, instead of the matrix product here. Examples -------- Here we multiply a DataFrame with a Series. >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]]) >>> s = pd.Series([1, 1, 2, 1]) >>> df.dot(s) 0 -4 1 5 dtype: int64 Here we multiply a DataFrame with another DataFrame. >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]]) >>> df.dot(other) 0 1 0 1 4 1 2 2 Note that the dot method give the same result as @ >>> df @ other 0 1 0 1 4 1 2 2 The dot method works also if other is an np.array. >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]]) >>> df.dot(arr) 0 1 0 1 4 1 2 2 Note how shuffling of the objects does not change the result. >>> s2 = s.reindex([1, 0, 2, 3]) >>> df.dot(s2) 0 -4 1 5 dtype: int64 zmatrices are not alignedFrrrrrrzDot product shape mismatch, z vs rrrr)rrrrzunsupported type: )rr}rrunionrrrreindexr rRrrrCr4rrrrrrrfrr r) rrrQleftrightlvalsrvals common_typeresults rrrys3Z efi0 1 1\\'' 4F6{S..#f+EKK@P2P !;<<<<U<;DMMUM;EKKEMMEDKKEJJu%E{{1~Q/ 25;;-tEKK=Q eY ' '*4 +? ?rcgrr{rs r __matmul__DataFrame.__matmul__rrcgrr{rs rrrrrc$URU5$)2 Matrix multiplication using binary `@` operator. )rrs rrrsxxrc URR[R"U55R$![aHnS[ U5;aeS[R "U5SUR S3n[ U5UeSnAff=f)rzshape mismatchzshapes z and z not alignedN)rQrr transposerstrrC)rrerrmsgs r __rmatmul__DataFrame.__rmatmul__su +66::bll51244 4 +s3x/BHHUO,E$**\JCS/s *  +s8; B ABB rorientcSnUR5nUS:Xa[U5S:ay[[[ UR 555[ [45(a [U5nO[[UR55n[UR 55nO(US;aUb[SUS35eO[SUS35eUS :waU"XXCS 9$US nS nU"USUS 5nU"USUS5nU"XeXCS 9$)aB Construct DataFrame from dict of array-like or dicts. Creates DataFrame object from dictionary by columns or by index allowing dtype specification. Parameters ---------- data : dict Of the form {field : array-like} or {field : dict}. orient : {'columns', 'index', 'tight'}, default 'columns' The "orientation" of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'. If 'tight', assume a dict with keys ['index', 'columns', 'data', 'index_names', 'column_names']. .. versionadded:: 1.4.0 'tight' as an allowed value for the ``orient`` argument dtype : dtype, default None Data type to force after DataFrame construction, otherwise infer. columns : list, default None Column labels to use when ``orient='index'``. Raises a ValueError if used with ``orient='columns'`` or ``orient='tight'``. Returns ------- DataFrame See Also -------- DataFrame.from_records : DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame. DataFrame : DataFrame object creation using constructor. DataFrame.to_dict : Convert the DataFrame to a dictionary. Examples -------- By default the keys of the dict become the DataFrame columns: >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']} >>> pd.DataFrame.from_dict(data) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d Specify ``orient='index'`` to create the DataFrame using dictionary keys as rows: >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']} >>> pd.DataFrame.from_dict(data, orient='index') 0 1 2 3 row_1 3 2 1 0 row_2 a b c d When using the 'index' orientation, the column names can be specified manually: >>> pd.DataFrame.from_dict(data, orient='index', ... columns=['A', 'B', 'C', 'D']) A B C D row_1 3 2 1 0 row_2 a b c d Specify ``orient='tight'`` to create the DataFrame using a 'tight' format: >>> data = {'index': [('a', 'b'), ('a', 'c')], ... 'columns': [('x', 1), ('y', 2)], ... 'data': [[1, 3], [2, 4]], ... 'index_names': ['n1', 'n2'], ... 'column_names': ['z1', 'z2']} >>> pd.DataFrame.from_dict(data, orient='tight') z1 x y z2 1 2 n1 n2 a b 1 3 c 2 4 Nrr)rtightz*cannot use columns parameter with orient=''zBExpected 'index', 'columns' or 'tight' for orient parameter. Got 'z ' insteadrrrrrch[U5S:a[R"XS9nU$[XSS9nU$)Nrr rr)rrk from_tuplesrf) indexlistnamelistrs r create_index)DataFrame.from_dict..create_indexs:x=1$&229ME ")1+>E rrr column_names) lowerrrnextiterr r}r_from_nested_dictrkeysr)clsrrrrrrealdatar s r from_dictDataFrame.from_dict st W 4y1}d4 #67&$HH,T2D -E  .D + +" #MfXUV!WXX#xy*  W t'G GF|H !g]0CDE"4 ?D4HIGxgK KrcUb[R"U5nURRXUS9nURULa[R"XAS9nU$)a Convert the DataFrame to a NumPy array. By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are ``float16`` and ``float32``, the results dtype will be ``float32``. This may require copying data and coercing values, which may be expensive. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray`. copy : bool, default False Whether to ensure that the returned value is not a view on another array. Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. na_value : Any, optional The value to use for missing values. The default value depends on `dtype` and the dtypes of the DataFrame columns. Returns ------- numpy.ndarray See Also -------- Series.to_numpy : Similar method for Series. Examples -------- >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy() array([[1, 3], [2, 4]]) With heterogeneous data, the lowest common type will have to be used. >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]}) >>> df.to_numpy() array([[1. , 3. ], [2. , 4.5]]) For a mix of numeric and non-numeric types, the output array will have object dtype. >>> df['C'] = pd.date_range('2000', periods=2) >>> df.to_numpy() array([[1, 3.0, Timestamp('2000-01-01 00:00:00')], [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object) )rrna_valuer)rrras_arrayr)rrrrrs rto_numpyDataFrame.to_numpysPt  HHUOE##%X#N < {index -> value}} - 'list' : dict like {column -> [values]} - 'series' : dict like {column -> Series(values)} - 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]} - 'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values], 'index_names' -> [index.names], 'column_names' -> [column.names]} - 'records' : list like [{column -> value}, ... , {column -> value}] - 'index' : dict like {index -> {column -> value}} .. versionadded:: 1.4.0 'tight' as an allowed value for the ``orient`` argument into : class, default dict The collections.abc.MutableMapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized. index : bool, default True Whether to include the index item (and index_names item if `orient` is 'tight') in the returned dictionary. Can only be ``False`` when `orient` is 'split' or 'tight'. .. versionadded:: 2.0.0 Returns ------- dict, list or collections.abc.MutableMapping Return a collections.abc.MutableMapping object representing the DataFrame. The resulting transformation depends on the `orient` parameter. See Also -------- DataFrame.from_dict: Create a DataFrame from a dictionary. DataFrame.to_json: Convert a DataFrame to JSON format. Examples -------- >>> df = pd.DataFrame({'col1': [1, 2], ... 'col2': [0.5, 0.75]}, ... index=['row1', 'row2']) >>> df col1 col2 row1 1 0.50 row2 2 0.75 >>> df.to_dict() {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}} You can specify the return orientation. >>> df.to_dict('series') {'col1': row1 1 row2 2 Name: col1, dtype: int64, 'col2': row1 0.50 row2 0.75 Name: col2, dtype: float64} >>> df.to_dict('split') {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'], 'data': [[1, 0.5], [2, 0.75]]} >>> df.to_dict('records') [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}] >>> df.to_dict('index') {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}} >>> df.to_dict('tight') {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'], 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]} You can also specify the mapping type. >>> from collections import OrderedDict, defaultdict >>> df.to_dict(into=OrderedDict) OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])), ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))]) If you want a `defaultdict`, you need to initialize it: >>> dd = defaultdict(list) >>> df.to_dict('records', into=dd) [defaultdict(, {'col1': 1, 'col2': 0.5}), defaultdict(, {'col1': 2, 'col2': 0.75})] r)r(r,)pandas.core.methods.to_dictr()rrr'rr(s rr(r)sb 8t$<`__. See the `How to authenticate with Google BigQuery `__ guide for authentication instructions. Parameters ---------- destination_table : str Name of table to be written, in the form ``dataset.tablename``. project_id : str, optional Google BigQuery Account project ID. Optional when available from the environment. chunksize : int, optional Number of rows to be inserted in each chunk from the dataframe. Set to ``None`` to load the whole dataframe at once. reauth : bool, default False Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used. if_exists : str, default 'fail' Behavior when the destination table exists. Value can be one of: ``'fail'`` If table exists raise pandas_gbq.gbq.TableCreationError. ``'replace'`` If table exists, drop it, recreate it, and insert data. ``'append'`` If table exists, insert data. Create if does not exist. auth_local_webserver : bool, default True Use the `local webserver flow`_ instead of the `console flow`_ when getting user credentials. .. _local webserver flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server .. _console flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console *New in version 0.2.0 of pandas-gbq*. .. versionchanged:: 1.5.0 Default value is changed to ``True``. Google has deprecated the ``auth_local_webserver = False`` `"out of band" (copy-paste) flow `_. table_schema : list of dicts, optional List of BigQuery table fields to which according DataFrame columns conform to, e.g. ``[{'name': 'col1', 'type': 'STRING'},...]``. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field. *New in version 0.3.1 of pandas-gbq*. location : str, optional Location where the load job should run. See the `BigQuery locations documentation `__ for a list of available locations. The location must match that of the target dataset. *New in version 0.5.0 of pandas-gbq*. progress_bar : bool, default True Use the library `tqdm` to show the progress bar for the upload, chunk by chunk. *New in version 0.5.0 of pandas-gbq*. credentials : google.auth.credentials.Credentials, optional Credentials for accessing Google APIs. Use this parameter to override default credentials, such as to use Compute Engine :class:`google.auth.compute_engine.Credentials` or Service Account :class:`google.oauth2.service_account.Credentials` directly. *New in version 0.8.0 of pandas-gbq*. See Also -------- pandas_gbq.to_gbq : This function in the pandas-gbq library. read_gbq : Read a DataFrame from Google BigQuery. Examples -------- Example taken from `Google BigQuery documentation `_ >>> project_id = "my-project" >>> table_id = 'my_dataset.my_table' >>> df = pd.DataFrame({ ... "my_string": ["a", "b", "c"], ... "my_int64": [1, 2, 3], ... "my_float64": [4.0, 5.0, 6.0], ... "my_bool1": [True, False, True], ... "my_bool2": [False, True, False], ... "my_dates": pd.date_range("now", periods=3), ... } ... ) >>> df.to_gbq(table_id, project_id=project_id) # doctest: +SKIP r)gbq) project_id chunksizereauth if_existsauth_local_webserver table_schemalocation progress_bar credentialsN) pandas.ior4r2) rr1r5r6r7r8r9r:r;r<r=r4s rr2DataFrame.to_gbqs:t "  !!5%%#  rc[U[5(aq[R"S[[ 5S9 Ub[ U5(aU/nXnUbURU5nUbURUS9nURSS9$SnUb [U5nSSjn[U5(aUS:XaU"5$[U5n Sn [U S 5(a'U RR (a U Rn U /n UcX- n O(U R#[$R&"XS - 55 U b[(R*"XS 9nOU n[U[,5(aUc+[[/U55=pLUV s/sHoU PM nn GO/n/nUR15H/un nX;dM UR3U 5 UR3U5 M1 [5U5n U"XXB5upnO[U[(R65(a[9X5upUn Ot[9X5upU(aC[;U5H4unnUR[<:XdM[>R@"US S9UU'M6 [U 5n UcU nO U"XXB5upnUc [C5nO [CU5nUb[U[D5(d[US5(dJURGU5nURIU5 [KU5S:a[5UUUS9nOJ[5/US9nO?UVs/sHnXRGU5PM nn[MUUS9nUROU5 [UU5(atUVs/sH nUU ;dM UPM nnUVs/sHnU RGU5PM nn[;U5VVs/sHunnUU;dMUPM nnnURU5n[WSS S9n[YXUUS9nUR[UUR\S9$![a U"X$S 9s$f=fs sn fs snf![P[R4a UnNf=fs snfs snfs snnf)ah Convert structured or record ndarray to DataFrame. Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame. Parameters ---------- data : structured ndarray, sequence of tuples or dicts, or DataFrame Structured input data. .. deprecated:: 2.1.0 Passing a DataFrame is deprecated. index : str, list of fields, array-like Field of array to use as the index, alternately a specific set of input labels to use. exclude : sequence, default None Columns or fields to exclude. columns : sequence, default None Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns). coerce_float : bool, default False Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets. nrows : int, default None Number of rows to read if data is an iterator. Returns ------- DataFrame See Also -------- DataFrame.from_dict : DataFrame from dict of array-like or dicts. DataFrame : DataFrame object creation using constructor. Examples -------- Data can be provided as a structured ndarray: >>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')], ... dtype=[('col_1', 'i4'), ('col_2', 'U1')]) >>> pd.DataFrame.from_records(data) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d Data can be provided as a list of dicts: >>> data = [{'col_1': 3, 'col_2': 'a'}, ... {'col_1': 2, 'col_2': 'b'}, ... {'col_1': 1, 'col_2': 'c'}, ... {'col_1': 0, 'col_2': 'd'}] >>> pd.DataFrame.from_records(data) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d Data can be provided as a list of tuples with corresponding columns: >>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')] >>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2']) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d zwPassing a DataFrame to DataFrame.from_records is deprecated. Use set_index and/or drop to modify the DataFrame instead.rN)rFrc[U5(a[US5nOSnSn[U5S:XaUcUS:Xa [S5n[XX$5upXU4$)z If our desired 'columns' do not match the data's pre-existing 'arr_columns', we re-order our arrays. This is like a pre-emptive (cheap) reindex. rN)rrhrx)r arr_columnsrrlength result_indexs r maybe_reorder-DataFrame.from_records..maybe_reorderz s^6{{VAYL6{aEMfk,Q/ "0g"V F 4 4rrrrrrT) try_float__iter__rrrrrr)rzlist[ArrayLike]rBrfrrfreturnz+tuple[list[ArrayLike], Index, Index | None])/rrrrrr+rF set_indexdroprrirDr StopIterationrrr r itertoolsislicerrrsortedrrrfrryrrrmaybe_convert_objectsrrget_locaddrrjupdateKeyErrorr anyrrqrr)rrrexcluder coerce_floatnrowsrDrE first_rowrr rBrrarr_columns_listrrrFfield index_datax arr_excludecol to_removerrs r from_recordsDataFrame.from_records sAj dI & & MMI+-   "W%%&iG} ~~e,"yyy199%9( (   "7+G 5# 527 5BG 5 8 5& t  zu  9 J Ey'**y/D/D![F} i..tQY?@ xx4 dD ! !(4VD\(BB+237aq'73#%  JJLDAq|(//2 a() $$45 4A51\bjj ) )'6OF!K"+D": F'/FAsyyF*%($=$=&*%q 0'{3K%4A51\ ?eG'lG  %%%WUJ-G-GOOE* E"v;?#(#?L#(%#8L*RW!XRW&)<)>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]}, ... index=['a', 'b']) >>> df A B a 1 0.50 b 2 0.75 >>> df.to_records() rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)], dtype=[('index', 'O'), ('A', '>> df.index = df.index.rename("I") >>> df.to_records() rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)], dtype=[('I', 'O'), ('A', '>> df.to_records(index=False) rec.array([(1, 0.5 ), (2, 0.75)], dtype=[('A', '>> df.to_records(column_dtypes={"A": "int32"}) rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)], dtype=[('I', 'O'), ('A', '>> df.to_records(index_dtypes=">> index_dtypes = f">> df.to_records(index_dtypes=index_dtypes) rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)], dtype=[('I', 'S1'), ('A', 'rrrrrec fromarrays)rr column_dtypes index_dtypesrix_valsrrrr c index_lenrfr index_int dtype_mappingelementrs r to_recordsDataFrame.to_records sf tzz1122A 4::66q9:2  5:3t||;L5M 5M 499QT?+5M Ftzz//0K$**j11!44[A Q'&i +4??; +UV+U4SY+UEVE;@T\\AR;ST;Sabjj1a41;SFT%)\\2\SV\E2K $ f%DAI$ , "-& - ||I.M**($1$7M/$1$", "<", "little", or "big". default is `sys.byteorder`. time_stamp : datetime A datetime to use as file creation date. Default is the current time. data_label : str, optional A label for the data set. Must be 80 characters or smaller. variable_labels : dict Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller. version : {{114, 117, 118, 119, None}}, default 114 Version to use in the output dta file. Set to None to let pandas decide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables. Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files. convert_strl : list, optional List of column names to convert to string columns to Stata StrL format. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. {storage_options} value_labels : dict of dicts Dictionary containing columns as keys and dictionaries of column value to labels as values. Labels for a single variable must be 32,000 characters or smaller. .. versionadded:: 1.4.0 Raises ------ NotImplementedError * If datetimes contain timezone information * Column dtype is not representable in Stata ValueError * Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime * Column listed in convert_dates is not in DataFrame * Categorical label contains more than 32,000 characters See Also -------- read_stata : Import Stata data files. io.stata.StataWriter : Low-level writer for Stata data files. io.stata.StataWriter117 : Low-level writer for version 117 files. Examples -------- >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon', ... 'parrot'], ... 'speed': [350, 18, 361, 15]}}) >>> df.to_stata('animals.dta') # doctest: +SKIP )ruvwNz1Only formats 114, 117, 118 and 119 are supported.rNz#strl is not supported in format 114r) StataWriterr)StataWriter117)StataWriterUTF8rrr) rrrrrrrr|r)rpandas.io.statarrr write_file)rr~rrrrrrrrrr|r statawriterkwargswriters rto_stataDataFrame.to_stata s\ 4 4PQ Q c>' !FGG B ^   "$ ?gn%1> " ?gn '9     (!!#+#+%     rc "SSKJn U"X40UD6 g)a Write a DataFrame to the binary Feather format. Parameters ---------- path : str, path object, file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. If a string or a path, it will be used as Root Directory path when writing a partitioned dataset. **kwargs : Additional keywords passed to :func:`pyarrow.feather.write_feather`. This includes the `compression`, `compression_level`, `chunksize` and `version` keywords. Notes ----- This function writes the dataframe as a `feather file `_. Requires a default index. For saving the DataFrame with your custom index use a method that supports custom indices e.g. `to_parquet`. Examples -------- >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]]) >>> df.to_feather("file.feather") # doctest: +SKIP r) to_featherN)pandas.io.feather_formatr)rr~rrs rrDataFrame.to_feather] s6 84((r to_markdownraExamples -------- >>> df = pd.DataFrame( ... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]} ... ) >>> print(df.to_markdown()) | | animal_1 | animal_2 | |---:|:-----------|:-----------| | 0 | elk | dog | | 1 | pig | quetzal | Output markdown with a tabulate option. >>> print(df.to_markdown(tablefmt="grid")) +----+------------+------------+ | | animal_1 | animal_2 | +====+============+============+ | 0 | elk | dog | +----+------------+------------+ | 1 | pig | quetzal | +----+------------+------------+)rr|examplesc VSU;a [S5eURSS5 URSS5 URSU5 [S5nUR"U40UD6nUcU$[ XUS9nUR R U5 SSS5 g!,(df  g=f) N showindexz"Pass 'index' instead of 'showindexheadersrtablefmtpipetabulater|)r setdefaultrrrhandlewrite) rr\moderr|rrrhandless rrDataFrame.to_markdown| sJ & AB B)V,*f-+u--j9""4262 ;M ? Cw NN  (DD Cs 5B B(c grr{rr~enginerrpartition_colsr|rs r to_parquetDataFrame.to_parquet  rc grr{rs rrr rrrrc .SSKJn U"UUU4UUUUS.UD6$)a$ Write a DataFrame to the binary parquet format. This function writes the dataframe as a `parquet file `_. You can choose different parquet backends, and have the option of compression. See :ref:`the user guide ` for more details. Parameters ---------- path : str, path object, file-like object, or None, default None String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset. engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used. The default ``io.parquet.engine`` behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. compression : str or None, default 'snappy' Name of the compression to use. Use ``None`` for no compression. Supported options: 'snappy', 'gzip', 'brotli', 'lz4', 'zstd'. index : bool, default None If ``True``, include the dataframe's index(es) in the file output. If ``False``, they will not be written to the file. If ``None``, similar to ``True`` the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output. partition_cols : list, optional, default None Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string. {storage_options} **kwargs Additional arguments passed to the parquet library. See :ref:`pandas io ` for more details. Returns ------- bytes if no path argument is provided else None See Also -------- read_parquet : Read a parquet file. DataFrame.to_orc : Write an orc file. DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf. Notes ----- This function requires either the `fastparquet `_ or `pyarrow `_ library. Examples -------- >>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}}) >>> df.to_parquet('df.parquet.gzip', ... compression='gzip') # doctest: +SKIP >>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP col1 col2 0 1 3 1 2 4 If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don't use partition_cols, which creates multiple files. >>> import io >>> f = io.BytesIO() >>> df.to_parquet(f) >>> f.seek(0) 0 >>> content = f.read() r)r)rrrr|)pandas.io.parquetr) rr~rrrrr|rrs rrr s;z 1     $)+    rr4rr engine_kwargscSSKJn U"XX#US9$)an Write a DataFrame to the ORC format. .. versionadded:: 1.5.0 Parameters ---------- path : str, file-like object or None, default None If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned. engine : {'pyarrow'}, default 'pyarrow' ORC library to use. index : bool, optional If ``True``, include the dataframe's index(es) in the file output. If ``False``, they will not be written to the file. If ``None``, similar to ``infer`` the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output. engine_kwargs : dict[str, Any] or None, default None Additional keyword arguments passed to :func:`pyarrow.orc.write_table`. Returns ------- bytes if no path argument is provided else None Raises ------ NotImplementedError Dtype of one or more columns is category, unsigned integers, interval, period or sparse. ValueError engine is not pyarrow. See Also -------- read_orc : Read a ORC file. DataFrame.to_parquet : Write a parquet file. DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf. Notes ----- * Before using this function you should read the :ref:`user guide about ORC ` and :ref:`install optional dependencies `. * This function requires `pyarrow `_ library. * For supported dtypes please refer to `supported ORC features in Arrow `__. * Currently timezones in datetime columns are not preserved when a dataframe is converted into ORC files. Examples -------- >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]}) >>> df.to_orc('df.orc') # doctest: +SKIP >>> pd.read_orc('df.orc') # doctest: +SKIP col1 col2 0 1 4 1 2 3 If you want to get a buffer to the orc content you can write it to io.BytesIO >>> import io >>> b = io.BytesIO(df.to_orc()) # doctest: +SKIP >>> b.seek(0) # doctest: +SKIP 0 >>> content = b.read() # doctest: +SKIP r)to_orcr) pandas.io.orcr)rr~rrrrs rrDataFrame.to_orc4 sd ) v-  rcgrr{rr\rrrrrrrrrrrVrrrrclassesrrbordertable_id render_linksrs rrDataFrame.to_html 6 rcgrr{rs rrr rrrz,Whether to print column labels, default Truez&str or int, list or dict of int or strzXThe minimum width of each column in CSS length units. An int is assumed to be px units.c "U bU [R;a [S5e[R"U40SU_SU_SU_SU_SU_SU_SU_S U_S U _S U _S U _S U_SU_SU _SU _SU_6n[R"U5R UUUUUUUS9$)az Render a DataFrame as an HTML table. %(shared_params)s bold_rows : bool, default True Make the row labels bold in the output. classes : str or list or tuple, default None CSS class(es) to apply to the resulting html table. escape : bool, default True Convert the characters <, >, and & to HTML-safe sequences. notebook : {True, False}, default False Whether the generated HTML is for IPython Notebook. border : int A ``border=border`` attribute is included in the opening `` tag. Default ``pd.options.display.html.border``. table_id : str, optional A css id is included in the opening `
