!hp ddlmZddlmZmZddlmZmZmZm Z ddl Z ddl Z ddl mZddlmZmZddlmZddlmZdd lmZmZmZdd lmZdd lmZmZddlm cm!Z"dd l#m$Z$dd l%m&Z&ddl'm(Z(m)Z)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0erddl1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7edee$ddddddddddej8df dHd)Z9dId+Z: dJdKd.Z; dLdMd/Z< dLdNd0Z= dLdOd1Z>d2Z?edee$d3dej8ej8d4dPd:Z@ dQdRd=ZA dLdSd>ZBdTdUdBZCdVdGZDdS)W) annotations)HashableSequence) TYPE_CHECKINGCallableLiteralcastN)lib)Appender Substitution)find_stack_level)maybe_downcast_to_dtype) is_list_likeis_nested_list_like is_scalar)ExtensionDtype) ABCDataFrame ABCSeries) _shared_docs)Grouper)Index MultiIndexget_objs_combined_axis)concat)cartesian_product)Series) AggFuncTypeAggFuncTypeBaseAggFuncTypeDict IndexLabel DataFramez data : DataFrame pivot_table)indentsmeanFTAlldatar"aggfuncrmarginsbooldropna margins_namerobservedbool | lib.NoDefaultsortreturnc t|}t|}t|trg} g} |D]U} t|||||| |||| |  }| || t | d| Vt | | d}||dSt|||||||||| | }||dS)N) valuesindexcolumns fill_valuer)r*r,r-r.r0__name__r$)keysaxisr#)method) _convert_by isinstancelist__internal_pivot_tableappendgetattrr __finalize__)r(r3r4r5r)r6r*r,r-r.r0piecesr8func_tabletables Sc:\xampp_lite_8_4\www\timesheet\venv\Lib\site-packages\pandas/core/reshape/pivot.pyr#r#=s+   E'""G'4  >"$ 9 9D+%)!   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CN " "$*j11 >>>-=>>> ,'''+J77    W 65(A(A W(8,f55F''V $J(GG..tF|/C:.VVG__- . .FAGASFL MrG normalize/bool | Literal[0, 1, 'all', 'index', 'columns']c ||td||tdt|s|g}t|s|g}d} d||zD} | rt| dd} t||d }t||d }t ||\} } }}d d lm}itt| |tt||}||| }|d |d<td d}n ||d<d|i}|j d| ||||dd|}| durt|| ||}| | d }| |d}|S)a Compute a simple cross tabulation of two (or more) factors. By default, computes a frequency table