` tag if specified. render_links : bool, default False Convert URLs to HTML links. encoding : str, default "utf-8" Set character encoding. %(returns)s See Also -------- to_string : Convert DataFrame to a string. Examples -------- >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]}) >>> html_string = '''
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
col1col2
014
123
''' >>> assert html_string == df.to_html() z#Invalid value for justify parameterrrrrrrrrrrrrrrVrr)r\rrrrrr)r|VALID_JUSTIFY_PARAMETERSrrrr)rr\rrrrrrrrrrrVrrrrrrrrrrrrs rrr sp  7#2N2N#NBC C**           " &     $    ! ",# ($$Y/77%8  r)r root_namerow_namer attr_cols elem_cols namespacesprefixrxml_declaration pretty_printparser stylesheetrr|path_or_buffercgrr{rrrrrrrrrrrrrrrrr|s rto_xmlDataFrame.to_xml? * rcgrr{rs rrrV rrrcSSKJnJn [SSS9nU S:XaUbUnO[ S5eU S:XaUnO [ S 5eU"U4UUUUUUUUU U U U UUUS .6nUR 5$) a Render a DataFrame to an XML document. .. versionadded:: 1.3.0 Parameters ---------- path_or_buffer : 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. index : bool, default True Whether to include index in XML document. root_name : str, default 'data' The name of root element in XML document. row_name : str, default 'row' The name of row element in XML document. na_rep : str, optional Missing data representation. attr_cols : list-like, optional List of columns to write as attributes in row element. Hierarchical columns will be flattened with underscore delimiting the different levels. elem_cols : list-like, optional List of columns to write as children in row element. By default, all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels. namespaces : dict, optional All namespaces to be defined in root element. Keys of dict should be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example, :: namespaces = {{"": "https://example.com"}} prefix : str, optional Namespace prefix to be used for every element and/or attribute in document. This should be one of the keys in ``namespaces`` dict. encoding : str, default 'utf-8' Encoding of the resulting document. xml_declaration : bool, default True Whether to include the XML declaration at start of document. pretty_print : bool, default True Whether output should be pretty printed with indentation and line breaks. parser : {{'lxml','etree'}}, default 'lxml' Parser module to use for building of tree. Only 'lxml' and 'etree' are supported. With 'lxml', the ability to use XSLT stylesheet is supported. stylesheet : str, path object or file-like object, optional A URL, file-like object, or a raw string containing an XSLT script used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires ``lxml`` to be installed. Only XSLT 1.0 scripts and not later versions is currently supported. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. {storage_options} Returns ------- None or str If ``io`` is None, returns the resulting XML format as a string. Otherwise returns None. See Also -------- to_json : Convert the pandas object to a JSON string. to_html : Convert DataFrame to a html. Examples -------- >>> df = pd.DataFrame({{'shape': ['square', 'circle', 'triangle'], ... 'degrees': [360, 360, 180], ... 'sides': [4, np.nan, 3]}}) >>> df.to_xml() # doctest: +SKIP 0 square 360 4.0 1 circle 360 2 triangle 180 3.0 >>> df.to_xml(attr_cols=[ ... 'index', 'shape', 'degrees', 'sides' ... ]) # doctest: +SKIP >>> df.to_xml(namespaces={{"doc": "https://example.com"}}, ... prefix="doc") # doctest: +SKIP 0 square 360 4.0 1 circle 360 2 triangle 180 3.0 r)EtreeXMLFormatterLxmlXMLFormatterz lxml.etreeignore)errorslxmlz7lxml not found, please install or use the etree parser.etreez,Values for parser can only be lxml or etree.)rrrrrrrrrrrrrrr|)pandas.io.formats.xmlrrr ImportErrorr write_output)rrrrrrrrrrrrrrrrr|rrr TreeBuilder xml_formatters rrrm sH *,xH V . !Mw +KKL L#  )!+%!#+! &))++rc<[UUS9nURUUUUS9 g)N)r memory_usage)r\rverbose show_counts)rrender)rrr\rrrrvs rrvDataFrame.info>s3%  #  rc FURUR5VVs/sHup4URSUS9PM snnUR[R S9nU(a9URUR RUS9S/S9nURU5nU$s snnf)a Return the memory usage of each column in bytes. The memory usage can optionally include the contribution of the index and elements of `object` dtype. This value is displayed in `DataFrame.info` by default. This can be suppressed by setting ``pandas.options.display.memory_usage`` to False. Parameters ---------- index : bool, default True Specifies whether to include the memory usage of the DataFrame's index in returned Series. If ``index=True``, the memory usage of the index is the first item in the output. deep : bool, default False If True, introspect the data deeply by interrogating `object` dtypes for system-level memory consumption, and include it in the returned values. Returns ------- Series A Series whose index is the original column names and whose values is the memory usage of each column in bytes. See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of an ndarray. Series.memory_usage : Bytes consumed by a Series. Categorical : Memory-efficient array for string values with many repeated values. DataFrame.info : Concise summary of a DataFrame. Notes ----- See the :ref:`Frequently Asked Questions ` for more details. Examples -------- >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool'] >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t)) ... for t in dtypes]) >>> df = pd.DataFrame(data) >>> df.head() int64 float64 complex128 object bool 0 1 1.0 1.0+0.0j 1 True 1 1 1.0 1.0+0.0j 1 True 2 1 1.0 1.0+0.0j 1 True 3 1 1.0 1.0+0.0j 1 True 4 1 1.0 1.0+0.0j 1 True >>> df.memory_usage() Index 128 int64 40000 float64 40000 complex128 80000 object 40000 bool 5000 dtype: int64 >>> df.memory_usage(index=False) int64 40000 float64 40000 complex128 80000 object 40000 bool 5000 dtype: int64 The memory footprint of `object` dtype columns is ignored by default: >>> df.memory_usage(deep=True) Index 128 int64 40000 float64 40000 complex128 80000 object 180000 bool 5000 dtype: int64 Use a Categorical for efficient storage of an object-dtype column with many repeated values. >>> df['object'].astype('category').memory_usage(deep=True) 5244 F)rrrrrrfr$)rrrrrintpr_append)rrrr_rqrindex_memory_usages rrDataFrame.memory_usageRsr))BF**, O,Q^^%d^ 3, O,,''*  !%!9!9 ''T'27)":" (//7F  PsB rcL[R"U05 [UR5nUR(aUR R nU(a[5(dUR5nURUURURSURS9n[5(a4[U5S:a%URRUR5 GOUR (Ga"U(Ga[#US[$5(Ga[#US[&5(a2SSKJn U"[-[.[0UR3555nO[#US[45(a0SSKJnJn U "[-[.UUR3555nO?USn U R=5n UR>n U V s/sHoRAXS9PM nn [CU5REUURURSS9nOmUR>R nU(a[5(dUR5nURUURURURSS9nURGUS S 9$s sn f) aw Transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:`.T` is an accessor to the method :meth:`transpose`. Parameters ---------- *args : tuple, optional Accepted for compatibility with NumPy. copy : bool, default False Whether to copy the data after transposing, even for DataFrames with a single dtype. Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types. .. 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 ------- DataFrame The transposed DataFrame. See Also -------- numpy.transpose : Permute the dimensions of a given array. Notes ----- Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the `object` dtype. In such a case, a copy of the data is always made. Examples -------- **Square DataFrame with homogeneous dtype** >>> d1 = {'col1': [1, 2], 'col2': [3, 4]} >>> df1 = pd.DataFrame(data=d1) >>> df1 col1 col2 0 1 3 1 2 4 >>> df1_transposed = df1.T # or df1.transpose() >>> df1_transposed 0 1 col1 1 2 col2 3 4 When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype: >>> df1.dtypes col1 int64 col2 int64 dtype: object >>> df1_transposed.dtypes 0 int64 1 int64 dtype: object **Non-square DataFrame with mixed dtypes** >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob 8.0 True 0 >>> df2_transposed = df2.T # or df2.transpose() >>> df2_transposed 0 1 name Alice Bob score 9.5 8.0 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the `object` dtype: >>> df2.dtypes name object score float64 employed bool kids int64 dtype: object >>> df2_transposed.dtypes 0 object 1 object dtype: object Frr)#transpose_homogeneous_masked_arrays)ArrowExtensionArraytranspose_homogeneous_pyarrowr)rrry)rrrrrmethod)$nvvalidate_transposerrrKrRrQrrrrrrrrrrGrrMrLpandas.core.arrays.maskedrrr rY_iter_column_arraysrKpandas.core.arrays.arrow.arrayrrconstruct_array_typer _from_sequencerrzr)rrargsrnew_valsrr new_valuesrrdtyparr_typr r!new_arrs rrDataFrame.transposes/Z dB'dkk"  # #||~~H/11#==?&&ll nn 'F#$$TQ **4995  % % %6!9n55&)_55A/2D4L4L4NO F1Iz22 ;"568P8P8RS ay335QWXQW#44S4EQW X$Z,,ll !& -FkkmmG/11!,,.&&ll mm 'F""4 "<<-YsJ!c"UR5$)aG The transpose of the DataFrame. Returns ------- DataFrame The transposed DataFrame. See Also -------- DataFrame.transpose : Transpose index and columns. Examples -------- >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df col1 col2 0 1 3 1 2 4 >>> df.T 0 1 col1 1 2 col2 3 4 )rrs rrQ DataFrame.Tos6~~rrrcUS:XaURRU5n[UR[R 5=(a URR SLnURX3RS9nURUUl URU5nURXS9 U$URUnURRU5nURXq5nUR!X`5 U$)zB Parameters ---------- i : int axis : int Returns ------- Series rNrr)rfast_xsrrrrbaserrrrr _set_is_copyriget_box_col_values_set_as_cached)rrrnew_mgrrrlabelcol_mgrs rrDataFrame._ixss 19ii''*GgmmRZZ8WW]]=O=OSW=WD66w\\6RF::a=FL((.F     0MLLOEiinnQ'G))'5F  ! !% .Mrc8URRU5$)z Get the values of the i'th column (ndarray or ExtensionArray, as stored in the Block) Warning! The returned array is a view but doesn't handle Copy-on-Write, so this should be used with caution (for read-only purposes). )r iget_valuesrrs r_get_column_arrayDataFrame._get_column_arraysyy$$Q''rc## [UR[5(aURRShvN g[ [ UR 55HnURU5v M gN>7f)a Iterate over the arrays of all columns in order. This returns the values as stored in the Block (ndarray or ExtensionArray). Warning! The returned array is a view but doesn't handle Copy-on-Write, so this should be used with caution (for read-only purposes). N)rrrorrrrrrs rrDataFrame._iter_column_arrayssY dii . .yy'' ' '3t||,-,,Q//. (s9A<A:?A<c URRUS5SnURUnURRUUSSSSS9nUR XDR S9nUR U5nU$)z\ Behaves like __getitem__, but returns a view in cases where __getitem__ would make a copy. rrrTF)r allow_dupsr only_slicer)r_get_indexer_strictrreindex_indexerrrr)rkeyindexernew_axisr rs r_getitem_nocopyDataFrame._getitem_nocopys ,,223 B1E<<())++   , ++G,,+G$$T* rc[U5 [R"U5n[R"X5n[ U5(a[ U5(d[UR[5nU(dWURR(aXR;dXRRSS9;aURU5$U(a;URR(a XR;aURU5$[U[5(aURU5$[U[ 5(aUR#U5$[R$"U5(aUR'U5$[U[(5=(d [+U5(+nU(aZURR,S:aURU5$URR/U5n[1U5(aU/nO:[ U5(a [3U5nURR5US5Sn[7USS5[8:Xa[:R""U5Sn[U[5(aUR=USS9$UR?USS9nU(aCUR@SS:Xa0[UR[5(dURU5$U$)NFkeeprrrrr)!rnritem_from_zerodimriapply_if_callablerArDrrrkrdrop_duplicatesr_getitem_multilevelslice_getitem_slicerwhereis_bool_indexer_getitem_bool_arraytuplerErgrQrBrrrr#r_slice_take_with_is_copyrC)rris_mi is_single_keyrrs r __getitem__DataFrame.__getitem__sM"3'##C(##C. s  K$4$4t||Z8E &&<<',,66E6BB++C004<<11c\\6I//44 c5 ! !&&s+ + c9 % %::c? "   s # #++C0 0#3.Gl36G2G ||##a'//44ll**3/G'""")33ill66sIFqIG 7GT *d 2hhw'*G gu % %;;wQ;/ /&&wQ&7  zz!}!*T\\:*N*N++C00 rc [U[5(aMURRUR5(d#[R "S[ [5S9 OP[U5[UR5:wa.[S[U5S[UR5S35e[URU5nUR5(aURSS9$UR5SnURUSS9$) Nz>Boolean Series key will be reindexed to match DataFrame index.rItem wrong length instead of rrrr)rr}requalsrr UserWarningr+rrrmallrnonzeror0)rrrs rr-DataFrame._getitem_bool_array"s c6 " "399+;+;DJJ+G+G MMP+-  XTZZ ($SXJl3tzz?:K1M  !S1 779999$9' '++-"&&wQ&77rc&URRU5n[U[[R 45(aURUn[ X15nURSS2U4nXEl[UR5S:XacURSn[U[5(aUSnUS:Xa4USn[U[5(aURXPRUS9nURU5 U$URUSS9$)Nrrrr)rrQrr)rrrlrrr.r}rrr r)rrloc new_columnsresult_columnsrtops rr(DataFrame._getitem_multilevel=sll""3' cE2::. / /,,s+K-k?NYYq#v&F+N6>>"a'nnQ'c5))a&C"9#BZF!&&11!%!9!9"**3":"    %M99Sq9) )rcnU(aURUSS9nURU$URU5nURRn[ UR[ 5(d*URRU5nURU$URU5nURU$)aS Quickly retrieve single value at passed column and index. Parameters ---------- index : row label col : column label takeable : interpret the index/col as indexers, default False Returns ------- scalar Notes ----- Assumes that both `self.index._index_as_unique` and `self.columns._index_as_unique`; Caller is responsible for checking. rr)rrRrr_enginerrkrQ)rrr_takeableseriesrr!r?s r _get_valueDataFrame._get_value_s& YYsY+F>>%( (%%c*##$**j11**$$U+C>>#& &nnU#~~c""rc[U[5(a[U5(aU/n[U5[UR5:wa.[ S[U5S[UR5S35e[ U5H:up4URURSS2U45upVURXESUS9 M< gURU5upVURXSUS9 g)a Set the given value in the column with position `loc`. This is a positional analogue to ``__setitem__``. Parameters ---------- loc : int or sequence of ints Index position for the column. value : scalar or arraylike Value(s) for the column. Notes ----- ``frame.isetitem(loc, value)`` is an in-place method as it will modify the DataFrame in place (not returning a new object). In contrast to ``frame.iloc[:, i] = value`` which will try to update the existing values in place, ``frame.isetitem(loc, value)`` will not update the values of the column itself in place, it will instead insert a new array. In cases where ``frame.columns`` is unique, this is equivalent to ``frame[frame.columns[i]] = value``. zGot z positions but value has z columns.NFrrefs) rrrBrrrr_sanitize_columnr_iset_item_mgr)rr?rqridx arraylikerLs risetitemDataFrame.isetitems0 eY ' '#e3x3u}}-- 3s8*$=c%-->P=QR  $C."&"7"7 1a48H"I ##CE#M) //6  CEErc[(dH[5(a9[R"U5S::a[R "[ [SS9 O[(d[5(d[R"U5S::ag[5(d:[5(dI[SURR55(a[R "[[SS9 [R"X5n[!U["5(a+UR$R'USS9nUR)X25$[!U[*5(d[-USS5S:XaUR/X5 g[!U[0[2R4[6[845(aUR;X5 g[!U[*5(aUR=X5 g[?U5(akUR@RB(dPS[EUR@RGU/55s=:a[EU5:XaO OUR;U/U5 gURIX5 g) Nrrc3T# UHoRR5v M g7fr)rL has_reference)r^bs rr`(DataFrame.__setitem__..s M ? ?    + y ) )  & &s 2   LL**C 44cU;<JE J   u - NN3 &rc@UR5 X RU'gr)_check_setitem_copyrrrrqs rr^DataFrame._setitem_slices   " #rc[R"U5(a[U5[UR5:wa.[ S[U5S[UR5S35e[ URU5nUR 5SnUR5 [U[5(a*URURRU55nX RU'g[U[5(a;[URX5 [XR5H upEX%X'M g[!U5(dUHnX U'M g[U["R$5(a"UR&S:XaUR)X5 g["R&"U5S:a&[U5R*nUR-X5$UR)X5 g)Nr6r7!rrr)rir,rrrrmr;rhrrrtakerrdrrrErrr_iset_not_inplacer r`)rrrqrk1k2r_s rr`DataFrame._setitem_arraysz   s # #3x3tzz?* (S ,s4::>OqQ%TZZ5CkkmA&G  $ $ &%++ djjoog&>?!&IIg  %++ s:!#}}5FB$yDH6"%((C %IE2::..5::?&&s2!#!%(//**366&&s2rcSSjnURR(aP[R"U5S[ U5:wa [ S5e[ U5HupEU"X$5X'M gURRU5SnUS:R5(a[e[R"U5S[ U5:wa [ S5e[R"U5S::deURn[[[ UR555Ul[ U5HupHU"X$5X'M Xplg!Xplf=f)NcV[U[R5(aUSU4$X$)N.)rrr)objrs rigetitem-DataFrame._iset_not_inplace..igetitems*#rzz**36{"v rrN"Columns must be same length as keyrr)rint) rrrrCrrrget_indexer_non_uniquerUNotImplementedErrorrrfr) rrrqrurr_ilocs orig_columnsrs rrnDataFrame._iset_not_inplaces  << ! !xxr"c#h. !EFF#C.$U. )LL77Q& &&< (/GA!)%!3DJ 0 , | s A EE c[U[R5(aJURUR:wa [ S5eUR "U40UR 5DSS0D6nUR(a,[SUR55(d [S5eUR5 URU*USS9 g)Nz,Array conditional must be same shape as selfrFc38# UHn[U5v M g7fr)r<)r^rs rr`+DataFrame._setitem_frame..<sM* e 4 4*rbz;Must pass DataFrame or 2-d ndarray with boolean values onlyTr) rrrrCrr_construct_axes_dictsizer:rr rh_whereris rr_DataFrame._setitem_frame4s c2:: & &yyDJJ& !OPP##CS4+D+D+FSUSC 88CM#**MMMM    " SD% .rcURU5 XR;GaURRU5nURUn[U5(d[ U[ 5(aSO [ U5nU[ UR5:wa [S5e[ UR[5(a[ U[[[R[45(af[XA5n[ U5(a0URUR5(dUR!USS9n[#XF5H upxX(X'M g[U5(aX"RSX'g[ U[5(a7[R$"UR&UR(UR*5n O'[U5(aU/n OUR-5Sn UR/X5$[ UR5S:a[SU35e[ UR5S:Xa[SU35eX"RSX'g)NrrwrrzBCannot set a DataFrame with multiple columns to the single column z5Cannot set a DataFrame without columns to the column )_ensure_valid_indexrrQrFrr.rrrkr)r}rrrfrlr8rrarangestartstopstepr;rQ) rrrqr?colslen_colscols_droplevelr_ col_droplevellocss rraDataFrame._set_item_frame_valueDs   ' ,, ,,&&s+C<<$D%dOOz$/F/FqCPTIH3u}}-- !EFF$,, 33 eVRZZ799"2$!<~&&~/D/DU]]/S/S!MM.qMAE*-d*C&C % 4DI+D"==#34 #u%%yyCHHchh?3u{{}Q'==- - u}}  !   1 $GuM --*+ rcXURRXX4S9 UR5 g)NrK)riset_clear_item_cache)rr?rqrrLs rrNDataFrame._iset_item_mgrvs$ s7>  rcURRU5nURXBUS9 [ U5(aUR5 gg![a3 URR [ UR5XU5 N^f=f)NrL) _info_axisrQrNrTrrrrh)rrrqrLr?s r _set_item_mgrDataFrame._set_item_mgrs~ 7//))#.C     6 t99  $ $ &  E II  S13t D EsA:B  B c[5(a%URXRX2RS9 O(URXRR 5SS9 [ U5(aUR 5 gg)NrKTr)rrNrR _referencesrrrh)rr?rqrs r _iset_itemDataFrame._iset_itemsm    ]]G:K:K     ]]%7%7%94  H t99  $ $ & rcURU5up#XR;aURS:Xa[UR[ 5(dURR (a[UR[5(aQXn[U[5(a8[R"U[UR5S45RnSnURXU5 g)a Add series to DataFrame in specified column. If series is a numpy-array (not a Series/TimeSeries), it must be the same length as the DataFrames index or an error will be thrown. Series/TimeSeries will be conformed to the DataFrames index to ensure homogeneity. rN)rMrrrrrMrrkrrtilerrQr)rrrqrLexisting_pieces rrcDataFrame._set_items++E2  <<  au{{N;;<<))Z j-Q-Q!%ni88GGEC0F0F,G+KLNNED 3t,rc U(aUn[[U5nO6URRU5nURRU5nUR R XVUSS9 UR5 g![[[[4aE U(aX0RX4'OX0RX4'URRUS5 g[ an[!S[#U535UeSnAff=f)z Put single value at passed column and index. Parameters ---------- index : Label row label col : Label column label value : scalar takeable : bool, default False Sets whether or not index/col interpreted as indexers T) inplace_onlyNz)You can only assign a scalar value not a )rrxrrQrrcolumn_setitemrrTr rr0rr?rpopr"r)rrr_rqrFicoliindexii_errs r _set_valueDataFrame._set_values  c5)||++C0++E2 II $ $T5t $ L  " " $)Z1BC , (- %*%',$    d +  $;DK=I  sA;A>>AD D%C==Dc[UR5(d[U5(a[U5(a[U[5(d [ U5nURR5nURRbURRUl URRUS[RS9Ul gggg![ [[4an[ S5UeSnAff=f)zV Ensure that if we don't have an index, that we can create one from the passed value. zYCannot set a frame with no defined index and a value that cannot be converted to a SeriesNr)r fill_value)rrrErrr}rrzr rrr reindex_axisrnan)rrqr index_copys rrDataFrame._ensure_valid_indexs 4::<#6#63u::eY//"5ME))+Jzz*"&**//  ..zbff.UDI`. *Backtick quoted variables* Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems. During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped). In a special case, quotes that make a pair around a backtick can confuse the parser. For example, ```it's` > `that's``` will raise an error, as it forms a quoted string (``'s > `that'``) with a backtick inside. See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in :mod:`pandas.core.computation.parsing`. Examples -------- >>> df = pd.DataFrame({'A': range(1, 6), ... 'B': range(10, 0, -2), ... 'C C': range(10, 5, -1)}) >>> df A B C C 0 1 10 10 1 2 8 9 2 3 6 8 3 4 4 7 4 5 2 6 >>> df.query('A > B') A B C C 4 5 2 6 The previous expression is equivalent to >>> df[df.A > df.B] A B C C 4 5 2 6 For columns with spaces in their name, you can use backtick quoting. >>> df.query('B == `C C`') A B C C 0 1 10 10 The previous expression is equivalent to >>> df[df.B == df['C C']] A B C C 0 1 10 10 rz'expr must be a string to be evaluated, z givenlevelrrNtarget) r.rrrrrevalr?_update_inplace)rrrrrrrs rrrHsR&gy9$$$;DJ`. Examples -------- >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)}) >>> df A B 0 1 10 1 2 8 2 3 6 3 4 4 4 5 2 >>> df.eval('A + B') 0 11 1 10 2 9 3 8 4 7 dtype: int64 Assignment is allowed though by default the original DataFrame is not modified. >>> df.eval('C = A + B') A B C 0 1 10 11 1 2 8 10 2 3 6 9 3 4 4 8 4 5 2 7 >>> df A B 0 1 10 1 2 8 2 3 6 3 4 4 4 5 2 Multiple columns can be assigned to using multi-line expressions: >>> df.eval( ... ''' ... C = A + B ... D = A - B ... ''' ... ) A B C D 0 1 10 11 -9 1 2 8 10 -6 2 3 6 9 -3 3 4 4 8 0 4 5 2 7 3 r)rrrrr resolversr{)pandas.core.computation.evalrr.r_get_index_resolvers_get_cleaned_column_resolversr.r)rrrr_evalindex_resolverscolumn_resolversrs rrrsx ?%gy9 **Wa014w335==?$5 6 !#8 #FJJ{B$?@9L{T575f55rc(^^^[T5(dTbT4OSm[T5(dTbT4OSm[T5[T54n[U5(d [S5eSnU"T5mU"T5mTT4Hn[ U5 M TR T5(d[STT-35eS SjmS UUU4SjjnUR RU5RSS9nURXwRS 9RU5$) au Return a subset of the DataFrame's columns based on the column dtypes. Parameters ---------- include, exclude : scalar or list-like A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied. Returns ------- DataFrame The subset of the frame including the dtypes in ``include`` and excluding the dtypes in ``exclude``. Raises ------ ValueError * If both of ``include`` and ``exclude`` are empty * If ``include`` and ``exclude`` have overlapping elements * If any kind of string dtype is passed in. See Also -------- DataFrame.dtypes: Return Series with the data type of each column. Notes ----- * To select all *numeric* types, use ``np.number`` or ``'number'`` * To select strings you must use the ``object`` dtype, but note that this will return *all* object dtype columns * See the `numpy dtype hierarchy `__ * To select datetimes, use ``np.datetime64``, ``'datetime'`` or ``'datetime64'`` * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or ``'timedelta64'`` * To select Pandas categorical dtypes, use ``'category'`` * To select Pandas datetimetz dtypes, use ``'datetimetz'`` or ``'datetime64[ns, tz]'`` Examples -------- >>> df = pd.DataFrame({'a': [1, 2] * 3, ... 'b': [True, False] * 3, ... 'c': [1.0, 2.0] * 3}) >>> df a b c 0 1 True 1.0 1 2 False 2.0 2 1 True 1.0 3 2 False 2.0 4 1 True 1.0 5 2 False 2.0 >>> df.select_dtypes(include='bool') b 0 True 1 False 2 True 3 False 4 True 5 False >>> df.select_dtypes(include=['float64']) c 0 1.0 1 2.0 2 1.0 3 2.0 4 1.0 5 2.0 >>> df.select_dtypes(exclude=['int64']) b c 0 True 1.0 1 False 2.0 2 True 1.0 3 False 2.0 4 True 1.0 5 False 2.0 Nr{z3at least one of include or exclude must be nonemptyc/nUHn[U[5(aUS:Xd U[La@UR[R 5 UR[R 5 MgUS:Xd U[La1UR[R[R/5 MUR[U55 M [U5$)Nrxfloat) rrrxrrint32int64rrfloat64float32r9 frozenset)rconverted_dtypesrs rcheck_int_infer_dtype6DataFrame.select_dtypes..check_int_infer_dtypes+- uc**u~5C<$++BHH5$++BHH5g%%$++RZZ,DE$++,CE,JK -. .rzinclude and exclude overlap on c[U[5(dUO URn[UR[ U55=(d> [ RU;=(a$ [USS5=(a [U5(+$)N _is_numericF) rrK numpy_dtype issubclassrr.rnumberrr<)r dtypes_sets rdtype_predicate0DataFrame.select_dtypes..dtype_predicatese!+E:!>!>EEDUDUEejj% *;< Z'-E=%8-%e,, rcv>URnT(aT"UT5(dgT(aT"UT5(agg)NFTr)rFrrrVincludes r predicate*DataFrame.select_dtypes..predicates5IIE&ug66 "5'22 rrr)rrrIr#)rFrrIr#) rErrUrr6 isdisjointr_get_data_subsetrrrr) rrrV selectionrrrrrs `` @r select_dtypesDataFrame.select_dtypesWsfG$$$+$7wjRGG$$$+$7wjRGw'7);< 9~~RS S /(0'0(F $V ,)!!'**>'@Q>STU U   ii((388d8C))#HH)=JJ4PPrchU[RLaSnU(a&URR(d [ S5eU(dX R ;a[ SUS35e[ U5(d [S5e[U5n[U[5(a;[UR 5S:a"[ S[UR 5S35e[U[5(aURS S 2S 4nURU5up5URRXX5S 9 g ) a Insert column into DataFrame at specified location. Raises a ValueError if `column` is already contained in the DataFrame, unless `allow_duplicates` is set to True. Parameters ---------- loc : int Insertion index. Must verify 0 <= loc <= len(columns). column : str, number, or hashable object Label of the inserted column. value : Scalar, Series, or array-like Content of the inserted column. allow_duplicates : bool, optional, default lib.no_default Allow duplicate column labels to be created. See Also -------- Index.insert : Insert new item by index. Examples -------- >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df col1 col2 0 1 3 1 2 4 >>> df.insert(1, "newcol", [99, 99]) >>> df col1 newcol col2 0 1 99 3 1 2 99 4 >>> df.insert(0, "col1", [100, 100], allow_duplicates=True) >>> df col1 col1 newcol col2 0 100 1 99 3 1 100 2 99 4 Notice that pandas uses index alignment in case of `value` from type `Series`: >>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2])) >>> df col0 col1 col1 newcol col2 0 NaN 100 1 99 3 1 5.0 100 2 99 4 FzZCannot specify 'allow_duplicates=True' when 'self.flags.allows_duplicate_labels' is False.zcannot insert z, already existszloc must be intrz8Expected a one-dimensional object, got a DataFrame with z columns instead.Nrr)r no_defaultflagsallows_duplicate_labelsrrrBr rxrrrrrMrr)rr?rdrqallow_duplicatesrLs rrDataFrame.insertsl s~~ -$  DJJ$F$FA  Fll$:~fX5EFG G#-. .#h eY ' 'C ,>,BJu}}%&&79 y ) )JJq!t$E++E2  e7rc URSS9nUR5Hup4[R"XB5X#'M U$)a* Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. Parameters ---------- **kwargs : dict of {str: callable or Series} The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn't check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned. Returns ------- DataFrame A new DataFrame with the new columns in addition to all the existing columns. Notes ----- Assigning multiple columns within the same ``assign`` is possible. Later items in '\*\*kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order. Examples -------- >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]}, ... index=['Portland', 'Berkeley']) >>> df temp_c Portland 17.0 Berkeley 25.0 Where the value is a callable, evaluated on `df`: >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32) temp_c temp_f Portland 17.0 62.6 Berkeley 25.0 77.0 Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence: >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32) temp_c temp_f Portland 17.0 62.6 Berkeley 25.0 77.0 You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign: >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32, ... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9) temp_c temp_f temp_k Portland 17.0 62.6 290.15 Berkeley 25.0 77.0 298.15 Nr)rrrir&)rrrrrs rassignDataFrame.assign6s>|yydy#LLNDA++A4DG# rc0URU5 [U[5(ae[U5(a5[U[5(d [ U5n[ XR 5$[U5(a [R"XR 5 [XR SSS9n[U[5(aLURS:Xa<URUR:wa"[R"S[[!5S9 US4$)a; Ensures new columns (which go into the BlockManager as new blocks) are always copied (or a reference is being tracked to them under CoW) and converted into an array. Parameters ---------- value : scalar, Series, or array-like Returns ------- tuple of numpy.ndarray or ExtensionArray and optional BlockValuesRefs T)rallow_2drzSetting an Index with object dtype into a DataFrame will stop inferring another dtype in a future version. Cast the Index explicitly before setting it into the DataFrame.rN)rrrr>r}_reindex_for_setitemrrErirequire_length_matchr`rfrrrrr+)rrqrFs rrMDataFrame._sanitize_columnzs   'eY////   eV,,u 'zz: :     $ $UJJ 7UJJTDI ue $ $ x' U[[( MMC+-  Dyrc ~[UR5VVs0sHupX RUSS9_M snn$s snnf)Nrr)rrr)rrOrs r_seriesDataFrame._seriess7>G >UV>Uii!i,,>UVVVs9c URRUS5upEURRUS5upgUb+Ub(XW4n[URXS9n UR XUSS9$UR XE/Xg/S.UUS9$)z* We are guaranteed non-Nones in the axes. rr)rFrrr)rr)rr)rrrrWr r_reindex_with_indexers) rrrr new_index row_indexerr@ col_indexerrrs r_reindex_multiDataFrame._reindex_multis"&!3!3DM!B #'<<#7#7Y#H  "{'> ".G&t{{GSJ$$[u% ..,+1KL%/ ra Examples -------- >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) Change the row labels. >>> df.set_axis(['a', 'b', 'c'], axis='index') A B a 1 4 b 2 5 c 3 6 Change the column labels. >>> df.set_axis(['I', 'II'], axis='columns') I II 0 1 4 1 2 5 2 3 6 rz column orz, and 1 identifies the columnsz or columns)rrextended_summary_subaxis_description_sub see_also_subrrc >[TU]XUS9$)Nr)superset_axis)rlabelsrr __class__s rrDataFrame.set_axissLw==rr)rr) rrrrrrrlimit tolerancec 0>[T U]UUUUUUUUU U S9 $)N) rrrrrrrrrr)rr) rrrrrrrrrrrrs rrDataFrame.reindexs9&w!  r)rrrrrcgrr{rrrrrrrrs rrKDataFrame.drop r)rrrrrrcgrr{r s rrKr!r"rcgrr{r s rrKr!+r"rraisec *>[TU]UUUUUUUS9$)a Drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the :ref:`user guide ` for more information about the now unused levels. Parameters ---------- labels : single label or list-like Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like. axis : {0 or 'index', 1 or 'columns'}, default 0 Whether to drop labels from the index (0 or 'index') or columns (1 or 'columns'). index : single label or list-like Alternative to specifying axis (``labels, axis=0`` is equivalent to ``index=labels``). columns : single label or list-like Alternative to specifying axis (``labels, axis=1`` is equivalent to ``columns=labels``). level : int or level name, optional For MultiIndex, level from which the labels will be removed. inplace : bool, default False If False, return a copy. Otherwise, do operation in place and return None. errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and only existing labels are dropped. Returns ------- DataFrame or None Returns DataFrame or None DataFrame with the specified index or column labels removed or None if inplace=True. Raises ------ KeyError If any of the labels is not found in the selected axis. See Also -------- DataFrame.loc : Label-location based indexer for selection by label. DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. DataFrame.drop_duplicates : Return DataFrame with duplicate rows removed, optionally only considering certain columns. Series.drop : Return Series with specified index labels removed. Examples -------- >>> df = pd.DataFrame(np.arange(12).reshape(3, 4), ... columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 Drop columns >>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11 >>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11 Drop a row by index >>> df.drop([0, 1]) A B C D 2 8 9 10 11 Drop columns and/or rows of MultiIndex DataFrame >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> df = pd.DataFrame(index=midx, columns=['big', 'small'], ... data=[[45, 30], [200, 100], [1.5, 1], [30, 20], ... [250, 150], [1.5, 0.8], [320, 250], ... [1, 0.8], [0.3, 0.2]]) >>> df big small llama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 weight 1.0 0.8 length 0.3 0.2 Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination ``'falcon'`` and ``'weight'``, which deletes only the corresponding row >>> df.drop(index=('falcon', 'weight')) big small llama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 length 0.3 0.2 >>> df.drop(index='cow', columns='small') big llama speed 45.0 weight 200.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 >>> df.drop(index='length', level=1) big small llama speed 45.0 30.0 weight 200.0 100.0 cow speed 30.0 20.0 weight 250.0 150.0 falcon speed 320.0 250.0 weight 1.0 0.8 )rrrrrrr)rrK) rrrrrrrrrs rrKr!9s1hw|  r)rrrrrrcgrr{ rmapperrrrrrrrs rrDataFrame.rename r)rrrrrrrcgrr{r(s rrr*r+rcgrr{r(s rrr*r+rrc ,>[T U]UUUUUUUUS9$)aE Rename columns or index labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. See the :ref:`user guide ` for more. Parameters ---------- mapper : dict-like or function Dict-like or function transformations to apply to that axis' values. Use either ``mapper`` and ``axis`` to specify the axis to target with ``mapper``, or ``index`` and ``columns``. index : dict-like or function Alternative to specifying axis (``mapper, axis=0`` is equivalent to ``index=mapper``). columns : dict-like or function Alternative to specifying axis (``mapper, axis=1`` is equivalent to ``columns=mapper``). axis : {0 or 'index', 1 or 'columns'}, default 0 Axis to target with ``mapper``. Can be either the axis name ('index', 'columns') or number (0, 1). The default is 'index'. copy : bool, default True 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 Whether to modify the DataFrame rather than creating a new one. If True then value of copy is ignored. level : int or level name, default None In case of a MultiIndex, only rename labels in the specified level. errors : {'ignore', 'raise'}, default 'ignore' If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`, or `columns` contains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored. Returns ------- DataFrame or None DataFrame with the renamed axis labels or None if ``inplace=True``. Raises ------ KeyError If any of the labels is not found in the selected axis and "errors='raise'". See Also -------- DataFrame.rename_axis : Set the name of the axis. Examples -------- ``DataFrame.rename`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` We *highly* recommend using keyword arguments to clarify your intent. Rename columns using a mapping: >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) >>> df.rename(columns={"A": "a", "B": "c"}) a c 0 1 4 1 2 5 2 3 6 Rename index using a mapping: >>> df.rename(index={0: "x", 1: "y", 2: "z"}) A B x 1 4 y 2 5 z 3 6 Cast index labels to a different type: >>> df.index RangeIndex(start=0, stop=3, step=1) >>> df.rename(index=str).index Index(['0', '1', '2'], dtype='object') >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise") Traceback (most recent call last): KeyError: ['C'] not found in axis Using axis-style parameters: >>> df.rename(str.lower, axis='columns') a b 0 1 4 1 2 5 2 3 6 >>> df.rename({1: 2, 2: 4}, axis='index') A B 0 1 4 2 2 5 4 3 6 )r)rrrrrrr)r_rename) rr)rrrrrrrrs rrr*s4Fw  rc>[TU]US9$)a! Return item and drop from frame. Raise KeyError if not found. Parameters ---------- item : label Label of column to be popped. Returns ------- Series 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')) >>> df name class max_speed 0 falcon bird 389.0 1 parrot bird 24.0 2 lion mammal 80.5 3 monkey mammal NaN >>> df.pop('class') 0 bird 1 bird 2 mammal 3 mammal Name: class, dtype: object >>> df name max_speed 0 falcon 389.0 1 parrot 24.0 2 lion 80.5 3 monkey NaN )r)rr)rrrs rr DataFrame.popsRw{{%%rc$U(aUOURSS9nURn[U5HEupgXq;dM URSS2U4nXupUR XUS9n UR XkUS9 MG U(agUR U5$)z Dispatch to Series.replace column-wise. Parameters ---------- mapping : dict of the form {col: (target, value)} inplace : bool regex : bool or same types as `to_replace` in DataFrame.replace Returns ------- DataFrame or None Nr)regexr)rrrrrrr) rmappingrr3raxrax_valuerrrqnewobjs r_replace_columnwiseDataFrame._replace_columnwises$d499$9#7 \\$R=KA"ii1o ' 1 V%@q':)  %%r)rrc >UbEU[RLa2[R"S[[ 5S9 [RnUR (aUR5$URU5n[U5(a[[U5nUS:Xa [S5e[U5S:Xa [S5eSSKJn /nUH{n[!U5(d[#SUS [%U5S 35e[[&U5nUR)[*TU]YXX4S 9R/U(aUS U3OS U355 M} U"USS 9$U(a [S5e[[&U5n[UR05n UR2R4n US:XGaUS:wGaU S:Ga~UGczU[RLGa8UR0Sn US:axUR6SS2SU*24n [9[;U [=U555H?n UR6SS2S4R-[U55nU R?SXSS9 MA OUR6SS2U*S24n [9[;U [=U555HRn UR6SS2S4R-[U55nU R?[U R05XSS9 MT UR0R5U lU $[U 5S:d[AU SU5(d[=U5n[;X5nUS:a?[BRD"S/U-[G[9X- 55-[BRHS9nO<[BRD"[G[9X55S/U--[BRHS9nUR2RKUR0USUSS9nURMUURNS9nURQUSS9$URRR-XS9RR$[*TU]YXX4S 9$)NzPassing a 'freq' together with a 'fill_value' silently ignores the fill_value and is deprecated. This will raise in a future version.rrz:If `periods` contains multiple shifts, `axis` cannot be 1.rz0If `periods` is an iterable, it cannot be empty.concatzPeriods must be integer, but z is r)periodsfreqrrrrz/Cannot specify `suffix` if `periods` is an int.TrrNr)rrrrshiftr)r=r)*rrrrrr+emptyr_get_axis_numberrErr rrpandas.core.reshape.concatr<rBr rrxrrr@ add_suffixrrrrrrgabsrr1rrrrrrrrrQ)rr=r>rrsuffixr<shifted_dataframesperiodncolsrrrr_fillernperrrres_dfrs rr@DataFrame.shifts   #.. @ MM+-  J ::99; $$T*  8W-Gqy P7|q  !STT 9!# !!&))#7xtDL>QRSc6*"))G#)4UWZ6(!F8 4axLQ ",15 5 NO OsG$DLL!!! 19A%!) S^^+ QQ;!YYq)G8)|4F$SG %=>"&1a4!6!6s4y!A a N ? "YYq7()|4F$SG %=>!%1b5!1!7!7D !B /QU& ?"&!2!2!4 Vq %VAY ;;7|4'Q; hht d5+A&BB"''G!hhU4/0B4$;>bggGii//LL)# 033Cchh3G**4*@@vv||G|KMMMw}T  r)rKrrrycgrr{rrrKrrrys rrJDataFrame.set_indexQ r)rKrrycgrr{rOs rrJrP]rQrc ^^[US5nTRU5 [U[5(dU/nSn/nUHm[T[[ [ R[[R45(a[TSS5S:wa [U5eM_TTR;nU(dURT5 MM U(a[!SUS35eU(aTn OTR#SS 9n /n /n U(a[TR$R&5n [TR$[(5(a9U R+U4S j[-TR$R.555 OU RTR$5 /n UGHm[T[(5(aKU R+U4S j[-TR.555 U R+TR&5 GO[T[[ 45(a-U RT5 U RTR05 O[T[[ R45(a#U RT5 U RS5 O[T[R5(a,U R[T55 U RS5 O=U RU T5 U RT5 U(aU RT5 [3U S 5[3T5:wdGM[S [3T5S[3U S 535e [5X5nU(a?UR6(d.XR95R;5n[SU35e[=U 5HnU U M UR?5 XlU(dU $g![an [US[T535U eSn A ff=f)af Set the DataFrame index using existing columns. Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it. Parameters ---------- keys : label or array-like or list of labels/arrays This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, "array" encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and instances of :class:`~collections.abc.Iterator`. drop : bool, default True Delete columns to be used as the new index. append : bool, default False Whether to append columns to existing index. inplace : bool, default False Whether to modify the DataFrame rather than creating a new one. verify_integrity : bool, default False Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method. Returns ------- DataFrame or None Changed row labels or None if ``inplace=True``. See Also -------- DataFrame.reset_index : Opposite of set_index. DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindex_like : Change to same indices as other DataFrame. Examples -------- >>> df = pd.DataFrame({'month': [1, 4, 7, 10], ... 'year': [2012, 2014, 2013, 2014], ... 'sale': [55, 40, 84, 31]}) >>> df month year sale 0 1 2012 55 1 4 2014 40 2 7 2013 84 3 10 2014 31 Set the index to become the 'month' column: >>> df.set_index('month') year sale month 1 2012 55 4 2014 40 7 2013 84 10 2014 31 Create a MultiIndex using columns 'year' and 'month': >>> df.set_index(['year', 'month']) sale year month 2012 1 55 2014 4 40 2013 7 84 2014 10 31 Create a MultiIndex using an Index and a column: >>> df.set_index([pd.Index([1, 2, 3, 4]), 'year']) month sale year 1 2012 1 55 2 2014 4 40 3 2013 7 84 4 2014 10 31 Create a MultiIndex using two Series: >>> s = pd.Series([1, 2, 3, 4]) >>> df.set_index([s, s**2]) month year sale 1 1 1 2012 55 2 4 4 2014 40 3 9 7 2013 84 4 16 10 2014 31 rzThe parameter "keys" may be a column key, one-dimensional array, or a list containing only valid column keys and one-dimensional arrays.rrz. Received column of type NzNone of z are in the columnsrc3Z># UH nTRRU5v M" g7fr)r_get_level_values)r^rrs rr`&DataFrame.set_index..s'=VDJJ0033=Vs(+c3F># UHnTRU5v M g7fr)rU)r^nr_s rr`rVs!S@R1c33A66@Rs!rNzLength mismatch: Expected z rows, received array of length zIndex has duplicate keys: ) r.*_check_inplace_and_allows_duplicate_labelsrrrfr}rrrrrrrrr rrTrrr rkrrrgrrrjrruniquer_cleanup)rrrKrrryerr_msgmissingfoundrframerr r`r duplicatesrqr_s` @rrJrPisRD&gy9 77@$%%6D &  #%C#vrzz4NOO3*a/$W--0,4<</E !s+!$ XgY.ABC C EII4I(E  " ))*E$**j11 =B4::CUCU=V djj)$& C#z** Sckk@RSS SYY'C%11  c" SXX&C$ !344 c" T"C.. d3i( T" eCj) S!$$S)6":#d)+!0T <003F2J/@B?H,F: EOO//1299;J9*FG GYAa   Le!#")#=d3i[IsO## P -PP )rKr col_levelcol_fillrr cgrr{rrrKrrarbrr s r reset_indexDataFrame.reset_index5r"r)rKrarbrr cgrr{rds rrerfCr"rcgrr{rds rrerfQr"rr>c@[US5nURU5 U(aUnOURSS9nU[RLa [US5n[ [ U55n Ub[U[[45(dU/nUV s/sHoRRU 5PM nn [ U5URR:aURRU5n U(GdSU;aSOSn URRX{5n[UR[5(a5[!URR"URR$5n OURS44n [UR&[5n [)[[+U 555GHLunupUbX;aMX~nU (a[U[5(a [U5OU/nUc8[ U5SUR&R4;a[-SU35eUS nUR&RU5nU/U-U-nUR&R[ U5- nUU/U-- n[U5nU R.nUR0[2R4:Xa[R6"U5nUb![8R:"UUS U R<S 9nUR?S UUUS 9 GMO Xl U(dU$gs sn f) a* Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels. Parameters ---------- level : int, str, tuple, or list, default None Only remove the given levels from the index. Removes all levels by default. drop : bool, default False Do not try to insert index into dataframe columns. This resets the index to the default integer index. inplace : bool, default False Whether to modify the DataFrame rather than creating a new one. col_level : int or str, default 0 If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level. col_fill : object, default '' If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated. allow_duplicates : bool, optional, default lib.no_default Allow duplicate column labels to be created. .. versionadded:: 1.5.0 names : int, str or 1-dimensional list, default None Using the given string, rename the DataFrame column which contains the index data. If the DataFrame has a MultiIndex, this has to be a list or tuple with length equal to the number of levels. .. versionadded:: 1.5.0 Returns ------- DataFrame or None DataFrame with the new index or None if ``inplace=True``. See Also -------- DataFrame.set_index : Opposite of reset_index. DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindex_like : Change to same indices as other DataFrame. Examples -------- >>> df = pd.DataFrame([('bird', 389.0), ... ('bird', 24.0), ... ('mammal', 80.5), ... ('mammal', np.nan)], ... index=['falcon', 'parrot', 'lion', 'monkey'], ... columns=('class', 'max_speed')) >>> df class max_speed falcon bird 389.0 parrot bird 24.0 lion mammal 80.5 monkey mammal NaN When we reset the index, the old index is added as a column, and a new sequential index is used: >>> df.reset_index() index class max_speed 0 falcon bird 389.0 1 parrot bird 24.0 2 lion mammal 80.5 3 monkey mammal NaN We can use the `drop` parameter to avoid the old index being added as a column: >>> df.reset_index(drop=True) class max_speed 0 bird 389.0 1 bird 24.0 2 mammal 80.5 3 mammal NaN You can also use `reset_index` with `MultiIndex`. >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'), ... ('bird', 'parrot'), ... ('mammal', 'lion'), ... ('mammal', 'monkey')], ... names=['class', 'name']) >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'), ... ('species', 'type')]) >>> df = pd.DataFrame([(389.0, 'fly'), ... (24.0, 'fly'), ... (80.5, 'run'), ... (np.nan, 'jump')], ... index=index, ... columns=columns) >>> df speed species max type class name bird falcon 389.0 fly parrot 24.0 fly mammal lion 80.5 run monkey NaN jump Using the `names` parameter, choose a name for the index column: >>> df.reset_index(names=['classes', 'names']) classes names speed species max type 0 bird falcon 389.0 fly 1 bird parrot 24.0 fly 2 mammal lion 80.5 run 3 mammal monkey NaN jump If the index has multiple levels, we can reset a subset of them: >>> df.reset_index(level='class') class speed species max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump If we are not dropping the index, by default, it is placed in the top level. We can place it in another level: >>> df.reset_index(level='class', col_level=1) speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump When the index is inserted under another level, we can specify under which one with the parameter `col_fill`: >>> df.reset_index(level='class', col_level=1, col_fill='species') species speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump If we specify a nonexistent level for `col_fill`, it is created: >>> df.reset_index(level='class', col_level=1, col_fill='genus') genus speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump rNrrrlevel_0rz:col_fill=None is incompatible with incomplete column name rT allow_fillrr?) r.rYrrrrhrrr.rr_get_level_numberrg droplevel_get_default_index_namesrkrlevelscodesrreversedrrrRrrrrPrPrm _na_valuer)rrrKrrarbrr new_objr levdefault to_insert multi_colrlabrcol_namelev_numname_lstr] level_valuess rrerf_sZ&gy9 77@ GiiTi*G 3>> 123CEWX !#g,/  eeT]33BGH%3ZZ11#6%EH5zDJJ... JJ007 ")!4g)GJJ77GE$**j11 1 14::3C3CD "jj$/1 "4<<H"ll22S]BG W 44H ?D #{{ %%3#&#<#<\#JL?#-??$cds}}$L %5 ?"FL" NuIs$LcURR[S9nURXRS9nUR USS9$)N)funcrrNr)rrNrrr)rres_mgrrs rrNDataFrame.isnaXsC))..d.+++G,,+G""4"77rc"UR5$)z2 DataFrame.isnull is an alias for DataFrame.isna. rNrs risnullDataFrame.isnull^s yy{rc$UR5)$rrrs rrODataFrame.notnaes |rc$UR5)$)z4 DataFrame.notnull is an alias for DataFrame.notna. rrs rnotnullDataFrame.notnullis  |r)rhowthreshsubsetr ignore_indexcgrr{rrrrrrrs rdropnaDataFrame.dropnaprr)rrrrrcgrr{rs rrr}rrcU[RLaU[RLa [S5eU[RLaSn[US5n[ U[ [ 45(a [S5eURU5nSU- nUnUb[U5(dU/nURU5n U RU5n U S:Hn U R5(a0[[R"U5U R55eUR!XS9nU[RLaUR#US9n X:n ONUS:Xa[%U5R'US S 9n O.US :Xa[%U5RUS S 9n O[)S U35e[R&"U 5(aUR+SS 9nOUR-US9U nU(a[/[1U55UlU(dU$UR5U5 g)u Remove missing values. See the :ref:`User Guide ` for more on which values are considered missing, and how to work with missing data. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Determine if rows or columns which contain missing values are removed. * 0, or 'index' : Drop rows which contain missing values. * 1, or 'columns' : Drop columns which contain missing value. Only a single axis is allowed. how : {'any', 'all'}, default 'any' Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. * 'any' : If any NA values are present, drop that row or column. * 'all' : If all values are NA, drop that row or column. thresh : int, optional Require that many non-NA values. Cannot be combined with how. subset : column label or sequence of labels, optional Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include. inplace : bool, default False Whether to modify the DataFrame rather than creating a new one. ignore_index : bool, default ``False`` If ``True``, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 2.0.0 Returns ------- DataFrame or None DataFrame with NA entries dropped from it or None if ``inplace=True``. See Also -------- DataFrame.isna: Indicate missing values. DataFrame.notna : Indicate existing (non-missing) values. DataFrame.fillna : Replace missing values. Series.dropna : Drop missing values. Index.dropna : Drop missing indices. Examples -------- >>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], ... "toy": [np.nan, 'Batmobile', 'Bullwhip'], ... "born": [pd.NaT, pd.Timestamp("1940-04-25"), ... pd.NaT]}) >>> df name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Drop the rows where at least one element is missing. >>> df.dropna() name toy born 1 Batman Batmobile 1940-04-25 Drop the columns where at least one element is missing. >>> df.dropna(axis='columns') name 0 Alfred 1 Batman 2 Catwoman Drop the rows where all elements are missing. >>> df.dropna(how='all') name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Keep only the rows with at least 2 non-NA values. >>> df.dropna(thresh=2) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Define in which columns to look for missing values. >>> df.dropna(subset=['name', 'toy']) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT zBYou cannot set both the how and thresh arguments at the same time.rUrz7supplying multiple axes to axis is no longer supported.rNrNrF)r bool_onlyr:zinvalid how option: r)rrr r.rr.rrBrE _get_axisrbrUrTrrtolistrmcountrOr:rrr?rhrrr)rrrrrrragg_axisagg_objr5indicescheckrmaskrs rrrsV s~~ %F#..,HT  #.. C%gy9 dUDM * *UV V$$T*t8  '' )B((0GrMEyy{{rxx/6==?@@iii7G  'MMxM0E?D E\>%%8u%ED E\>%%8u%ED3C59: : 66$<<YYDY)FXX4X(.F (V5FLM V$r)r$rcgrr{rrr$rrs rr'DataFrame.drop_duplicates* r)r$rrcgrr{rs rr'r5rrcgrr{rs rr'r@rrfirstc UR(aURSS9$[US5n[US5nXRXS9*nU(a[ [ U55UlU(aURU5 gU$)u Return DataFrame with duplicate rows removed. Considering certain columns is optional. Indexes, including time indexes are ignored. Parameters ---------- subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns. keep : {'first', 'last', ``False``}, default 'first' Determines which duplicates (if any) to keep. - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - ``False`` : Drop all duplicates. inplace : bool, default ``False`` Whether to modify the DataFrame rather than creating a new one. ignore_index : bool, default ``False`` If ``True``, the resulting axis will be labeled 0, 1, …, n - 1. Returns ------- DataFrame or None DataFrame with duplicates removed or None if ``inplace=True``. See Also -------- DataFrame.value_counts: Count unique combinations of columns. Examples -------- Consider dataset containing ramen rating. >>> df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) >>> df brand style rating 0 Yum Yum cup 4.0 1 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0 By default, it removes duplicate rows based on all columns. >>> df.drop_duplicates() brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0 To remove duplicates on specific column(s), use ``subset``. >>> df.drop_duplicates(subset=['brand']) brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5 To remove duplicates and keep last occurrences, use ``keep``. >>> df.drop_duplicates(subset=['brand', 'style'], keep='last') brand style rating 1 Yum Yum cup 4.0 2 Indomie cup 3.5 4 Indomie pack 5.0 Nrrrr#)rArr.rrhrrr)rrr$rrrs rr'rKswb ::99$9' '%gy9*<H v99: (V5FL    (Mrc ^^^TR(aTR[S9$S U4SjjnTc TRmOX[R "T5(a:[ T[5(d%[ T[5(aTTR;aT4m[[T5m[T5[TR5- nU(a[[U55e[T5S:Xa:TRR(aTTSR!U5nSUlOsU4SjTR%55n['[([+['X6565upx[-U[U5SSS9n TR[!X5TR.S 9nUR1TS S 9$) a Return boolean Series denoting duplicate rows. Considering certain columns is optional. Parameters ---------- subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns. keep : {'first', 'last', False}, default 'first' Determines which duplicates (if any) to mark. - ``first`` : Mark duplicates as ``True`` except for the first occurrence. - ``last`` : Mark duplicates as ``True`` except for the last occurrence. - False : Mark all duplicates as ``True``. Returns ------- Series Boolean series for each duplicated rows. See Also -------- Index.duplicated : Equivalent method on index. Series.duplicated : Equivalent method on Series. Series.drop_duplicates : Remove duplicate values from Series. DataFrame.drop_duplicates : Remove duplicate values from DataFrame. Examples -------- Consider dataset containing ramen rating. >>> df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) >>> df brand style rating 0 Yum Yum cup 4.0 1 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0 By default, for each set of duplicated values, the first occurrence is set on False and all others on True. >>> df.duplicated() 0 False 1 True 2 False 3 False 4 False dtype: bool By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True. >>> df.duplicated(keep='last') 0 True 1 False 2 False 3 False 4 False dtype: bool By setting ``keep`` on False, all duplicates are True. >>> df.duplicated(keep=False) 0 True 1 True 2 False 3 False 4 False dtype: bool To find duplicates on specific column(s), use ``subset``. >>> df.duplicated(subset=['brand']) 0 False 1 True 2 False 3 True 4 True dtype: bool rcz>[R"U[T5S9upURSSS9[U54$)N) size_hinti8Fr)rP factorizerastype)valsrrCrs rfDataFrame.duplicated..f s7&00TKMF==E=2CJ> >rNrrc3P># UHupUT;dM URv M g7fr)r )r^rr_rs rr`'DataFrame.duplicated..-sO 94JCJJ s &&F)sortxnullr$rr)rIztuple[np.ndarray, int])rArr#rriterablerrr.rr rrTrfrrrrrrrrrrr) rrr$rdiffrrrrCidss `` rrDataFrame.duplicatedsS| ::++$+7 7 ? >\\F F##&#&&&%(($,,&YFh' 6{S.. 5;' ' v;!  6 6&)_//5FFKO ODc3q<&89MF!&%,U%PC--j.C4::-VF""4 "==r)r ascendingrrZ na_positionrrcgrr{ rbyrrrrZrrrs r sort_valuesDataFrame.sort_values7r+r)rrrZrrrcgrr{rs rrrFr+r quicksortlastc @[US5nURU5n[U5n[U[5(dU/n[ U5(a<[ U5[ U5:wa$[S[ U5S[ U5S35e[ U5S:aRUV s/sHoRXS9PM n n Ub&[X5V V s/sHup[XS9PM n n n [XXhS 9n O[ U5(aNURUS US9n Ub [XS S9n [U[[45(aUS n[XX6US 9n O'U(aURU5$URSS 9$[!U [ U 55(adURU(+=(a [#5(+S 9nU(a[%[ U55UlU(aURU5$U$UR(R+XR-U5S S9nU(a3UR/UR-U5[%[ U 555 UR1XR2S9nU(aURU5$UR5USS9$s sn fs sn n f)u Sort by the values along either axis. Parameters ---------- by : str or list of str Name or list of names to sort by. - if `axis` is 0 or `'index'` then `by` may contain index levels and/or column labels. - if `axis` is 1 or `'columns'` then `by` may contain column levels and/or index labels. axis : "{0 or 'index', 1 or 'columns'}", 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 rzLength of ascending (z) != length of by ()rrNr)ordersrrr)rZrrrrF)rverifyrrr)r.rBr-rrrGrr_get_label_or_level_valuesrr}rr.rrrrrrhrrrm_get_block_manager_axisrrrr)rrrrrrZrrrr]rrrrrnew_datas rrrUs|&gy9$$T*&y1 "d##B y ! ! Gs9~ %'I'78%%(WIQ0  r7Q;KMN2a33A3A2DN &)]%2 1(%2 &KGWW//1D/AA1a5))eT]33%aL  PSG++D11yydy++ GS\ 2 2YY[%N9L9N5NYPF,S[9 ++F33 99>> 66t[T U]UUUUUUUUU S9 $)u Sort object by labels (along an axis). Returns a new DataFrame sorted by label if `inplace` argument is ``False``, otherwise updates the original DataFrame and returns None. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns. level : int or level name or list of ints or list of level names If not None, sort on values in specified index level(s). ascending : bool or list-like of bools, default True Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually. inplace : bool, default False Whether to modify the DataFrame rather than creating a new one. 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. Not implemented for MultiIndex. sort_remaining : bool, default True If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. key : callable, optional If not None, apply the key function to the index 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 an ``Index`` and return an ``Index`` of the same shape. For MultiIndex inputs, the key is applied *per level*. Returns ------- DataFrame or None The original DataFrame sorted by the labels or None if ``inplace=True``. See Also -------- Series.sort_index : Sort Series by the index. DataFrame.sort_values : Sort DataFrame by the value. Series.sort_values : Sort Series by the value. Examples -------- >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], ... columns=['A']) >>> df.sort_index() A 1 4 29 2 100 1 150 5 234 3 By default, it sorts in ascending order, to sort in descending order, use ``ascending=False`` >>> df.sort_index(ascending=False) A 234 3 150 5 100 1 29 2 1 4 A key function can be specified which is applied to the index before sorting. For a ``MultiIndex`` this is applied to each level separately. >>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd']) >>> df.sort_index(key=lambda x: x.str.lower()) a A 1 b 2 C 3 d 4 r)rr) rrrrrrZrrrrrs rrrrs8Bw!#)%"  rcUcURR5nU(aSOSnURXSS9RR 5nXglU(aUR US9nU(aXwR5-n[U5(aI[U5S:Xa:[R"UR/URR /S9Ul U$)a Return a Series containing the frequency of each distinct row in the Dataframe. Parameters ---------- subset : label or list of labels, optional Columns to use when counting unique combinations. normalize : bool, default False Return proportions rather than frequencies. sort : bool, default True Sort by frequencies when True. Sort by DataFrame column values when False. ascending : bool, default False Sort in ascending order. dropna : bool, default True Don't include counts of rows that contain NA values. .. versionadded:: 1.3.0 Returns ------- Series See Also -------- Series.value_counts: Equivalent method on Series. Notes ----- The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row. Examples -------- >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6], ... 'num_wings': [2, 0, 0, 0]}, ... index=['falcon', 'dog', 'cat', 'ant']) >>> df num_legs num_wings falcon 2 2 dog 4 0 cat 4 0 ant 6 0 >>> df.value_counts() num_legs num_wings 4 0 2 2 2 1 6 0 1 Name: count, dtype: int64 >>> df.value_counts(sort=False) num_legs num_wings 2 2 1 4 0 2 6 0 1 Name: count, dtype: int64 >>> df.value_counts(ascending=True) num_legs num_wings 2 2 1 6 0 1 4 0 2 Name: count, dtype: int64 >>> df.value_counts(normalize=True) num_legs num_wings 4 0 0.50 2 2 0.25 6 0 0.25 Name: proportion, dtype: float64 With `dropna` set to `False` we can also count rows with NA values. >>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'], ... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']}) >>> df first_name middle_name 0 John Smith 1 Anne 2 John 3 Beth Louise >>> df.value_counts() first_name middle_name Beth Louise 1 John Smith 1 Name: count, dtype: int64 >>> df.value_counts(dropna=False) first_name middle_name Anne NaN 1 Beth Louise 1 John Smith 1 NaN 1 Name: count, dtype: int64 >>> df.value_counts("first_name") first_name John 2 Anne 1 Beth 1 Name: count, dtype: int64 proportionrF)robserved)rrr) rrgroupby_grouperrrrsumrErrk from_arraysr)rr normalizerrrrcountss r value_countsDataFrame.value_countssd >\\((*F(|gfeDMMRRT '')'>> df = pd.DataFrame({'population': [59000000, 65000000, 434000, ... 434000, 434000, 337000, 11300, ... 11300, 11300], ... 'GDP': [1937894, 2583560 , 12011, 4520, 12128, ... 17036, 182, 38, 311], ... 'alpha-2': ["IT", "FR", "MT", "MV", "BN", ... "IS", "NR", "TV", "AI"]}, ... index=["Italy", "France", "Malta", ... "Maldives", "Brunei", "Iceland", ... "Nauru", "Tuvalu", "Anguilla"]) >>> df population GDP alpha-2 Italy 59000000 1937894 IT France 65000000 2583560 FR Malta 434000 12011 MT Maldives 434000 4520 MV Brunei 434000 12128 BN Iceland 337000 17036 IS Nauru 11300 182 NR Tuvalu 11300 38 TV Anguilla 11300 311 AI In the following example, we will use ``nlargest`` to select the three rows having the largest values in column "population". >>> df.nlargest(3, 'population') population GDP alpha-2 France 65000000 2583560 FR Italy 59000000 1937894 IT Malta 434000 12011 MT When using ``keep='last'``, ties are resolved in reverse order: >>> df.nlargest(3, 'population', keep='last') population GDP alpha-2 France 65000000 2583560 FR Italy 59000000 1937894 IT Brunei 434000 12128 BN When using ``keep='all'``, the number of element kept can go beyond ``n`` if there are duplicate values for the smallest element, all the ties are kept: >>> df.nlargest(3, 'population', keep='all') population GDP alpha-2 France 65000000 2583560 FR Italy 59000000 1937894 IT Malta 434000 12011 MT Maldives 434000 4520 MV Brunei 434000 12128 BN However, ``nlargest`` does not keep ``n`` distinct largest elements: >>> df.nlargest(5, 'population', keep='all') population GDP alpha-2 France 65000000 2583560 FR Italy 59000000 1937894 IT Malta 434000 12011 MT Maldives 434000 4520 MV Brunei 434000 12128 BN To order by the largest values in column "population" and then "GDP", we can specify multiple columns like in the next example. >>> df.nlargest(3, ['population', 'GDP']) population GDP alpha-2 France 65000000 2583560 FR Italy 59000000 1937894 IT Brunei 434000 12128 BN rXr$r)r{ SelectNFramenlargestrrXrr$s rrDataFrame.nlargestes!n##DDJSSUUrcH[R"XX2S9R5$)a Return the first `n` rows ordered by `columns` in ascending order. Return the first `n` rows with the smallest values in `columns`, in ascending order. The columns that are not specified are returned as well, but not used for ordering. This method is equivalent to ``df.sort_values(columns, ascending=True).head(n)``, but more performant. Parameters ---------- n : int Number of items to retrieve. columns : list or str Column name or names to order by. keep : {'first', 'last', 'all'}, default 'first' Where there are duplicate values: - ``first`` : take the first occurrence. - ``last`` : take the last occurrence. - ``all`` : keep all the ties of the largest item even if it means selecting more than ``n`` items. Returns ------- DataFrame See Also -------- DataFrame.nlargest : Return the first `n` rows ordered by `columns` in descending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first `n` rows without re-ordering. Examples -------- >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000, ... 434000, 434000, 337000, 337000, ... 11300, 11300], ... 'GDP': [1937894, 2583560 , 12011, 4520, 12128, ... 17036, 182, 38, 311], ... 'alpha-2': ["IT", "FR", "MT", "MV", "BN", ... "IS", "NR", "TV", "AI"]}, ... index=["Italy", "France", "Malta", ... "Maldives", "Brunei", "Iceland", ... "Nauru", "Tuvalu", "Anguilla"]) >>> df population GDP alpha-2 Italy 59000000 1937894 IT France 65000000 2583560 FR Malta 434000 12011 MT Maldives 434000 4520 MV Brunei 434000 12128 BN Iceland 337000 17036 IS Nauru 337000 182 NR Tuvalu 11300 38 TV Anguilla 11300 311 AI In the following example, we will use ``nsmallest`` to select the three rows having the smallest values in column "population". >>> df.nsmallest(3, 'population') population GDP alpha-2 Tuvalu 11300 38 TV Anguilla 11300 311 AI Iceland 337000 17036 IS When using ``keep='last'``, ties are resolved in reverse order: >>> df.nsmallest(3, 'population', keep='last') population GDP alpha-2 Anguilla 11300 311 AI Tuvalu 11300 38 TV Nauru 337000 182 NR When using ``keep='all'``, the number of element kept can go beyond ``n`` if there are duplicate values for the largest element, all the ties are kept. >>> df.nsmallest(3, 'population', keep='all') population GDP alpha-2 Tuvalu 11300 38 TV Anguilla 11300 311 AI Iceland 337000 17036 IS Nauru 337000 182 NR However, ``nsmallest`` does not keep ``n`` distinct smallest elements: >>> df.nsmallest(4, 'population', keep='all') population GDP alpha-2 Tuvalu 11300 38 TV Anguilla 11300 311 AI Iceland 337000 17036 IS Nauru 337000 182 NR To order by the smallest values in column "population" and then "GDP", we can specify multiple columns like in the next example. >>> df.nsmallest(3, ['population', 'GDP']) population GDP alpha-2 Tuvalu 11300 38 TV Anguilla 11300 311 AI Nauru 337000 182 NR r)r{r nsmallestrs rrDataFrame.nsmallests!\##DDJTTVVrzaxis : {0 or 'index', 1 or 'columns'}, default 0 The axis to swap levels on. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.at Examples -------- >>> df = pd.DataFrame( ... {"Grade": ["A", "B", "A", "C"]}, ... index=[ ... ["Final exam", "Final exam", "Coursework", "Coursework"], ... ["History", "Geography", "History", "Geography"], ... ["January", "February", "March", "April"], ... ], ... ) >>> df Grade Final exam History January A Geography February B Coursework History March A Geography April C In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices. >>> df.swaplevel() Grade Final exam January History A February Geography B Coursework March History A April Geography C By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows. >>> df.swaplevel(0) Grade January History Final exam A February Geography Final exam B March History Coursework A April Geography Coursework C We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices. >>> df.swaplevel(0, 1) Grade History Final exam January A Geography Final exam February B History Coursework March A Geography Coursework April C)r extra_paramsrrNcURSS9nURU5n[URU5[5(d [ S5eUS:XaC[UR [5(deUR RX5UlU$[UR[5(deURRX5UlU$)Nrz,Can only swap levels on a hierarchical axis.r) rrBrrrkr r swaplevelr)rrjrrs rrDataFrame.swaplevelNs~%$$T*&**40*==JK K 19fllJ77 77!<<11!7FL fnnj99 99#^^55a;FN rcURU5n[URU5[5(d [ S5eUR SS9nUS:XaC[UR [5(deUR RU5UlU$[UR[5(deURRU5UlU$)aK Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int or list of str List representing new level order. Reference level by number (position) or by key (label). axis : {0 or 'index', 1 or 'columns'}, default 0 Where to reorder levels. Returns ------- DataFrame Examples -------- >>> data = { ... "class": ["Mammals", "Mammals", "Reptiles"], ... "diet": ["Omnivore", "Carnivore", "Carnivore"], ... "species": ["Humans", "Dogs", "Snakes"], ... } >>> df = pd.DataFrame(data, columns=["class", "diet", "species"]) >>> df = df.set_index(["class", "diet"]) >>> df species class diet Mammals Omnivore Humans Carnivore Dogs Reptiles Carnivore Snakes Let's reorder the levels of the index: >>> df.reorder_levels(["diet", "class"]) species diet class Omnivore Mammals Humans Carnivore Mammals Dogs Reptiles Snakes z/Can only reorder levels on a hierarchical axis.Nrr) rBrrrkr rrreorder_levelsr)rorderrrs rrDataFrame.reorder_levelssR$$T*$... ;;MN N% 19fllJ77 77!<<66u=FL fnnj99 99#^^::5AFN rcnSnURXSSS9upURXUS9nURU5$)NrFflexrr) _align_for_op_dispatch_frame_op_construct_resultrroprrs r _cmp_methodDataFrame._cmp_methodsH((5(M **54*@%%h//rcfURXSSS5(aURX5$Sn[R"XRU45nUR XSSS9up[ R"SS9 URXUS9nSSS5 URW5$!,(df  N=f)NrTrrr:r) _should_reindex_frame_op_arith_method_with_reindexrSmaybe_prepare_scalar_for_oprCrrerrstaterrrs r _arith_methodDataFrame._arith_methods  ( (AtT B B225= =// 48H7JK((4t(L  [[X &..ut.DH'%%h//' &s 8B"" B0c[R"U5n[R"U5n[ U5(d2UR R XAS9nURXURS9$[U[5(aURRUR5(deURRUR5(deUR RUR U5nURXURS9$[U[5(aUS:XaURRUR5(deUR n[U["R$5(ae['UR)5U5VVs/sH upgU"Xg5PM nnnO~[U[5(a^URRUR5(deUR nUR)5V s/sH o"X5PM nn O [+U5e[-U5R/XRURSS9$s snnfs sn f)aY Evaluate the frame operation func(left, right) by evaluating column-by-column, dispatching to the Series implementation. Parameters ---------- right : scalar, Series, or DataFrame func : arithmetic or comparison operator axis : {None, 0, 1} Returns ------- DataFrame Notes ----- Caller is responsible for setting np.errstate where relevant. )rrrF)ry)rS get_array_oprr%rErapplyrrrrrr8roperate_blockwiser}rRrrrrrzrrz) rrrrarray_opbm_left_rightrrs rrDataFrame._dispatch_frame_ops,##D)%%e,E""7B--bww-? ? y ) )::$$U[[11 11<<&&u}}55 55 ,,  B--bww-? ? v & &419;;%%dll33 33MME!%44 44&))A)A)CU%K%KME'%K F v & &;;%%djj11 11MME8<8P8P8RS8Rht+8RFSF&e, ,Dz&& LL$**u'  Ts ,I0#I6cF^^TcTnOUU4SjnURX5nU$)NcF>[R"XT5upT"X5$r)rS fill_binop)rrrrs r _arith_op+DataFrame._combine_frame.._arith_opDs""nnT*E D((r)r)rrrrr rs `` r_combine_frameDataFrame._combine_frame:s.  I )**5<rcUnURRURSSSS9upEnURSS2U4nURSS2U4nU"Xx5n URRURSSSS9un n U RR(aHU RR U 5up[ R "U 5n U RSX/0SS9n U $U RU SS9n U $) z For DataFrame-with-DataFrame operations that require reindexing, operate only on shared columns, then reindex. Parameters ---------- right : DataFrame op : binary operator Returns ------- DataFrame innerNT)rrreturn_indexersouterr)rr) rjoinrhas_duplicatesryrPunique1dr r) rrrrrlcolsrcolsnew_left new_rightr join_columnsrrs rr$DataFrame._arith_method_with_reindexNs"\\.. MMwdD/ U99QX&JJq%x( H("\\.. MMwdD/  a >> ( ( >>|LJG ))'2G22\+,3F  ^^Lq^9F rcU[RLdU[RLag[ U[ 5(dgUcUcUS:XaUR R5nUR R5nURU5n[U5(a1[U5[U5:Xa[U5[U5:Xdgg)zM Check if this is an operation between DataFrames that will need to reindex. FrT) operatorpowrTrpowrrrrZ intersectionr) rrrrrr left_uniques right_uniquesrs rr"DataFrame._should_reindex_frame_opzs  y~~!5%++  %-DAI <<..0L!MM002M,,];D4yyD S..3t9M@R3Rrc^^XsmnUU4Sjn[U[R5(GaURS:Xa U"U5nGOURS:XGaASnUR[ :Xa[ nUR TR :Xa(TRUTRTRUS9nGOqUR STR S:Xa[UR SS:XaH[R"UTR 5nTRUTRTRUS9nOUR STR S:Xa#UR SS:XaU"USSS245nO[STR SUR 35eURS:a[S UR 35eOe[U5(aU[U[[45(d:[S U55(a[S [!US5S 35eU"U5nUb[[U[5(aFTR#U5(d+U(aTR%US USS9umnTU4$[S5eTU4$[U[5(anTbTOSmU(d8TR&TR)UR5(d [S5eTR%US TUSS9umnTR+UT5nTU4$)ae Convert rhs to meet lhs dims if input is list, tuple or np.ndarray. Parameters ---------- left : DataFrame right : Any axis : int flex : bool or None, default False Whether this is a flex op, in which case we reindex. None indicates not to check for alignment. level : int or level name, default None Returns ------- left : DataFrame right : Any c>SnSn[USS5[:Xa[nTS:Xat[TR5[U5:wa5[ UR [TR5[U5S95eTR UTRUS9nU$[TR5[U5:wa5[ UR [TR5[U5S95eTR UTRUS9nU$)NzGUnable to coerce to Series, length must be {req_len}: given {given_len}rr)req_len given_lenr)rrrrrrrr)rrrrrs r to_series*DataFrame._align_for_op..to_seriess> Eugt,6qytzz?c%j0$ 3tzz?c%j Q00djjPU0VL t||$E 2$ 3t||+<E S00dllRW0XLrrrNrrz-Unable to coerce to DataFrame, shape must be z: given z>Unable to coerce to Series/DataFrame, dimension must be <= 2: c38# UHn[U5v M g7fr)r;)r^els rr`*DataFrame._align_for_op..s5u=$$urbzUnable to coerce list of z to Series/DataFramerF)rrrzOCan only compare identically-labeled (both index and columns) DataFrame objectszdOperands are not aligned. Do `left, right = left.align(right, axis=1, copy=False)` before operating.)rrrr)rrrrrrrCrrr broadcast_torrEr}rrUr _indexed_samealignrr8_maybe_align_series_as_frame) rrrrrrr*rrs ` @rrDataFrame._align_for_ops2 e 8 eRZZ ( (zzQ!%(q;;&(#E;;$**, --TZZU.E[[^tzz!}4Q19LOOE4::>E --TZZU.E[[^tzz!}4Q19L%eAqDk2E%##'::,hu{{mE a //4{{m= % EFI;N)O)O5u555 /U1X/??STe$E   5) < <%%e,,"&**G5u#-#KD%8U{1%, -<U{)v & &+4Dyy--ekk::$, ** %KD%55eTBEU{rcURn[U[R5(d)URS;a[R "U5nOU$US:XaUR SS5nOUR SS5n[R"X0R5nURUURURURS9$)zV If the Series operand is not EA-dtype, we can broadcast to 2D and operate blockwise. )zdatetime64[ns]ztimedelta64[ns]rrNrr) rRrrrrrrPr/rCrrr)rrGrrvaluess rr2&DataFrame._maybe_align_series_as_frame s ..'2::..}} EE**W- 19oob!,Gooa,G//'::6  **LL-- !  r)rrrcUbURU5OSnURXX5U5(aURX5$[U[5(aUb[ SUS35e[ R"XR5nURXSUS9up[R"SS9 [U[5(aURXU5nOK[U[5(aURXUS9nO%UbURU5nURX5nSSS5 UR!W5$!,(df  N=f) Nrz fill_value z not supported.Trrrr)rBrrrr}rzrSrrCrrrrrrfillnar)rrrrrrrs r_flex_arith_methodDataFrame._flex_arith_method: s*/3.>t$$T*A  ( (De L L225= = eV $ $)?& J<&OP P//zzB((4u(M  [[X &%++..u*EE6**22542H);;z2D225='%%h//' &s .A4D;; E cURUSS9RU5nURUlURUlU$)z Wrap the result of an arithmetic, comparison, or logical operation. Parameters ---------- result : DataFrame Returns ------- DataFrame Fr)rrrr)rrouts rrDataFrame._construct_resultZ sAU3@@Fll JJ  rcX-nXU-- nX#4$rr{rrdivmods r __divmod__DataFrame.__divmod__m sm5[ xrcX-nXU-- nX#4$rr{r?s r __rdivmod__DataFrame.__rdivmod__s smDj xrrrcUbURU5OSnURXSUS9upURXUS9nURU5$)NrTrr)rBrrr)rrrrrrs r_flex_cmp_methodDataFrame._flex_cmp_methody sX.2.>t$$T*A((4u(M **54*@%%h//reqc@URU[RX#S9$NrG)rIrrKrrrrs rrK DataFrame.eq $$UHKKd$PPrnec@URU[RX#S9$rM)rIrrQrNs rrQ DataFrame.ne rPrlec@URU[RX#S9$rM)rIrrTrNs rrT DataFrame.le rPrltc@URU[RX#S9$rM)rIrrWrNs rrW DataFrame.lt rPrgec@URU[RX#S9$rM)rIrrZrNs rrZ DataFrame.ge rPrgtc@URU[RX#S9$rM)rIrr]rNs rr] DataFrame.gt rPrrRcBURU[RX4US9$N)rrr)r9rrRrrrrrs rrR DataFrame.add )&& 8<>D'  rsubcBURU[RX4US9$ra)r9rrirbs rri DataFrame.sub rdrrsubcBURU[RX4US9$ra)r9rTrlrbs rrlDataFrame.rsub rhrmulcBURU[RX4US9$ra)r9rrorbs rro DataFrame.mul rdrrmulcBURU[RX4US9$ra)r9rTrrrbs rrrDataFrame.rmul rhrtruedivcBURU[RX4US9$ra)r9rrurbs rruDataFrame.truediv s+&& 8##5d'  rrtruedivcBURU[RX4US9$ra)r9rTrxrbs rrxDataFrame.rtruediv s,&& 9%%UPT'  rfloordivcBURU[RX4US9$ra)r9rr{rbs rr{DataFrame.floordiv s+&& 8$$Et'  r rfloordivcBURU[RX4US9$ra)r9rTr~rbs rr~DataFrame.rfloordiv s,&& 9&&eQU'  rrAcBURU[RX4US9$ra)r9rrArbs rrA DataFrame.mod rdrrmodcBURU[RX4US9$ra)r9rTrrbs rrDataFrame.rmod rhrr cBURU[RX4US9$ra)r9rr rbs rr  DataFrame.pow!rdrr!cBURU[RX4US9$ra)r9rTr!rbs rr!DataFrame.rpow !rhrcomparea Returns ------- DataFrame DataFrame that shows the differences stacked side by side. The resulting index will be a MultiIndex with 'self' and 'other' stacked alternately at the inner level. Raises ------ ValueError When the two DataFrames don't have identical labels or shape. See Also -------- Series.compare : Compare with another Series and show differences. DataFrame.equals : Test whether two objects contain the same elements. Notes ----- Matching NaNs will not appear as a difference. Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames Examples -------- >>> df = pd.DataFrame( ... {{ ... "col1": ["a", "a", "b", "b", "a"], ... "col2": [1.0, 2.0, 3.0, np.nan, 5.0], ... "col3": [1.0, 2.0, 3.0, 4.0, 5.0] ... }}, ... columns=["col1", "col2", "col3"], ... ) >>> df col1 col2 col3 0 a 1.0 1.0 1 a 2.0 2.0 2 b 3.0 3.0 3 b NaN 4.0 4 a 5.0 5.0 >>> df2 = df.copy() >>> df2.loc[0, 'col1'] = 'c' >>> df2.loc[2, 'col3'] = 4.0 >>> df2 col1 col2 col3 0 c 1.0 1.0 1 a 2.0 2.0 2 b 3.0 4.0 3 b NaN 4.0 4 a 5.0 5.0 Align the differences on columns >>> df.compare(df2) col1 col3 self other self other 0 a c NaN NaN 2 NaN NaN 3.0 4.0 Assign result_names >>> df.compare(df2, result_names=("left", "right")) col1 col3 left right left right 0 a c NaN NaN 2 NaN NaN 3.0 4.0 Stack the differences on rows >>> df.compare(df2, align_axis=0) col1 col3 0 self a NaN other c NaN 2 self NaN 3.0 other NaN 4.0 Keep the equal values >>> df.compare(df2, keep_equal=True) col1 col3 self other self other 0 a c 1.0 1.0 2 b b 3.0 4.0 Keep all original rows and columns >>> df.compare(df2, keep_shape=True) col1 col2 col3 self other self other self other 0 a c NaN NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN 2 NaN NaN NaN NaN 3.0 4.0 3 NaN NaN NaN NaN NaN NaN 4 NaN NaN NaN NaN NaN NaN Keep all original rows and columns and also all original values >>> df.compare(df2, keep_shape=True, keep_equal=True) col1 col2 col3 self other self other self other 0 a c 1.0 1.0 1.0 1.0 1 a a 2.0 2.0 2.0 2.0 2 b b 3.0 3.0 3.0 4.0 3 b b NaN NaN 4.0 4.0 4 a a 5.0 5.0 5.0 5.0 c&>[TU]UUUUUS9$)N)r align_axis keep_shape keep_equal result_names)rr)rrrrrrrs rrDataFrame.compare!s+xw!!!%   rcT[UR5nURUSS9upaURnUR(a2[U5[UR5:XaUR 5$UR(a[U5U:XaUR 5$UR R UR 5nUSLn 0n UGH!n Xkn Xn U RnU Rn[U 5n[U 5nU(d+UR5(aXkR 5X'MlU (a(U R 5n U R 5n X>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2 >>> df1.combine(df2, take_smaller) A B 0 0 3 1 0 3 Example using a true element-wise combine function. >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, np.minimum) A B 0 1 2 1 0 3 Using `fill_value` fills Nones prior to passing the column to the merge function. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 -5.0 1 0 4.0 However, if the same element in both dataframes is None, that None is preserved >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 -5.0 1 0 3.0 Example that demonstrates the use of `overwrite` and behavior when the axis differ between the dataframes. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2]) >>> df1.combine(df2, take_smaller) A B C 0 NaN NaN NaN 1 NaN 3.0 -10.0 2 NaN 3.0 1.0 >>> df1.combine(df2, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 -10.0 2 NaN 3.0 1.0 Demonstrating the preference of the passed in dataframe. >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2]) >>> df2.combine(df1, take_smaller) A B C 0 0.0 NaN NaN 1 0.0 3.0 NaN 2 NaN 3.0 NaN >>> df2.combine(df1, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0 FrNrcombiner)rrr1rArrrrrNr:rrr4rrr8rr)rrrr overwrite other_idxlenthisr r@do_fillrr_rG other_series this_dtype other_dtype this_mask other_mask new_dtyperF frame_results rrDataFrame.combine!s^5;;' jjUj3 JJ ;;3y>S_<99;  ::#e* 4::< ll((7 D(CYF :LJ&,,KV Il+J!1!1"inn. +002 $.y!+5Z(,,&( #]]95]AF -j-FG yu=+22952I v,C)RXX.. .FK_d((+(V ((i(@@/"sH H'&H'c ^^SSKJm S UU4Sjjn[U5S:XahTRTRR URR TR55SS9nURUR5nOTRXSS9nTRRUR5Vs0sHMnURUTRU:wdM%U[TRUURU/5_MO nnU(aURU5nURTSS 9$s snf) a  Update null elements with value in the same location in `other`. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the 'first' dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second). Parameters ---------- other : DataFrame Provided DataFrame to use to fill null values. Returns ------- DataFrame The result of combining the provided DataFrame with the other object. See Also -------- DataFrame.combine : Perform series-wise operation on two DataFrames using a given function. Examples -------- >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine_first(df2) A B 0 1.0 3.0 1 0.0 4.0 Null values still persist if the location of that null value does not exist in `other` >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]}) >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2]) >>> df1.combine_first(df2) A B C 0 NaN 4.0 NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0 r) expressionsc>UR5RnURnURnURTR;aU$TR X$U5$r)rNrRrrr+)r]yrx_valuesy_valuesrrs rcombiner)DataFrame.combine_first..combiner}"sP668##DyyHyyHvvT\\)$$TX> >rrrF)r combine_firstr)r]r}rr}) pandas.core.computationrrrrr differencerrrr"r4r)rrrcombinedr_rrs` @rrDataFrame.combine_firstL"s#^ 8 ? ? u:?|| ##EMM$<$(,D>c[(dL[5(a=[R"U5[::a[ R "[[SS9 Ok[(d`[5(dQUR5(a<[R"U5[::a[ R "[[SS9 US:wa [S5eUS;a [S5e[U[5(d [U5nUR!UR"5nUR$R'UR$5GHnXR(nXR(nUbU"U5)[+U5-n OWUS:Xa3[-U5n [-U5n [/X-5(a [S 5eU(a [+U5n O [-U5n U R15(aM[ R2"5 [ R4"S S [S 9 XR7X5UR8SS2U4'SSS5 GM g!,(df  GM=f) a: Modify in place using non-NA values from another DataFrame. Aligns on indices. There is no return value. Parameters ---------- other : DataFrame, or object coercible into a DataFrame Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame. join : {'left'}, default 'left' Only left join is implemented, keeping the index and columns of the original object. overwrite : bool, default True How to handle non-NA values for overlapping keys: * True: overwrite original DataFrame's values with values from `other`. * False: only update values that are NA in the original DataFrame. filter_func : callable(1d-array) -> bool 1d-array, optional Can choose to replace values other than NA. Return True for values that should be updated. errors : {'raise', 'ignore'}, default 'ignore' If 'raise', will raise a ValueError if the DataFrame and `other` both contain non-NA data in the same place. Returns ------- None This method directly changes calling object. Raises ------ ValueError * When `errors='raise'` and there's overlapping non-NA data. * When `errors` is not either `'ignore'` or `'raise'` NotImplementedError * If `join != 'left'` See Also -------- dict.update : Similar method for dictionaries. DataFrame.merge : For column(s)-on-column(s) operations. Examples -------- >>> df = pd.DataFrame({'A': [1, 2, 3], ... 'B': [400, 500, 600]}) >>> new_df = pd.DataFrame({'B': [4, 5, 6], ... 'C': [7, 8, 9]}) >>> df.update(new_df) >>> df A B 0 1 4 1 2 5 2 3 6 The DataFrame's length does not increase as a result of the update, only values at matching index/column labels are updated. >>> df = pd.DataFrame({'A': ['a', 'b', 'c'], ... 'B': ['x', 'y', 'z']}) >>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']}) >>> df.update(new_df) >>> df A B 0 a d 1 b e 2 c f >>> df = pd.DataFrame({'A': ['a', 'b', 'c'], ... 'B': ['x', 'y', 'z']}) >>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2]) >>> df.update(new_df) >>> df A B 0 a d 1 b y 2 c f For Series, its name attribute must be set. >>> df = pd.DataFrame({'A': ['a', 'b', 'c'], ... 'B': ['x', 'y', 'z']}) >>> new_column = pd.Series(['d', 'e', 'f'], name='B') >>> df.update(new_column) >>> df A B 0 a d 1 b e 2 c f If `other` contains NaNs the corresponding values are not updated in the original dataframe. >>> df = pd.DataFrame({'A': [1, 2, 3], ... 'B': [400., 500., 600.]}) >>> new_df = pd.DataFrame({'B': [4, np.nan, 6]}) >>> df.update(new_df) >>> df A B 0 1 4.0 1 2 500.0 2 3 6.0 rrrzOnly left join is supported)rr%z7The parameter errors must be either 'ignore' or 'raise'Nr%zData overlaps.rzDowncasting behavior)messagecategory)rrr[r\rrrr#r!_is_view_after_cow_rulesr%rrzrrrrrrr"rRrNrOrUr:catch_warningsfilterwarningsr+r?) rrrr filter_funcrr_rthatr mask_this mask_thats rrSDataFrame.update"slt+--t$ 1 2*  1338U8U8W8Wt$ 1 :!  6>%&CD D , ,VW W%++e$E djj)<<,,U]];C9$$D:%%D&#D))DJ6W$ %d I %d I9011()9:::D ;Dxxzz((*''2*$(9??4#>C +*-<,+*s ?I I aQ Examples -------- >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon', ... 'Parrot', 'Parrot'], ... 'Max Speed': [380., 370., 24., 26.]}) >>> df Animal Max Speed 0 Falcon 380.0 1 Falcon 370.0 2 Parrot 24.0 3 Parrot 26.0 >>> df.groupby(['Animal']).mean() Max Speed Animal Falcon 375.0 Parrot 25.0 **Hierarchical Indexes** We can groupby different levels of a hierarchical index using the `level` parameter: >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'], ... ['Captive', 'Wild', 'Captive', 'Wild']] >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type')) >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]}, ... index=index) >>> df Max Speed Animal Type Falcon Captive 390.0 Wild 350.0 Parrot Captive 30.0 Wild 20.0 >>> df.groupby(level=0).mean() Max Speed Animal Falcon 370.0 Parrot 25.0 >>> df.groupby(level="Type").mean() Max Speed Type Captive 210.0 Wild 185.0 We can also choose to include NA in group keys or not by setting `dropna` parameter, the default setting is `True`. >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]] >>> df = pd.DataFrame(l, columns=["a", "b", "c"]) >>> df.groupby(by=["b"]).sum() a c b 1.0 2 3 2.0 2 5 >>> df.groupby(by=["b"], dropna=False).sum() a c b 1.0 2 3 2.0 2 5 NaN 1 4 >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]] >>> df = pd.DataFrame(l, columns=["a", "b", "c"]) >>> df.groupby(by="a").sum() b c a a 13.0 13.0 b 12.3 123.0 >>> df.groupby(by="a", dropna=False).sum() b c a a 13.0 13.0 b 12.3 123.0 NaN 12.3 33.0 When using ``.apply()``, use ``group_keys`` to include or exclude the group keys. The ``group_keys`` argument defaults to ``True`` (include). >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon', ... 'Parrot', 'Parrot'], ... 'Max Speed': [380., 370., 24., 26.]}) >>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x) Max Speed Animal Falcon 0 380.0 1 370.0 Parrot 2 24.0 3 26.0 >>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x) Max Speed 0 380.0 1 370.0 2 24.0 3 26.0 rc 0U[RLa]URU5nUS:Xa#[R"S[ [ 5S9 O%[R"S[ [ 5S9 OSnSSKJn UcUc [S5eU "UUUUUUUUUS9 $) Nrz\DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.rz^The 'axis' keyword in DataFrame.groupby is deprecated and will be removed in a future version.rrz*You have to supply one of 'by' and 'level') rtrrras_indexr group_keysrr) rrrBrrrr+pandas.core.groupby.genericrr ) rrrrrrrrrrs rrDataFrame.groupbyQ#sl s~~ %((.Dqy C!/1  ;!/1 D@ =RZHI I!  ral Return reshaped DataFrame organized by given index / column values. Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified `index` / `columns` to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the :ref:`User Guide ` for more on reshaping. Parameters ----------%s columns : str or object or a list of str Column to use to make new frame's columns. index : str or object or a list of str, optional Column to use to make new frame's index. If not given, uses existing index. values : str, object or a list of the previous, optional Column(s) to use for populating new frame's values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns. Returns ------- DataFrame Returns reshaped DataFrame. Raises ------ ValueError: When there are any `index`, `columns` combinations with multiple values. `DataFrame.pivot_table` when you need to aggregate. See Also -------- DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt. Notes ----- For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods. Reference :ref:`the user guide ` for more examples. Examples -------- >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', ... 'two'], ... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'], ... 'baz': [1, 2, 3, 4, 5, 6], ... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']}) >>> df foo bar baz zoo 0 one A 1 x 1 one B 2 y 2 one C 3 z 3 two A 4 q 4 two B 5 w 5 two C 6 t >>> df.pivot(index='foo', columns='bar', values='baz') bar A B C foo one 1 2 3 two 4 5 6 >>> df.pivot(index='foo', columns='bar')['baz'] bar A B C foo one 1 2 3 two 4 5 6 >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo']) baz zoo bar A B C A B C foo one 1 2 3 x y z two 4 5 6 q w t You could also assign a list of column names or a list of index names. >>> df = pd.DataFrame({ ... "lev1": [1, 1, 1, 2, 2, 2], ... "lev2": [1, 1, 2, 1, 1, 2], ... "lev3": [1, 2, 1, 2, 1, 2], ... "lev4": [1, 2, 3, 4, 5, 6], ... "values": [0, 1, 2, 3, 4, 5]}) >>> df lev1 lev2 lev3 lev4 values 0 1 1 1 1 0 1 1 1 2 2 1 2 1 2 1 3 2 3 2 1 2 4 3 4 2 1 1 5 4 5 2 2 2 6 5 >>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values") lev2 1 2 lev3 1 2 1 2 lev1 1 0.0 1.0 2.0 NaN 2 4.0 3.0 NaN 5.0 >>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values") lev3 1 2 lev1 lev2 1 1 0.0 1.0 2 2.0 NaN 2 1 4.0 3.0 2 NaN 5.0 A ValueError is raised if there are any duplicates. >>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'], ... "bar": ['A', 'A', 'B', 'C'], ... "baz": [1, 2, 3, 4]}) >>> df foo bar baz 0 one A 1 1 one A 2 2 two B 3 3 two C 4 Notice that the first two rows are the same for our `index` and `columns` arguments. >>> df.pivot(index='foo', columns='bar', values='baz') Traceback (most recent call last): ... ValueError: Index contains duplicate entries, cannot reshape pivot)rr cSSKJn U"XXS9$)Nr)r)rrr )pandas.core.reshape.pivotr)rrrr rs rrDataFrame.pivott$s 4TGGra8 Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame. Parameters ----------%s values : list-like or scalar, optional Column or columns to aggregate. index : column, Grouper, array, or list of the previous Keys to group by on the pivot table index. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values. columns : column, Grouper, array, or list of the previous Keys to group by on the pivot table column. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values. aggfunc : function, list of functions, dict, default "mean" If a list of functions is passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves). If a dict is passed, the key is column to aggregate and the value is function or list of functions. If ``margin=True``, aggfunc will be used to calculate the partial aggregates. fill_value : scalar, default None Value to replace missing values with (in the resulting pivot table, after aggregation). margins : bool, default False If ``margins=True``, special ``All`` columns and rows will be added with partial group aggregates across the categories on the rows and columns. dropna : bool, default True Do not include columns whose entries are all NaN. If True, rows with a NaN value in any column will be omitted before computing margins. margins_name : str, default 'All' Name of the row / column that will contain the totals when margins is True. observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. .. deprecated:: 2.2.0 The default value of ``False`` is deprecated and will change to ``True`` in a future version of pandas. sort : bool, default True Specifies if the result should be sorted. .. versionadded:: 1.3.0 Returns ------- DataFrame An Excel style pivot table. See Also -------- DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt. Notes ----- Reference :ref:`the user guide ` for more examples. Examples -------- >>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo", ... "bar", "bar", "bar", "bar"], ... "B": ["one", "one", "one", "two", "two", ... "one", "one", "two", "two"], ... "C": ["small", "large", "large", "small", ... "small", "large", "small", "small", ... "large"], ... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7], ... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]}) >>> df A B C D E 0 foo one small 1 2 1 foo one large 2 4 2 foo one large 2 5 3 foo two small 3 5 4 foo two small 3 6 5 bar one large 4 6 6 bar one small 5 8 7 bar two small 6 9 8 bar two large 7 9 This first example aggregates values by taking the sum. >>> table = pd.pivot_table(df, values='D', index=['A', 'B'], ... columns=['C'], aggfunc="sum") >>> table C large small A B bar one 4.0 5.0 two 7.0 6.0 foo one 4.0 1.0 two NaN 6.0 We can also fill missing values using the `fill_value` parameter. >>> table = pd.pivot_table(df, values='D', index=['A', 'B'], ... columns=['C'], aggfunc="sum", fill_value=0) >>> table C large small A B bar one 4 5 two 7 6 foo one 4 1 two 0 6 The next example aggregates by taking the mean across multiple columns. >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'], ... aggfunc={'D': "mean", 'E': "mean"}) >>> table D E A C bar large 5.500000 7.500000 small 5.500000 8.500000 foo large 2.000000 4.500000 small 2.333333 4.333333 We can also calculate multiple types of aggregations for any given value column. >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'], ... aggfunc={'D': "mean", ... 'E': ["min", "max", "mean"]}) >>> table D E mean max mean min A C bar large 5.500000 9 7.500000 6 small 5.500000 9 8.500000 8 foo large 2.000000 5 4.500000 4 small 2.333333 6 4.333333 2 pivot_tablemeanAllc .SSKJn U "UUUUUUUUUU U S9 $)Nr)r) r rraggfuncrmarginsr margins_namerr)rr) rr rrrrrrrrrrs rrDataFrame.pivot_table%s5 : !%  rc^U(dSSKJnJn U[RLd-U[RLdTR R S:a"[R"S[[5S9 U[RLaSnU[RLaSn[U[[45(a U"TXUS9nOU"TXUS9nOSSKJn U[RLa [S 5eU[RLa [S 5e[U[[45(a<[!U4S jU55(d"[!S U55(d [S 5e[U[[45(dU/nUV s/sHn TR R#U 5PM nn U"TU5nUR%TSS9$s sn f)a[ Stack the prescribed level(s) from columns to index. Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe: - if the columns have a single level, the output is a Series; - if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame. Parameters ---------- level : int, str, list, default -1 Level(s) to stack from the column axis onto the index axis, defined as one index or label, or a list of indices or labels. dropna : bool, default True Whether to drop rows in the resulting Frame/Series with missing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section. sort : bool, default True Whether to sort the levels of the resulting MultiIndex. future_stack : bool, default False Whether to use the new implementation that will replace the current implementation in pandas 3.0. When True, dropna and sort have no impact on the result and must remain unspecified. See :ref:`pandas 2.1.0 Release notes ` for more details. Returns ------- DataFrame or Series Stacked dataframe or series. See Also -------- DataFrame.unstack : Unstack prescribed level(s) from index axis onto column axis. DataFrame.pivot : Reshape dataframe from long format to wide format. DataFrame.pivot_table : Create a spreadsheet-style pivot table as a DataFrame. Notes ----- The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe). Reference :ref:`the user guide ` for more examples. Examples -------- **Single level columns** >>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]], ... index=['cat', 'dog'], ... columns=['weight', 'height']) Stacking a dataframe with a single level column axis returns a Series: >>> df_single_level_cols weight height cat 0 1 dog 2 3 >>> df_single_level_cols.stack(future_stack=True) cat weight 0 height 1 dog weight 2 height 3 dtype: int64 **Multi level columns: simple case** >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'), ... ('weight', 'pounds')]) >>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]], ... index=['cat', 'dog'], ... columns=multicol1) Stacking a dataframe with a multi-level column axis: >>> df_multi_level_cols1 weight kg pounds cat 1 2 dog 2 4 >>> df_multi_level_cols1.stack(future_stack=True) weight cat kg 1 pounds 2 dog kg 2 pounds 4 **Missing values** >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'), ... ('height', 'm')]) >>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]], ... index=['cat', 'dog'], ... columns=multicol2) It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs: >>> df_multi_level_cols2 weight height kg m cat 1.0 2.0 dog 3.0 4.0 >>> df_multi_level_cols2.stack(future_stack=True) weight height cat kg 1.0 NaN m NaN 2.0 dog kg 3.0 NaN m NaN 4.0 **Prescribing the level(s) to be stacked** The first parameter controls which level or levels are stacked: >>> df_multi_level_cols2.stack(0, future_stack=True) kg m cat weight 1.0 NaN height NaN 2.0 dog weight 3.0 NaN height NaN 4.0 >>> df_multi_level_cols2.stack([0, 1], future_stack=True) cat weight kg 1.0 height m 2.0 dog weight kg 3.0 height m 4.0 dtype: float64 r)stackstack_multiplerzThe previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.rT)rr)stack_v3zdropna must be unspecified with future_stack=True as the new implementation does not introduce rows of NA values. This argument will be removed in a future version of pandas.zCannot specify sort with future_stack=True, this argument will be removed in a future version of pandas. Sort the result using .sort_index instead.c3T># UHoTRR;v M g7fr)rr )r^rurs rr`"DataFrame.stack..%sG#4<<#5#55s%(c3B# UHn[U[5v M g7fr)rrx)r^rus rr`r%sBESJsC00EszTlevel should contain all level names or all level numbers, not a mixture of the two.rr)pandas.core.reshape.reshaperrrrrrgrrrr+rr.rrrr:rmr) rrrr future_stackrrrrrus ` rrDataFrame.stack3%sj  cnn,s~~-<<''!+ G"/1 's~~%%%//'eNtUE <S^^+ N 3>>) +55$-00GGGGBEBBB 9 eeT]33DIJEST\\33C8EEJdE*F""4"88Ks%GcURR(dCURURR5R5n[ SU35e[ U5(d[ U[5(aU/nOx[ U[5(aX[SU55(aAU(d [ S5e[U5[[U55:a [ S5eUnO [ S5eURSS9n[U5S:XaXTS R5nOS nXS RU5nUSS H2n [XU RU5:H5(aM)[ S 5e [UV s0sHoXYR5_M sn 5nUR!USS 9R#U5nU(a[%[U55UlO*UR&R)UR&5UlUR+URSS9nUR-USS9$s sn f)u Transform each element of a list-like to a row, replicating index values. Parameters ---------- column : IndexLabel Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length. .. versionadded:: 1.3.0 Multi-column explode ignore_index : bool, default False If True, the resulting index will be labeled 0, 1, …, n - 1. Returns ------- DataFrame Exploded lists to rows of the subset columns; index will be duplicated for these rows. Raises ------ ValueError : * If columns of the frame are not unique. * If specified columns to explode is empty list. * If specified columns to explode have not matching count of elements rowwise in the frame. See Also -------- DataFrame.unstack : Pivot a level of the (necessarily hierarchical) index labels. DataFrame.melt : Unpivot a DataFrame from wide format to long format. Series.explode : Explode a DataFrame from list-like columns to long format. Notes ----- This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets. Reference :ref:`the user guide ` for more examples. Examples -------- >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]], ... 'B': 1, ... 'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]}) >>> df A B C 0 [0, 1, 2] 1 [a, b, c] 1 foo 1 NaN 2 [] 1 [] 3 [3, 4] 1 [d, e] Single-column explode. >>> df.explode('A') A B C 0 0 1 [a, b, c] 0 1 1 [a, b, c] 0 2 1 [a, b, c] 1 foo 1 NaN 2 NaN 1 [] 3 3 1 [d, e] 3 4 1 [d, e] Multi-column explode. >>> df.explode(list('AC')) A B C 0 0 1 a 0 1 1 b 0 2 1 c 1 foo 1 NaN 2 NaN 1 NaN 3 3 1 d 3 4 1 e z5DataFrame columns must be unique. Duplicate columns: c3f# UH'n[U5=(d [U[5v M) g7fr)rFrr.)r^rqs rr`$DataFrame.explode..i&s&. :@QIaL 0Jq%0 0&s/1zcolumn must be nonemptyzcolumn must be uniquez/column must be a scalar, tuple, or list thereofT)rKrrcZ[U5(a[U5S:a [U5$S$)Nrr)rEr)r]s r#DataFrame.explode..x&s"aSVaZc!fOaOrNz)columns must have matching element countsrFrexploder)rrrrrrFrr.rr:rrrerrrrKrrhrrmrr) rrdrduplicate_colsrrrmylencounts0rqs rrDataFrame.explode&sr||%%!\\$,,*A*A*CDKKMNGGWX  V   65 9 9hG  % %#. :@. + +  !:;;6{SV-- !899GNO O   4  ( w<1  ^++-FOE1:&,,U3GQR[71gmmE&::;;$%PQQ!GDGq25==? 2GDEFq)..v6 (V5FL::??6<<8FL 5A""4 ":: EsIc@SSKJn U"XX#5nURUSS9$)a Pivot a level of the (necessarily hierarchical) index labels. Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex). Parameters ---------- level : int, str, or list of these, default -1 (last level) Level(s) of index to unstack, can pass level name. fill_value : int, str or dict Replace NaN with this value if the unstack produces missing values. sort : bool, default True Sort the level(s) in the resulting MultiIndex columns. Returns ------- Series or DataFrame See Also -------- DataFrame.pivot : Pivot a table based on column values. DataFrame.stack : Pivot a level of the column labels (inverse operation from `unstack`). Notes ----- Reference :ref:`the user guide ` for more examples. Examples -------- >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ... ('two', 'a'), ('two', 'b')]) >>> s = pd.Series(np.arange(1.0, 5.0), index=index) >>> s one a 1.0 b 2.0 two a 3.0 b 4.0 dtype: float64 >>> s.unstack(level=-1) a b one 1.0 2.0 two 3.0 4.0 >>> s.unstack(level=0) one two a 1.0 3.0 b 2.0 4.0 >>> df = s.unstack(level=0) >>> df.unstack() one a 1.0 b 2.0 two a 3.0 b 4.0 dtype: float64 r)unstackrr)rrr)rrrrrrs rrDataFrame.unstack&s*~ 8j7""4 "::rr|zdf.melt()callerrc <[UUUUUUUS9RUSS9$)N)id_vars value_varsvar_name value_namerarr|r)r|r)rrrrrrars rr|DataFrame.melt&s6 !!%  ,tF, + ,rzcaxis : {0 or 'index', 1 or 'columns'}, default 0 Take difference over rows (0) or columns (1). a Difference with previous row >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6], ... 'b': [1, 1, 2, 3, 5, 8], ... 'c': [1, 4, 9, 16, 25, 36]}) >>> df a b c 0 1 1 1 1 2 1 4 2 3 2 9 3 4 3 16 4 5 5 25 5 6 8 36 >>> df.diff() a b c 0 NaN NaN NaN 1 1.0 0.0 3.0 2 1.0 1.0 5.0 3 1.0 1.0 7.0 4 1.0 2.0 9.0 5 1.0 3.0 11.0 Difference with previous column >>> df.diff(axis=1) a b c 0 NaN 0 0 1 NaN -1 3 2 NaN -1 7 3 NaN -1 13 4 NaN 0 20 5 NaN 2 28 Difference with 3rd previous row >>> df.diff(periods=3) a b c 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 3.0 2.0 15.0 4 3.0 4.0 21.0 5 3.0 6.0 27.0 Difference with following row >>> df.diff(periods=-1) a b c 0 -1.0 0.0 -3.0 1 -1.0 -1.0 -5.0 2 -1.0 -1.0 -7.0 3 -1.0 -2.0 -9.0 4 -1.0 -3.0 -11.0 5 NaN NaN NaN Overflow in input dtype >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8) >>> df.diff() a 0 NaN 1 255.0)rr other_klassrc[R"U5(d;[U5(aUR5(d [S5e[ U5nUR U5nUS:XaUS:waXR XS9- $SnURRUS9nURX3RS9nURUS5$)Nzperiods must be an integerrrr)rXrr) rrBr?rrxrBr@rrrrr)rr=rrrLs rrDataFrame.diff&sT~~g&&W%%'*<*<*>*> !=>>'lG$$T* 19!|jjj<<<D99>>G>,++H==+I""400rc@UcUnX1$URS:XaU$X1$)z Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : {1, 2} requested ndim of result subset : object, default None subset to act on rr)rrrrs r_gotitemDataFrame._gotitemC's5" >F { [[A M{ra! See Also -------- DataFrame.apply : Perform any type of operations. DataFrame.transform : Perform transformation type operations. pandas.DataFrame.groupby : Perform operations over groups. pandas.DataFrame.resample : Perform operations over resampled bins. pandas.DataFrame.rolling : Perform operations over rolling window. pandas.DataFrame.expanding : Perform operations over expanding window. pandas.core.window.ewm.ExponentialMovingWindow : Perform operation over exponential weighted window. a Examples -------- >>> df = pd.DataFrame([[1, 2, 3], ... [4, 5, 6], ... [7, 8, 9], ... [np.nan, np.nan, np.nan]], ... columns=['A', 'B', 'C']) Aggregate these functions over the rows. >>> df.agg(['sum', 'min']) A B C sum 12.0 15.0 18.0 min 1.0 2.0 3.0 Different aggregations per column. >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']}) A B sum 12.0 NaN min 1.0 2.0 max NaN 8.0 Aggregate different functions over the columns and rename the index of the resulting DataFrame. >>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean')) A B C x 7.0 NaN NaN y NaN 2.0 NaN z NaN NaN 6.0 Aggregate over the columns. >>> df.agg("mean", axis="columns") 0 2.0 1 5.0 2 8.0 3 NaN dtype: float64 aggregate)rrsee_alsorc|SSKJn URU5nU"XX#US9nUR5n[ Xq40UD6nU$Nr frame_apply)rrrr)pandas.core.applyrrBaggrVrrrrrrrrs rrDataFrame.aggregate'sD 2$$T* tv N/GG r transform)rrcpSSKJn U"XX#US9nUR5n[U[5(deU$r)rrrrrrs rrDataFrame.transform's8 2 tv N&),,,, rc hSSKJn U "UUUUUUUUUU S9 n U R5RUSS9$)at Apply a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either the DataFrame's index (``axis=0``) or the DataFrame's columns (``axis=1``). By default (``result_type=None``), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the `result_type` argument. Parameters ---------- func : function Function to apply to each column or row. axis : {0 or 'index', 1 or 'columns'}, default 0 Axis along which the function is applied: * 0 or 'index': apply function to each column. * 1 or 'columns': apply function to each row. raw : bool, default False Determines if row or column is passed as a Series or ndarray object: * ``False`` : passes each row or column as a Series to the function. * ``True`` : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance. result_type : {'expand', 'reduce', 'broadcast', None}, default None These only act when ``axis=1`` (columns): * 'expand' : list-like results will be turned into columns. * 'reduce' : returns a Series if possible rather than expanding list-like results. This is the opposite of 'expand'. * 'broadcast' : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained. The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns. args : tuple Positional arguments to pass to `func` in addition to the array/series. by_row : False or "compat", default "compat" Only has an effect when ``func`` is a listlike or dictlike of funcs and the func isn't a string. If "compat", will if possible first translate the func into pandas methods (e.g. ``Series().apply(np.sum)`` will be translated to ``Series().sum()``). If that doesn't work, will try call to apply again with ``by_row=True`` and if that fails, will call apply again with ``by_row=False`` (backward compatible). If False, the funcs will be passed the whole Series at once. .. versionadded:: 2.1.0 engine : {'python', 'numba'}, default 'python' Choose between the python (default) engine or the numba engine in apply. The numba engine will attempt to JIT compile the passed function, which may result in speedups for large DataFrames. It also supports the following engine_kwargs : - nopython (compile the function in nopython mode) - nogil (release the GIL inside the JIT compiled function) - parallel (try to apply the function in parallel over the DataFrame) Note: Due to limitations within numba/how pandas interfaces with numba, you should only use this if raw=True Note: The numba compiler only supports a subset of valid Python/numpy operations. Please read more about the `supported python features `_ and `supported numpy features `_ in numba to learn what you can or cannot use in the passed function. .. versionadded:: 2.2.0 engine_kwargs : dict Pass keyword arguments to the engine. This is currently only used by the numba engine, see the documentation for the engine argument for more information. **kwargs Additional keyword arguments to pass as keywords arguments to `func`. Returns ------- Series or DataFrame Result of applying ``func`` along the given axis of the DataFrame. See Also -------- DataFrame.map: For elementwise operations. DataFrame.aggregate: Only perform aggregating type operations. DataFrame.transform: Only perform transforming type operations. Notes ----- Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:`gotchas.udf-mutation` for more details. Examples -------- >>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B']) >>> df A B 0 4 9 1 4 9 2 4 9 Using a numpy universal function (in this case the same as ``np.sqrt(df)``): >>> df.apply(np.sqrt) A B 0 2.0 3.0 1 2.0 3.0 2 2.0 3.0 Using a reducing function on either axis >>> df.apply(np.sum, axis=0) A 12 B 27 dtype: int64 >>> df.apply(np.sum, axis=1) 0 13 1 13 2 13 dtype: int64 Returning a list-like will result in a Series >>> df.apply(lambda x: [1, 2], axis=1) 0 [1, 2] 1 [1, 2] 2 [1, 2] dtype: object Passing ``result_type='expand'`` will expand list-like results to columns of a Dataframe >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand') 0 1 0 1 2 1 1 2 2 1 2 Returning a Series inside the function is similar to passing ``result_type='expand'``. The resulting column names will be the Series index. >>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1) foo bar 0 1 2 1 1 2 2 1 2 Passing ``result_type='broadcast'`` will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals. >>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast') A B 0 1 2 1 1 2 2 1 2 rr) rrraw result_typeby_rowrrrrrr)rrrr) rrrrrrrrrrrrs rrDataFrame.apply'sN| 2  #'  xxz&&tG&<>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]]) >>> df 0 1 0 1.000 2.120 1 3.356 4.567 >>> df.map(lambda x: len(str(x))) 0 1 0 3 4 1 5 5 Like Series.map, NA values can be ignored: >>> df_copy = df.copy() >>> df_copy.iloc[0, 0] = pd.NA >>> df_copy.map(lambda x: len(str(x)), na_action='ignore') 0 1 0 NaN 4 1 5.0 5 It is also possible to use `map` with functions that are not `lambda` functions: >>> df.map(round, ndigits=1) 0 1 0 1.0 2.1 1 3.4 4.6 Note that a vectorized version of `func` often exists, which will be much faster. You could square each number elementwise. >>> df.map(lambda x: x**2) 0 1 0 1.000000 4.494400 1 11.262736 20.857489 But it's better to avoid map in that case. >>> df ** 2 0 1 0 1.000000 4.494400 1 11.262736 20.857489 >Nrz(na_action must be 'ignore' or None. Got c$>URTTS9$)N) na_action) _map_values)r]rrs rrDataFrame.map..infer(s===; ;rr)rreprrAr functoolspartialrr)rrrrrs `` rr DataFrame.map(sx^ , ,:4 ?:KL  ::99;   00 <zz% --dE::rc p[R"S[[5S9 UR"U4SU0UD6$)a Apply a function to a Dataframe elementwise. .. deprecated:: 2.1.0 DataFrame.applymap has been deprecated. Use DataFrame.map instead. This method applies a function that accepts and returns a scalar to every element of a DataFrame. Parameters ---------- func : callable Python function, returns a single value from a single value. na_action : {None, 'ignore'}, default None If 'ignore', propagate NaN values, without passing them to func. **kwargs Additional keyword arguments to pass as keywords arguments to `func`. Returns ------- DataFrame Transformed DataFrame. See Also -------- DataFrame.apply : Apply a function along input axis of DataFrame. DataFrame.map : Apply a function along input axis of DataFrame. DataFrame.replace: Replace values given in `to_replace` with `value`. Examples -------- >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]]) >>> df 0 1 0 1.000 2.120 1 3.356 4.567 >>> df.map(lambda x: len(str(x))) 0 1 0 3 4 1 5 5 zBDataFrame.applymap has been deprecated. Use DataFrame.map instead.rr)rrrr+r)rrrrs rapplymapDataFrame.applymap(s8^  P ') xx< >> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'], ... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']}) >>> df key A 0 K0 A0 1 K1 A1 2 K2 A2 3 K3 A3 4 K4 A4 5 K5 A5 >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'], ... 'B': ['B0', 'B1', 'B2']}) >>> other key B 0 K0 B0 1 K1 B1 2 K2 B2 Join DataFrames using their indexes. >>> df.join(other, lsuffix='_caller', rsuffix='_other') key_caller A key_other B 0 K0 A0 K0 B0 1 K1 A1 K1 B1 2 K2 A2 K2 B2 3 K3 A3 NaN NaN 4 K4 A4 NaN NaN 5 K5 A5 NaN NaN If we want to join using the key columns, we need to set key to be the index in both `df` and `other`. The joined DataFrame will have key as its index. >>> df.set_index('key').join(other.set_index('key')) A B key K0 A0 B0 K1 A1 B1 K2 A2 B2 K3 A3 NaN K4 A4 NaN K5 A5 NaN Another option to join using the key columns is to use the `on` parameter. DataFrame.join always uses `other`'s index but we can use any column in `df`. This method preserves the original DataFrame's index in the result. >>> df.join(other.set_index('key'), on='key') key A B 0 K0 A0 B0 1 K1 A1 B1 2 K2 A2 B2 3 K3 A3 NaN 4 K4 A4 NaN 5 K5 A5 NaN Using non-unique key values shows how they are matched. >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'], ... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']}) >>> df key A 0 K0 A0 1 K1 A1 2 K1 A2 3 K3 A3 4 K0 A4 5 K1 A5 >>> df.join(other.set_index('key'), on='key', validate='m:1') key A B 0 K0 A0 B0 1 K1 A1 B1 2 K1 A2 B1 3 K3 A3 NaN 4 K0 A4 B0 5 K1 A5 B1 rr;mergeNzOther Series must have a namecross)ronsuffixesrvalidateT)left_onr left_index right_indexrrrz?Joining multiple DataFrames only supported for joining on indexz7Suffixes not supported when joining multiple DataFramesDataFrame | Seriesc3L# UHoRRv M g7fr)rr)r^rs rr`!DataFrame.join..!*sA&BXX//&s"$rrr)rrryrFr)rrrr)rCr<pandas.core.reshape.mergerrr}rrrrrr:rr)rrrrlsuffixrsuffixrrr<rframes can_concatrjoinedr_s rrDataFrame.joinT)s| 63 eV $ $zz! !@AAuzz512E eY ' 'g~%/%: !+!  ~ U' M/67$u+EFA&AAJ&= QWtRVC;;tzz;>>!QS4dAYF# $% $Mrr)indentsc 2SSKJn U "UUUUUUUUUU U U U S9 $)Nrr) rrrright_onrrrrr indicatorr)r r)rrrrrr)rrrrrr*rrs rrDataFrame.merge=*s;" 4  !#  rc ^SSKJn SU4SjjnSSjm[R"X#5 [ U[ [ 45(a[ U[ 5(a&URR(d [S5e[U5(a0[SUR555(d [S5e[U"X55nOd[U5(aIUR R#U[%5S9nUR'XwR(S 9R+US S 9$[S 5eUbG[-U5S:a8UR/U"US S9URUR0S9R+US S 9$UR3SS9$)a Round a DataFrame to a variable number of decimal places. Parameters ---------- decimals : int, dict, Series Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if `decimals` is a dict-like, or in the index if `decimals` is a Series. Any columns not included in `decimals` will be left as is. Elements of `decimals` which are not columns of the input will be ignored. *args Additional keywords have no effect but might be accepted for compatibility with numpy. **kwargs Additional keywords have no effect but might be accepted for compatibility with numpy. Returns ------- DataFrame A DataFrame with the affected columns rounded to the specified number of decimal places. See Also -------- numpy.around : Round a numpy array to the given number of decimals. Series.round : Round a Series to the given number of decimals. Examples -------- >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)], ... columns=['dogs', 'cats']) >>> df dogs cats 0 0.21 0.32 1 0.01 0.67 2 0.66 0.03 3 0.21 0.18 By providing an integer each column is rounded to the same number of decimal places >>> df.round(1) dogs cats 0 0.2 0.3 1 0.0 0.7 2 0.7 0.0 3 0.2 0.2 With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value >>> df.round({'dogs': 1, 'cats': 0}) dogs cats 0 0.2 0.0 1 0.0 1.0 2 0.7 0.0 3 0.2 0.0 Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value >>> decimals = pd.Series([0, 1], index=['cats', 'dogs']) >>> df.round(decimals) dogs cats 0 0.2 0.0 1 0.0 1.0 2 0.7 0.0 3 0.2 0.0 rr;c3># UR5Hup#T"X1U5v M g![a Uv M)f=f7fr)rrT)rdecimalsr_r _series_rounds r _dict_round$DataFrame.round.._dict_round*sAXXZ 'sm<<( Js A ,A>A>Ac[UR5(d[UR5(aURU5$U$r)rCrr@round)rr.s rr/&DataFrame.round.._series_round*s3 **nSYY.G.Gyy**Jrz Index of decimals must be uniquec3<# UHup[U5v M g7fr)rB)r^rrqs rr`"DataFrame.round..*s22Bha 5!!2Bsz#Values in decimals must be integers)r.rrr3rz4decimals must be an integer, a dict-like or a SeriesrrrFr)rr)rr}r.rxrIr})rCr<rvalidate_roundrrr}rrrr>r:rr rrBrr3rrrrrrrr) rr.rrr<r0new_colsr r/s @rr3DataFrame.round`*sn^ 6   $' hv / /(F++HNN4L4L !CDDH%%c22:..2B2// EFFK78H  ! !iioo!-/&G--gLL-IVVWW RS S  CMA$5$$xa( DLL%l4l0 199%9( (rcU(aUR5OUnURnUR5nUR[[ R SS9nUS:Xa[R"XrS9nGOCUS:Xa[R"XrS9nGO'US:Xd[U5(GaUcSnURn[R"U5n [U5n [ R"X4[S9n[ R "U5n [#U5Hup[#U5H{upX:aM XX-nUR%5U:a[ R nO5X:XaS nO-UR'5(dU "U UUU5nOU "X5nUXU4'UXU 4'M} M O[)S US 35eUR+XUSS 9nUR-US S9$)aZ Compute pairwise correlation of columns, excluding NA/null values. Parameters ---------- method : {'pearson', 'kendall', 'spearman'} or callable Method of correlation: * pearson : standard correlation coefficient * kendall : Kendall Tau correlation coefficient * spearman : Spearman rank correlation * callable: callable with input two 1d ndarrays and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable's behavior. min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionadded:: 1.5.0 .. versionchanged:: 2.0.0 The default value of ``numeric_only`` is now ``False``. Returns ------- DataFrame Correlation matrix. See Also -------- DataFrame.corrwith : Compute pairwise correlation with another DataFrame or Series. Series.corr : Compute the correlation between two Series. Notes ----- Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations. * `Pearson correlation coefficient `_ * `Kendall rank correlation coefficient `_ * `Spearman's rank correlation coefficient `_ Examples -------- >>> def histogram_intersection(a, b): ... v = np.minimum(a, b).sum().round(decimals=1) ... return v >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)], ... columns=['dogs', 'cats']) >>> df.corr(method=histogram_intersection) dogs cats dogs 1.0 0.3 cats 0.3 1.0 >>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)], ... columns=['dogs', 'cats']) >>> df.corr(min_periods=3) dogs cats dogs 1.0 NaN cats NaN 1.0 Frrrpearson)minpspearmankendallrrg?zHmethod must be either 'pearson', 'spearman', 'kendall', or a callable, 'z' was suppliedr corrr)_get_numeric_datarrrrrrlibalgosnancorrnancorr_spearmancallablerQrR get_corr_funcrrAisfiniterrr:rrr)rr min_periods numeric_onlyrrrOmatcorrelcorrfKrracrbcvalidrqrs rr@DataFrame.corr*sN,8t%%'T||iikmm%"&&umE Y %%c+  ""6d"O""4"77rcU(aUR5OUnURnUR5nUR[[ R SS9n[U5R5(aUbaU[U5:aR[ R"URSURS45nUR[ R 5 O[ R"URUS9nUR[U5[U545nO[ R""USUS9nUR%XUSS9n U R'USS 9$) a Compute pairwise covariance of columns, excluding NA/null values. Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the `covariance matrix `__ of the columns of the DataFrame. Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as ``NaN``. This method is generally used for the analysis of time series data to understand the relationship between different measures across time. Parameters ---------- min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. ddof : int, default 1 Delta degrees of freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. This argument is applicable only when no ``nan`` is in the dataframe. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionadded:: 1.5.0 .. versionchanged:: 2.0.0 The default value of ``numeric_only`` is now ``False``. Returns ------- DataFrame The covariance matrix of the series of the DataFrame. See Also -------- Series.cov : Compute covariance with another Series. core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample covariance. core.window.expanding.Expanding.cov : Expanding sample covariance. core.window.rolling.Rolling.cov : Rolling sample covariance. Notes ----- Returns the covariance matrix of the DataFrame's time series. The covariance is normalized by N-ddof. For DataFrames that have Series that are missing data (assuming that data is `missing at random `__) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series. However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See `Estimation of covariance matrices `__ for more details. Examples -------- >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)], ... columns=['dogs', 'cats']) >>> df.cov() dogs cats dogs 0.666667 -1.000000 cats -1.000000 1.666667 >>> np.random.seed(42) >>> df = pd.DataFrame(np.random.randn(1000, 5), ... columns=['a', 'b', 'c', 'd', 'e']) >>> df.cov() a b c d e a 0.998438 -0.020161 0.059277 -0.008943 0.014144 b -0.020161 1.059352 -0.008543 -0.024738 0.009826 c 0.059277 -0.008543 1.010670 -0.001486 -0.000271 d -0.008943 -0.024738 -0.001486 0.921297 -0.013692 e 0.014144 0.009826 -0.000271 -0.013692 0.977795 **Minimum number of periods** This method also supports an optional ``min_periods`` keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result: >>> np.random.seed(42) >>> df = pd.DataFrame(np.random.randn(20, 3), ... columns=['a', 'b', 'c']) >>> df.loc[df.index[:5], 'a'] = np.nan >>> df.loc[df.index[5:10], 'b'] = np.nan >>> df.cov(min_periods=12) a b c a 0.316741 NaN -0.150812 b NaN 1.248003 0.191417 c -0.150812 0.191417 0.895202 Fr;r)ddofT)covr=r rTr)rArrrrrrrOr:rrArCfillrTrQrPrBrCrr) rrHrSrIrrrOrJbase_covrs rrT DataFrame.covQ+s `,8t%%'T||iikmm%"&&umE :>>  &;S+A88SYYq\399Q<$@A bff%66#%%d3''TCI(>?H''KHH""85"Q""4"66rc ^^URU5nU(aUR5OUn[T[5(aUR UU4SjUS9$U(aTR5mUR TSSS9upxUS:XaUR nUR nTS:XazXxS--nXS--nXwRUS 9- n XRUS 9- n X-R5n UR5S- URUS 9-URUS 9-n X- n OTS ;d[T5(a^U4S jnUR[U[URR URR 55UR SS 9n O[#S TS35eU(dUS:XaSOSnUR%U5R'TR%U55nUR)U R*5n[-U5S:a4U R/[[0R2/[-U5-US95n U $)a] Compute pairwise correlation. Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations. Parameters ---------- other : DataFrame, Series Object with which to compute correlations. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for column-wise. drop : bool, default False Drop missing indices from result. method : {'pearson', 'kendall', 'spearman'} or callable Method of correlation: * pearson : standard correlation coefficient * kendall : Kendall Tau correlation coefficient * spearman : Spearman rank correlation * callable: callable with input two 1d ndarrays and returning a float. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionadded:: 1.5.0 .. versionchanged:: 2.0.0 The default value of ``numeric_only`` is now ``False``. Returns ------- Series Pairwise correlations. See Also -------- DataFrame.corr : Compute pairwise correlation of columns. Examples -------- >>> index = ["a", "b", "c", "d", "e"] >>> columns = ["one", "two", "three", "four"] >>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns) >>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns) >>> df1.corrwith(df2) one 1.0 two 1.0 three 1.0 four 1.0 dtype: float64 >>> df2.corrwith(df1, axis=1) a 1.0 b 1.0 c 1.0 d 1.0 e NaN dtype: float64 c$>TRUTS9$)Nr)r@)r]rrs rr$DataFrame.corrwith..,s 1V (DrrrF)rrrr<r)rI)r?r>c<>[R"USUSTS9$)Nrrr)rRrC)r]rs rrqDataFrame.corrwith..c=,s~~adAaD@@rrzInvalid method zM was passed, valid methods are: 'pearson', 'kendall', 'spearman', or callabler$)rBrArr}rr1rQrrrstdrErrrr rrrrrrrrrr)rrrrKrrIrrrldemrdemnumdomrKrqraxisrDidx_diffs ` ` rcorrwithDataFrame.corrwith+s"P$$T*+7t%%'T eV $ $::D4:P P ++-EjjW5jA  1966DGGE Y !)#D1H$E)))>>D::<:@@D;##%C!(( (56)))67  YF . .(62B2B A--As4;;==%,,..9:ll.F!&***  #'!)QE>>%066uu7MNL#..v||>> df = pd.DataFrame({"Person": ... ["John", "Myla", "Lewis", "John", "Myla"], ... "Age": [24., np.nan, 21., 33, 26], ... "Single": [False, True, True, True, False]}) >>> df Person Age Single 0 John 24.0 False 1 Myla NaN True 2 Lewis 21.0 True 3 John 33.0 True 4 Myla 26.0 False Notice the uncounted NA values: >>> df.count() Person 5 Age 4 Single 5 dtype: int64 Counts for each **row**: >>> df.count(axis='columns') 0 3 1 2 2 3 3 3 4 3 dtype: int64 rr$rrFrrr) rBrArrr _get_agg_axisrOrrr)rrrIr_rs rrDataFrame.count^,s|$$T* **,EE ut$ % *--au7J7J47P-QF5\%%4%0F}}W5}1>>tG>TTrrskipnarI filter_typec . ^^^^^^^^Tb TS:XdT5eTS:XaSOSnTbTRT5mSUUUU4Sjjn 0mSS UUUUUU4Sjjjn S!UU4Sjjn Tn U(aU "5n Tc[U RRV s/sHoRPM sn 5n[ U[ 5(aHU RUSS9n [[U R555n U R"T4TSS.TD6$U "U R5$TS:XGaZ[U R5S :Xa9U R"TT4S TSTS .TD6RSS nU RUlU$U R S(GaTS :wGa[U RRV s/sHoRPM sn 5n[ U[ 5(GaS S S.R#TT5mU RUSS9n [[U R555n U R unn[$R&"[$R("U5U5n[$R*"[$R("U5U5n[-U USS9n[/STS3[0S[3T5R4STS3S9 UR7U5R8"T40TD6nSSS5 U RWlT(dhTS;abU R;5R=[$R>S9RASS9nTS;aSO[BRDnURGUU5nU$U RHn U RRKU 5nU RMUURNS9RS nUb#URS:waURU5nU$U RRQ5[R:HRA5(aTS;aUR[R5nU$[T5S :Xa9UR[R:Xa%TS;aUR[$RT5nU$s sn fs sn f!,(df  GN=f)"Nr#c>T"U4TTS.TD6$)Nrrjr{)r rkwdsrrjs rrDataFrame._reduce..func,sf?4?$? ?rrcv>[U[5(Ga[UR5(d5[T R[ 5(dUR "T4ST S.TD6$TRUR5nUc3[UR 5nSUR;nUTUR'U(aUR "T4T SS.TD6$[R"[U5S3[[5S9 UR "T4ST 0TD6n[R "U/5$T"U4UT S.TD6$) NrrnkeepdimsTrjrrz:._reduce will require a `keepdims` parameter in the futurerrj)rr[r:rrro_reducerr parametersrrrrr+rr) r r has_keepdimssignrdtype_has_keepdimsrorrrrjs rblk_func#DataFrame._reduce..blk_func,s!&.11*6<<88II|BB">>$NQvNNN155fllC '$V^^4D#-#@L7C&v||4!>>$UvUPTUUMM<.)((%#3#5  $^^DHH4HF88VH--&CtFCdCCrcb>TcTR5nU$TS:XdeTR5nU$)Nr#)rA_get_bool_data)rrkrs r _get_data$DataFrame._reduce.._get_data,sA"--/ K#f,,,**,KrFrrsrrikurtidxmaxidxmin)argmaxargminrzThe behavior of SeriesGroupBy.z with all-NA valuesThe behavior of rzn with all-NA values, or any-NA and skipna=False, is deprecated. In a future version this will raise ValueError)target_messagetarget_category new_message)rUr:rr)rrrNrboolean)rprod)r  np.ndarray)rrrrIr)+rBr4rrrrrMrrJrrrtr rrrrCrrrrrepeatr}r,rrrrrrNrbool_rUrrrrQreducerr get_dtypesrr)rrrrrjrIrkro out_dtyperryr}rrFrrrXrI row_index col_indexrrrrr<rxs````` `` @rrtDataFrame._reduce,s "kV&;H[H;)V3F  ((.D @ @:< D D2   B <$277>>%J>Cii>%JKE%00YYu5Y1#D)?)?)A$BC{{4OO$OO ? " QY288}!!!& + $r "xx  xx{{tv~(rww~~)N~))~)NOe^44'/(CGGdSD5u5B'R-C-C-E(FGC#%88LE5 " %(8% @I " "))E*:E BI IEBC)[TU]"XU40UD6n[U[5(aUR USS9nU$)Nrgr)rrgrr}rrrrjrIrrrs rrg DataFrame.mins-AT<B6B ff % %((e([TU]"XU40UD6n[U[5(aUR USS9nU$)Nrjr)rrjrr}rrs rrj DataFrame.max-rrrc H>[TU]"XX440UD6nURUSS9$)Nrr)rrrrrrjrI min_countrrrs rr DataFrame.sum-s0T<MfM""4"66rrc H>[TU]"XX440UD6nURUSS9$)Nrr)rrrrs rrDataFrame.prod-s0dLNvN""4"77rc v>[TU]"XU40UD6n[U[5(aUR USS9nU$)Nrr)rrrr}rrs rrDataFrame.mean-AdLCFC ff % %((f(=F rmedianc v>[TU]"XU40UD6n[U[5(aUR USS9nU$)Nrr)rrrr}rrs rrDataFrame.median-sAlEfE ff % %((h(?F rsemc v>[TU]"XX440UD6n[U[5(aUR USS9nU$)Nrr)rrrr}rrrrjrSrIrrrs rr DataFrame.sem-AT4HH ff % %((e([TU]"XX440UD6n[U[5(aUR USS9nU$)Nrr)rrrr}rrs rr DataFrame.var-rrr]c v>[TU]"XX440UD6n[U[5(aUR USS9nU$)Nr]r)rr]rr}rrs rr] DataFrame.std-rrskewc v>[TU]"XU40UD6n[U[5(aUR USS9nU$)Nrr)rrrr}rrs rrDataFrame.skew-rrrc v>[TU]"XU40UD6n[U[5(aUR USS9nU$)Nrr)rrrr}rrs rrDataFrame.kurt-rrcumminc8[R"XU/UQ70UD6$r)rbrrrrjrrs rrDataFrame.cummin.~~d&B4B6BBrcummaxc8[R"XU/UQ70UD6$r)rbrrs rrDataFrame.cummax .rrcumsumc8[R"XU/UQ70UD6$r)rbrrs rrDataFrame.cumsum.rrcumprodc8[R"XU/UQ70UD6$r)rbrrs rrDataFrame.cumprod.st6CDCFCCrc>UR[RXS9$)a Count number of distinct elements in specified axis. Return Series with number of distinct elements. Can ignore NaN values. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise. dropna : bool, default True Don't include NaN in the counts. Returns ------- Series See Also -------- Series.nunique: Method nunique for Series. DataFrame.count: Count non-NA cells for each column or row. Examples -------- >>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]}) >>> df.nunique() A 3 B 2 dtype: int64 >>> df.nunique(axis=1) 0 1 1 2 2 2 dtype: int64 )rr)rr}nunique)rrrs rrDataFrame.nunique.sLzz&..tzCCrrFalse)numeric_only_defaultcURU5nUR(aE[URU5(a(URURnUR US9$U(aUR 5nOUnUR[RSXSS9nURnUS:HR5(a9[R"S[U5RS3[ [#5S9 UR%U5n[&R("URUS UR*S 9n UR XR-U5S 9n U R/US S 9$)NrrFrrjrIrNrzu.idxmin with all-NA values, or any-NA and skipna=False, is deprecated. In a future version this will raise ValueErrorrTrkr$rr)rBrArrrrrArtrR nanargminrRrUrrrrrr+rrPrmrsrgr rrrjrI axis_dtyperrrrr final_results rrDataFrame.idxmin>.E$$T* ::#diio..4..J++*+= = ))+DDll   hTu ++ rM   MM"4:#6#6"7855+-  t$ MM7t //>P>PQU>V/W ((h(??rrcURU5nUR(aE[URU5(a(URURnUR US9$U(aUR 5nOUnUR[RSXSS9nURnUS:HR5(a9[R"S[U5RS3[ [#5S9 UR%U5n[&R("URUS UR*S 9n UR XR-U5S 9n U R/US S 9$)NrrFrrNrzu.idxmax with all-NA values, or any-NA and skipna=False, is deprecated. In a future version this will raise ValueErrorrTrkr$rr)rBrArrrrrArtrR nanargmaxrRrUrrrrrr+rrPrmrsrgrrs rrDataFrame.idxmaxc.rrczUS:Xa UR$US:Xa UR$[S[U5S35e)z Let's be explicit about this. rrzAxis must be 0 or 1 (got r)rrrr)raxis_nums rrgDataFrame._get_agg_axis.s@ q=<<  ]:: 8h8HJK Krc^U(dUOUR5nU4SjnURXQS9nUR(a[S5UlU$)a Get the mode(s) of each element along the selected axis. The mode of a set of values is the value that appears most often. It can be multiple values. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to iterate over while searching for the mode: * 0 or 'index' : get mode of each column * 1 or 'columns' : get mode of each row. numeric_only : bool, default False If True, only apply to numeric columns. dropna : bool, default True Don't consider counts of NaN/NaT. Returns ------- DataFrame The modes of each column or row. See Also -------- Series.mode : Return the highest frequency value in a Series. Series.value_counts : Return the counts of values in a Series. Examples -------- >>> df = pd.DataFrame([('bird', 2, 2), ... ('mammal', 4, np.nan), ... ('arthropod', 8, 0), ... ('bird', 2, np.nan)], ... index=('falcon', 'horse', 'spider', 'ostrich'), ... columns=('species', 'legs', 'wings')) >>> df species legs wings falcon bird 2 2.0 horse mammal 4 NaN spider arthropod 8 0.0 ostrich bird 2 NaN By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of ``species`` and ``legs`` contains ``NaN``. >>> df.mode() species legs wings 0 bird 2.0 0.0 1 NaN NaN 2.0 Setting ``dropna=False`` ``NaN`` values are considered and they can be the mode (like for wings). >>> df.mode(dropna=False) species legs wings 0 bird 2 NaN Setting ``numeric_only=True``, only the mode of numeric columns is computed, and columns of other types are ignored. >>> df.mode(numeric_only=True) legs wings 0 2.0 0.0 1 NaN 2.0 To compute the mode over columns and not rows, use the axis parameter: >>> df.mode(axis='columns', numeric_only=True) 0 1 falcon 2.0 NaN horse 4.0 NaN spider 0.0 8.0 ostrich 2.0 NaN c">URTS9$)N)r)r)rrs rrDataFrame.mode..f.s666( (rrr)rArrArhr)rrrIrrrs ` rrDataFrame.mode.sG`(tT-C-C-E )zz!z' ::&q)DJ rcgrr{rqrrI interpolationrs rquantileDataFrame.quantile.rrcgrr{rs rrr.rrcgrr{rs rrr/rrc[U5 URU5n[U5(dURU/UUUUS9nUS:XaURSnOUR RSS2S4nUS:XaN[ U5S:Xa?[[UR55n[U5(aURU5$U$[U[RS9nU(aUR5OUn US:Xa U R n [ U R 5S:Xa[/UR R"S9n [RnUS:Xa0[[UR55n [U 5(aU nUR%/XUS9nUR'US S 9$SS 1n X\;a[)S US U S35eUS:XaU R*RXS9nGOJUS :XGaC1Skn XM;a[)SUSU 35e[ U 5S:XaVU R,S:Xa[[UR55nO UR.nUR%/XR US9$[R"[R0"[ U 55XS 9nU R n[ U5S:a-UVs/sHnU R3U5PM nn[5U5nOU R3US5n[7U5nU R*R9UUSS9nXR:S'UR=WUR:S9nUR'US S 9$s snf)au Return values at the given quantile over requested axis. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value between 0 <= q <= 1, the quantile(s) to compute. axis : {0 or 'index', 1 or 'columns'}, default 0 Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 The default value of ``numeric_only`` is now ``False``. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. * higher: `j`. * nearest: `i` or `j` whichever is nearest. * midpoint: (`i` + `j`) / 2. method : {'single', 'table'}, default 'single' Whether to compute quantiles per-column ('single') or over all columns ('table'). When 'table', the only allowed interpolation methods are 'nearest', 'lower', and 'higher'. Returns ------- Series or DataFrame If ``q`` is an array, a DataFrame will be returned where the index is ``q``, the columns are the columns of self, and the values are the quantiles. If ``q`` is a float, a Series will be returned where the index is the columns of self and the values are the quantiles. See Also -------- core.window.rolling.Rolling.quantile: Rolling quantile. numpy.percentile: Numpy function to compute the percentile. Examples -------- >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), ... columns=['a', 'b']) >>> df.quantile(.1) a 1.3 b 3.7 Name: 0.1, dtype: float64 >>> df.quantile([.1, .5]) a b 0.1 1.3 3.7 0.5 2.5 55.0 Specifying `method='table'` will compute the quantile over all columns. >>> df.quantile(.1, method="table", interpolation="nearest") a 1 b 1 Name: 0.1, dtype: int64 >>> df.quantile([.1, .5], method="table", interpolation="nearest") a b 0.1 1 1 0.5 3 100 Specifying `numeric_only=False` will also compute the quantile of datetime and timedelta data. >>> df = pd.DataFrame({'A': [1, 2], ... 'B': [pd.Timestamp('2010'), ... pd.Timestamp('2011')], ... 'C': [pd.Timedelta('1 days'), ... pd.Timedelta('2 days')]}) >>> df.quantile(0.5, numeric_only=False) A 1.5 B 2010-07-02 12:00:00 C 1 days 12:00:00 Name: 0.5, dtype: object )rrIrrsinglerNrrrrrrr>zInvalid method: z. Method must be in r)qsr>rhighernearestzInvalid interpolation: z. Interpolation must be in rF)rr)r/rBrErrrQrr4rrrHrrfrrrArrrrrrrrrrrrrmrr)rrrrIrrrLrrrrcdtype valid_methodvalid_interpolationq_idxrr]rrrrs rrr/s#v A$$T*A]])+ #F!kk!nhhmmAqD)qySY!^(dkk):;&u--::e,,J !2:: &+7t%%'T 1966D t||  !$,,"3"34DJJEqy)$t{{*;<&v.."E##BaU#KC##D#< < '*  %"6(*>|nAN  X ))$$$GC w "@ 7 -m_=00C/DF 4yA~99>,T$++->?E JJE((1llRW(XXKK #d) 4aNEB2w{DFGBq77:BG)$/33BqE:"1+))...>CHHQK++Cchh+?""4 ";;Hs>Mc*URU=(a [5(+S9nURU5n[X5n[ U[ 5(d![ S[U5R35eURXS9n[XVU5 U$)as Cast to DatetimeIndex of timestamps, at *beginning* of period. Parameters ---------- freq : str, default frequency of PeriodIndex Desired frequency. how : {'s', 'e', 'start', 'end'} Convention for converting period to timestamp; start of period vs. end. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to convert (the index by default). copy : bool, default True If False then underlying input data is not copied. .. 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 ------- DataFrame The DataFrame has a DatetimeIndex. Examples -------- >>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y') >>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> df1 = pd.DataFrame(data=d, index=idx) >>> df1 col1 col2 2023 1 3 2024 2 4 The resulting timestamps will be at the beginning of the year in this case >>> df1 = df1.to_timestamp() >>> df1 col1 col2 2023-01-01 1 3 2024-01-01 2 4 >>> df1.index DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None) Using `freq` which is the offset that the Timestamps will have >>> df2 = pd.DataFrame(data=d, index=idx) >>> df2 = df2.to_timestamp(freq='M') >>> df2 col1 col2 2023-01-31 1 3 2024-01-31 2 4 >>> df2.index DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None) runsupported Type )r>r) rr_get_axis_namerrrgr rr to_timestampsetattr) rr>rrrrt axis_nameold_axnew_axs rrDataFrame.to_timestamp/sL))!C.A.C*C)D''- )&+../V 0E0E/FGH H$$$$8F+rc*URU=(a [5(+S9nURU5n[X5n[ U[ 5(d![ S[U5R35eURUS9n[XEU5 U$)a  Convert DataFrame from DatetimeIndex to PeriodIndex. Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). Parameters ---------- freq : str, default Frequency of the PeriodIndex. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to convert (the index by default). copy : bool, default True If False then underlying input data is not copied. .. note:: The `copy` keyword will change behavior in pandas 3.0. `Copy-on-Write `__ will be enabled by default, which means that all methods with a `copy` keyword will use a lazy copy mechanism to defer the copy and ignore the `copy` keyword. The `copy` keyword will be removed in a future version of pandas. You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True`` Returns ------- DataFrame The DataFrame has a PeriodIndex. Examples -------- >>> idx = pd.to_datetime( ... [ ... "2001-03-31 00:00:00", ... "2002-05-31 00:00:00", ... "2003-08-31 00:00:00", ... ] ... ) >>> idx DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'], dtype='datetime64[ns]', freq=None) >>> idx.to_period("M") PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]') For the yearly frequency >>> idx.to_period("Y") PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]') rr)r>) rrrrrrer rr to_periodr)rr>rrrtrrrs rrDataFrame.to_period0sr))!C.A.C*C)D''- )&-00/V 0E0E/FGH H!!t!,F+rc^^^[T[5(aHSSKJn [R "[ T5mU"UU4Sj[TR55SS9nGO?[T[5(aFTRR(d [S5eTRTRT5SS9nO[T[5(abTRR(aTRR(d [S5eTRTRT55nOm[!T5(d"[#S[%T5R&S 35eU4S jnTR(R+U5nTR-UUR.S 9nUR1TS S 9$)a Whether each element in the DataFrame is contained in values. Parameters ---------- values : iterable, Series, DataFrame or dict The result will only be true at a location if all the labels match. If `values` is a Series, that's the index. If `values` is a dict, the keys must be the column names, which must match. If `values` is a DataFrame, then both the index and column labels must match. Returns ------- DataFrame DataFrame of booleans showing whether each element in the DataFrame is contained in values. See Also -------- DataFrame.eq: Equality test for DataFrame. Series.isin: Equivalent method on Series. Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index. Examples -------- >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]}, ... index=['falcon', 'dog']) >>> df num_legs num_wings falcon 2 2 dog 4 0 When ``values`` is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings) >>> df.isin([0, 2]) num_legs num_wings falcon True True dog False True To check if ``values`` is *not* in the DataFrame, use the ``~`` operator: >>> ~df.isin([0, 2]) num_legs num_wings falcon False False dog True False When ``values`` is a dict, we can pass values to check for each column separately: >>> df.isin({'num_wings': [0, 3]}) num_legs num_wings falcon False False dog False True When ``values`` is a Series or DataFrame the index and column must match. Note that 'falcon' does not match based on the number of legs in other. >>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]}, ... index=['spider', 'falcon']) >>> df.isin(other) num_legs num_wings falcon False True dog False False rr;c3t># UH-upTRSS2U/4RTU5v M/ g7fr)risin)r^rr_rr s rr`!DataFrame.isin..0s;"9IIa!f%**6#;77"9s58rrz*cannot compute isin with a duplicate axis.rz`only list-like or dict-like objects are allowed to be passed to DataFrame.isin(), you passed a 'rc>[R"UR5T5nURUR5$r)rPr ravelrPrC)r]rr s risin_DataFrame.isin..isin_0s4 $GGI~~agg..rrr r)rrrCr<r defaultdictrrrr}rrrrK reindex_likerrEr rrrrrrr)rr r<rrrs`` rr DataFrame.isinV0saJ fd # # 9 ,,T6:F"+DLL"9 F ' '<<)) !MNNWWV006WWEF  * *NN,,1G1G !MNNWWV0067F''%%)&\%:%:$;1> /iiooe,G//\\0F""4"77rz!list[Literal['index', 'columns']] _AXIS_ORDERS)rrzdict[Axis, int]_AXIS_TO_AXIS_NUMBERz Literal[1]_info_axis_numberzLiteral['columns']_info_axis_namea The index (row labels) of the DataFrame. The index of a DataFrame is a series of labels that identify each row. The labels can be integers, strings, or any other hashable type. The index is used for label-based access and alignment, and can be accessed or modified using this attribute. Returns ------- pandas.Index The index labels of the DataFrame. See Also -------- DataFrame.columns : The column labels of the DataFrame. DataFrame.to_numpy : Convert the DataFrame to a NumPy array. Examples -------- >>> df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'], ... 'Age': [25, 30, 35], ... 'Location': ['Seattle', 'New York', 'Kona']}, ... index=([10, 20, 30])) >>> df.index Index([10, 20, 30], dtype='int64') In this example, we create a DataFrame with 3 rows and 3 columns, including Name, Age, and Location information. We set the index labels to be the integers 10, 20, and 30. We then access the `index` attribute of the DataFrame, which returns an `Index` object containing the index labels. >>> df.index = [100, 200, 300] >>> df Name Age Location 100 Alice 25 Seattle 200 Bob 30 New York 300 Aritra 35 Kona In this example, we modify the index labels of the DataFrame by assigning a new list of labels to the `index` attribute. The DataFrame is then updated with the new labels, and the output shows the modified DataFrame. )rr*ap The column labels of the DataFrame. Examples -------- >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) >>> df A B 0 1 3 1 2 4 >>> df.columns Index(['A', 'B'], dtype='object') plotc  URn[[[US55nUR 5R 5VVs0sH-up#X R X3RS9RU5_M/ snn$s snnf)z| Return a dict of dtype -> Constructor Types that each is a homogeneous dtype. Internal ONLY - only works for BlockManager blockr) rrrprtr(rrrr)rrrrs r_to_dict_of_blocksDataFrame._to_dict_of_blocks1sxii<C!9:,,. . ))!&&)9FFtL L.   s4Bc6URR5$)a Return a Numpy representation of the DataFrame. .. warning:: We recommend using :meth:`DataFrame.to_numpy` instead. Only the values in the DataFrame will be returned, the axes labels will be removed. Returns ------- numpy.ndarray The values of the DataFrame. See Also -------- DataFrame.to_numpy : Recommended alternative to this method. DataFrame.index : Retrieve the index labels. DataFrame.columns : Retrieving the column names. Notes ----- The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By :func:`numpy.find_common_type` convention, mixing int64 and uint64 will result in a float64 dtype. Examples -------- A DataFrame where all columns are the same type (e.g., int64) results in an array of the same type. >>> df = pd.DataFrame({'age': [ 3, 29], ... 'height': [94, 170], ... 'weight': [31, 115]}) >>> df age height weight 0 3 94 31 1 29 170 115 >>> df.dtypes age int64 height int64 weight int64 dtype: object >>> df.values array([[ 3, 94, 31], [ 29, 170, 115]]) A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object). >>> df2 = pd.DataFrame([('parrot', 24.0, 'second'), ... ('lion', 80.5, 1), ... ('monkey', np.nan, None)], ... columns=('name', 'max_speed', 'rank')) >>> df2.dtypes name object max_speed float64 rank object dtype: object >>> df2.values array([['parrot', 24.0, 'second'], ['lion', 80.5, 1], ['monkey', nan, None]], dtype=object) )rrrs rr DataFrame.values.1sTyy!!##r)rr)rIzCallable[..., DataFrame]r)rIr})NNNNN) r Axes | Nonerr r Dtype | Noner bool | NonerINone)FT)r(r#r&r#rI DataFrameXchg)r, str | NonerIrr)rIz list[Index])rIztuple[int, int])rIr#)rIrO)rIr)rIr%)...................)(r\r#rr r,int | list[int] | dict[Hashable, int] | Nonerbool | SequenceNotStr[str]rr#rrrfmt.FormattersType | Nonerfmt.FloatFormatType | Nonerr"rr#rr%rV int | Nonerr*rr#rrrr*rr*rr*rr%rIr)..................)(r\FilePath | WriteBuffer[str]rr rr&rr'rr#rrrr(rr)rr"rr#rr%rVr*rr*rr#rrrr*rr*rr*rr%rIr#)NNNTTrNNNTNNNFrNNNN)(r\"FilePath | WriteBuffer[str] | Nonerr rr&rr'rr#rrrr(rr)rr"rr#rr%rVr*rr*rr#rrrr*rr*rr*rr%rIr%) rFloatFormatType | Nonerr%rrrrrIr)rIr)rIz!Iterable[tuple[Hashable, Series]])TPandas)rr#rr%rIzIterable[tuple[Any, ...]])rIrx)rr}rIr})rzDataFrame | Index | ArrayLikerIr)rzAnyArrayLike | DataFramerIr)rNN) rrrrrr!rr rIr)rznpt.DTypeLike | Nonerr#rrrIr)rr#rz list[int]rIr).)r)......................)0r\r+rr rColspaceArgType | Nonerr#rr#rrrFormattersType | Nonerr-rr"rr#rr%rVr*rr*r bool | strrrrr#rstr | list | tuple | Nonerr#rr#rint | bool | Nonerr%rr#rr%rIr#).......................)0r\r#rr rr?rr#rr#rrrr@rr-rr"rr#rr%rVr*rr*rrArrrr#rrBrr#rr#rrCrr%rr#rr%rIr)NNNTTrNNNTNNNFrTNTFNNFN)0r\r,rr rr?rr#rr#rrrr@rr-rr"rr#rr%rVr*rr*rrArrrr#rrBrr#rr#rrCrr%rr#rr%rIr%)"rr#rr#rr%rr%rr%rr9rr9rdict[str | None, str] | Nonerr%rrrr"rr"rXMLParsers | Noner5FilePath | ReadBuffer[str] | ReadBuffer[bytes] | Nonerrr|r6rIr)"rz0FilePath | WriteBuffer[bytes] | WriteBuffer[str]rr#rr%rr%rr%rr9rr9rrDrr%rrrr"rr"rrErrFrrr|r6rIr#)NTrr!NNNNNzutf-8TTrNrN)"rz7FilePath | WriteBuffer[bytes] | WriteBuffer[str] | Nonerr#rr%rr%rr%rr9rr9rrDrr%rrrr"rr"rrErrFrrr|r6rIr%) rr"r\zWriteBuffer[str] | Nonerr*rzbool | str | Nonerr"rIr#)TF)rr#rr#rIr})rr#rIr)r)rrxrrrIr})rrxrIr)rIzIterator[ArrayLike])rr)F)rFr#rIr)rIr#)rr)rIr#)rqrrIr#)FN)r?zint | slice | np.ndarrayrr#rLBlockValuesRefs | NonerIr#)rqrrLrGrIr#)T)r?rxrqr}rr#rIr#)rrrqrrFr#rIr#)r rr?rxrIr})rrrIr})rqr}rr#rIr#)rrrLiteral[False]rIr)rrr Literal[True]rIr#)rrrr#rIDataFrame | None)rrrrHrIr)rrrr#rIz Any | None)NN)rIr) r?rxrdrrqzScalar | AnyArrayLikerbool | lib.NoDefaultrIr#)rI(tuple[ArrayLike, BlockValuesRefs | None])rzdict[str, Index]rr#rIr)rrrr"rIr)r Axis | NonerzReindexMethod | Nonerr"r Level | Nonerz Scalar | Nonerr*rIr)rrrrrrrrrrrrIrrrIr#)rrrrrrrrrrrrHrrrIr)rrrrrrrrrrrr#rrrIrJ)rIndexLabel | NonerrrrOrrOrrNrr#rrrIrJ)r)Renamer | NonerrPrrPrrMrr"rrIrrrrrIr#)r)rPrrPrrPrrMrr"rrHrrrrrIr)r)rPrrPrrPrrMrr"rr#rrrrrIrJ)r)rPrrPrrPrrMrr"rr#rrNrrrIrJ)r4zdict[Hashable, tuple[Any, Any]]rr#) r=zint | Sequence[int]r>Frequency | NonerrrrrFr%rIr) rKr#rr#rrHryr#rIr) rKr#rr#rrIryr#rIr#) rKr#rr#rr#ryr#rIrJ)rrrKr#rrHrarrbrrrKr $Hashable | Sequence[Hashable] | NonerIr)rrrKr#rrIrarrbrrrKr rRrIr#)rrrKr#rr#rarrbrrrKr rRrIrJ)rrOrKr#rr#rarrbrrrKr rRrIrJ)rrrAnyAll | lib.NoDefaultrint | lib.NoDefaultrrrrHrr#rIr)rrrrSrrTrrrrIrr#rIr#)rrrrSrrTrrOrr#rr#rIrJ) rrRr$rrrIrr#rIr#) rrRr$rrrHrr#rIr) rrRr$rrr#rr#rIrJ)Nr)rrRr$rrIr})rrrrrrHrZrrrrr#rrrIr)rrrrrrIrZrrrrr#rrrIr#)rrrrrz$bool | list[bool] | tuple[bool, ...]rr#rZrrrrr#rzValueKeyFunc | NonerIrJ)rrrrrbool | Sequence[bool]rrIrZrrrrr#rr#rrrIr#)rrrrrrUrrHrZrrrrr#rr#rrrIr)rrrrrrUrr#rZrrrrr#rr#rrrIrJ)rrrrOrrUrr#rZrrrrr#rr#rzIndexKeyFunc | NonerIrJ)NFTFT) rrOrr#rr#rr#rr#rIr})r)rXrxrrr$rrIr)rNr)rrrrrrrIr)rzSequence[int | str]rrrIr)rrrzAxisInt | NonerIr)rr)rrrIr)rrxrIr#)rrrr"rrN)rGr}rrr)rIztuple[DataFrame, DataFrame])rN)rrrIr)rFFr) rrrrrr#rr#rrrIr)rrrz-Callable[[Series, Series], Series | Hashable]rr#rIr)rrrIr)rTNr)rrrr#rrrIr#)rzAxis | lib.NoDefaultrrOrr#rr#rr#rrKrr#rIr)rrrr#rr#rrrrKrr#rIr)rrrrKrrKrr#)rdrrr#rIr)rNNT)rrrr#)NNNrqNT)rrrarNrr#rIr)rr)r=rxrrrIr)rrrrxrzDataFrame | Series | NonerIr)Nr)rrrrrIr)rFNr{compatpythonN)rrrrrr#rz/Literal['expand', 'reduce', 'broadcast'] | NonerzLiteral[False, 'compat']rzLiteral['python', 'numba']rzdict[str, bool] | None)rrrr%rIr)rrrzNaAction | NonerIr)FFF)rr#ryr#rr#rIr)Nrr>r>FN)rz1DataFrame | Series | Iterable[DataFrame | Series]rrOrrr!rr"rrr#rzJoinValidate | NonerIr) rNNNFFF)_x_yNFN)rrrrr IndexLabel | AnyArrayLike | Nonerr[r)r[rr#rr#rr#rrrr"r*z str | boolrzMergeValidate | NonerIr)r.z$int | dict[IndexLabel, int] | SeriesrIr)r<rF)rrrHrxrIr#rIr)NrF)rHr*rSr*rIr#rIr)rFr<F) rrrrrKr#rrrIr#rIr})rF)rrrIr#)rrrrrjr#rIr#)rrrjr#rIr})rrMrr#rjr#rIz Series | bool)rFT)rTF)rrMrjr#rIr#)rTFr)rrMrjr#rIr#rrx)rTrF)rrMrjr#rSrxrIr#)rrMrjr#)rT)rrrr#rIr})rrrjr#rIr#rIr})rrxrIrf)rrrIr#rr#rIr) rrrrrIr#rrrLiteral['single', 'table']rIr})....) rzAnyArrayLike | Sequence[float]rrrIr#rrrr\rISeries | DataFrame) rz&float | AnyArrayLike | Sequence[float]rrrIr#rrrr\rIr])g?rFlinearr)NrrN) r>rQrrrrrr"rIr)NrN)r>rQrrrr"rIr)r z'Series | DataFrame | Sequence | MappingrIr)rIr( r __module__ __qualname____firstlineno____doc__rb_internal_names_set_typr}rfr[rr_HANDLED_TYPESr__annotations__rr__pandas_priority__propertyrrrrrr)r1r;rrCrGrKrRrWrsrxrrrrhr)r(r|common_docstringreturn_docstringrrr~r'rrrrrrr classmethodrrrrr"r(rr2rarvrzr*rrr_shared_doc_kwargsrrrrrrrvrrrQrrrr r3r-r(rHrQrer^r`rnr_rarNrrrcrrr rrrrrrrrrrMrrrrrrKrrr8r@rJrerNrrOrrr'rrrrrrrr rrr_logical_methodrrrrrr2r9rrBrErIrS make_flex_docrKrQrTrWrZr]rRrerisubtractrlromultiplyrrrur@dividerxrdivr{r~rArr r!rrrrSrrrrrrr|rr_agg_see_also_doc_agg_examples_docrrrrrr rr _merge_docrr3r@rTrdrrtrrcrUr:rgrjrrrrrrr]rrkurtosisproductrrrrrrrrgrrrrr rrr _AXIS_LENrrr AxisPropertyrrrUrplotting PlotAccessorr hist_framehist boxplot_frameboxplotr^rrr __static_attributes__ __classcell__)rs@rrrs>DL%g.1L1LL De^RZZ@N$:J%$+$9$9IbM$IM>I %% %"28.7 -$!#" rr r  r  r rn=A,@,@59,@ ,@^,0 V( V  V*:** 22*AA4 . .:%-"^  -*X"BE-00336 #!"" #!$"#&")   @  +     . 1       ! "# $% &!' () * +  0 #BE-00336 #!"" #!$"#&") (  @  +     . 1       ! "# $% &!' () * +  0$VUO+)2K  4 4c>R>RS37#BF-10437 $ "## %!%##'#)J /JJ@ J + J  JJ.J1JJJJJJJ !J"#J$%J&!'J()J* +JT JX>-> >  >  > >,2. dl7#$.%.4n6>RR(2R "Rh     t@l     + "+"# }L}L}L }L  }L  }L}LB'+>> @#@@ @  @D)-EN ,PS   L 6          " 6       PS   L           "        $VX$6Y  p= p=  p=p= 1p=p=d$V-@$A "& $"(%)48#!E E E  E  E  E #E 2E E E  E E N" a1  a1a1 a1a1HDH^U^U ^U@ #!% /1  /1  /1 /1/1b $%67()>?&H59 -1/3!%6:!26*115@DS+S2 S  S + S-SS4SS0S(S/S>S S Sj)>$VUO-  )$%67, 83715  /  /  5:.R>RS37#,0,0/3 $ "##&+-1$(#"#1l /l l * l  l  l l *l -l l l l l l $l !l "#l $+%l &'l ()l *"+l ,-l ./l 01l 2 3l Tl \ #  #" &)&)36 '*$'$'LO*-14%         $ $ 1   % " " J! "(# $/% & '  ,  #" &)&)36 '*$'$'LO*-14% H        $ $ 1   % " " J! "(# $/% & '  ,$V-=$>X $%67()>?BRR SW &$!&*&*37!'+$($*LP*115#G,OG,G, G,  G,  G,$G,$G,1G,G,G,%G,"G,"G,JG, (!G,"/#G,$ %G, G,T ,+, $'+#*.#'   %   (  !    - &ccJ-2v=v=p  >  D( 0*BH86 *D$#$#L(FT('T&3P*,X/ 0,l'+ ! % ! ! % !  !EI'#'+A' '"'' -6GL++-3+?C+ +ZV. &!*  8"    27g6g6RNQNQj25 M8 M8M8% M8 / M8  M8^BH(TWW $,0 8 . )*+<=)=" g&&'  > >  >  >>(/>>  )+,>?  '+ "$&FF    %   "       6!  !!                     !  !"%!                      !  !!                     %)\ #'%)"%\ !\  \ ! \ # \ \ \ \  \ \ |"%  !$"%!                       "%  !$"%"%!                        "%  !$"%!                       "&L !%"&  "&L L  L  L  L L L L L  L L \)&)&V &6 &AE &D 09:()!%"~~! o $o o  o  o  o  o o ;o b "%!$                !$               !&J J  J  JJ JX   "%! 146:            /  4          ! 146:            /  4          ! 146:            /  4       $(t146:t t t  t  tt/t4t tr /898:8  /89:  09:; 09:; &)&) "%     $  $              &)&)      $  $             &)nn&)nn$("^^$ ^ $ ^ " ^^^ ^@8;   4          8; "%  4          8;   4          8<_!" _4_ _  _  _ _F8< F>4F>F>  F>V "%"%                                              $:>$!"#'kC kC kC 8 kC  kCkCkCkC!kC kCZ+."%"       )                    +."%"%"       )                    +."%"       )                    $#'+/$"(#"#'k k ! k ) k  k k  k k k !k  k k ^%) D!DD D  D  D DNJQwVwV)wV1FwV wVtJQnWnW)nW1FnW nW`  ) * 2 54 =|}=| 55t0 0$O=AJ #J +9J J X(*X<"" HH H  HT :*3$40"&0@&  ;D400ck23QQ4Qck23QQ4Qck23QQ4Qck23QQ4Qck23QQ4Qck23QQ4Qc{34DH   5 c 45DH   6 c{34DH   5 H c 45DH   6 c{34DH   5 H c 45DH   6 c ;78DH   9  C F c K89DH   :  D c K89DH   : c [9:DH   ; c{34DH   5 c 45DH   6 c{34DH   5 c 45DH   6  Ym o `!)esn  !2             ish ( qAqA<qA  qA  qAfOCh"& p?p? p?  p? p?he g iTl9%(::; %(^^#'),- #- ! -  -  - - '- -  - '*~~%(^^" R9R9%R9# R9  R9n#~;~;~;  ~;@C;C;Jl6" V%LLM&"&!,  ,  ,, ,N,,  :? A HR11SHR16-1  *   2  ) +Z [! )  '""  C[! )  ' /0'+   GK +3-504L=L=L= L= E L=)L=+L=.L=^=A\;"\;/9\; \;~BF4="4=/>4= 4=x#!& 3:3: 3:  3:  3:p!%(,g@g g g  g  gg&g gR" j!$ /34859 !) %)- !   -  2  3        '   % D@Az)<z) z)B%." p8!p8p8 p8  p8h#'" @7@7@7 @7  @7J$-" F!FF F " F  F FVKUKUd"JJ  J  JJX8 %a !        "  %a !      "  %a !"     "  %a !"     "  %a !" 7 7 7 7  7 7" 7 &q !"" 8 8 8 8  8 8# 8 &q !""     #  ( #$"     %  %a !"       "  %a !"       "  %a !"       "  &q !""     #  &q !""     # HG( #$CC%C ( #$CC%C ( #$CC%C )Q  DD!D&D&DP h g>HM"@"@&*"@AE"@ "@?"@H h g>HM"@"@&*"@AE"@ "@?"@H LJNZZ,0ZBFZ Zx /2-0      -  +      /2-0  )    -  +     58 /2-0  1    -  +     58"/7-5 m< 1m<m< m< - m< + m< m"> ?D ?? % %Doo++G H&9 :F  I$I$I$rc[R"[5nUR5H%up#UR5H upEXQUU'M M' U$r)rrrr)rrrrr_rs rrr{1sI(3(?(?(EHJJLggiFC#$SM%  ! OrcURRU5(d[U5(dX[5(a-[ U[ 5(aUR UR4$UR R5S4$URU5R nUS4$![a.nURR(dUe[S5UeSnAff=f)Nz6incompatible index of inserted column with frame index) rr8rrrr}rRrrrrrr )rqrreindexed_valuers rrr1s  {{%  E  Zv%>%>==%"3"33 3}}!!#T)) --.66 D   {{$$I D   s B)) C!3)CC!)rIzcollections.defaultdict)rqrrrfrIrL(rb __future__rrrcollections.abcrrrrr rinspectr ior rMrr[textwrapr typingr rrrrrrnumpyrrpandas._configrrrpandas._config.configr pandas._libsrrBrrpandas._libs.hashtablerpandas._libs.libr pandas.compatrpandas.compat._constantsrpandas.compat._optionalrpandas.compat.numpyr r pandas.errorsr!r"r#r$r%r&pandas.util._decoratorsr'r(r)r*pandas.util._exceptionsr+r,pandas.util._validatorsr-r.r/pandas.core.dtypes.castr0r1r2r3r4r5r6r7r8pandas.core.dtypes.commonr9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIpandas.core.dtypes.concatrJpandas.core.dtypes.dtypesrKrLrMpandas.core.dtypes.missingrNrO pandas.corerPrQrirRrSrTpandas.core.accessorrUrrVpandas.core.array_algos.takerWpandas.core.arraylikerXpandas.core.arraysrYrZr[r\r]pandas.core.arrays.sparser^pandas.core.constructionr_r`rapandas.core.genericrbrcpandas.core.indexersrdpandas.core.indexes.apirerfrgrhrirjpandas.core.indexes.multirkrlpandas.core.indexingrmrnpandas.core.internalsrorp"pandas.core.internals.constructionrqrrrsrtrurvrwrxryrzpandas.core.methodsr{pandas.core.reshape.meltr|pandas.core.seriesr}pandas.core.shared_docsr~pandas.core.sortingrrrpandas.io.commonrpandas.io.formatsrrr|pandas.io.formats.inforrrpandas.plottingrdatetimepandas._libs.internalsrpandas._typingrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr*pandas.core.interchange.dataframe_protocolrr$rrrrlrurrr{rrrs, #  . .-.>.    &4  0<6*: 2   ()%0 ( 61111111111111f=U7.  7 :K% %@F Z{}${}$|{! !&+!-!r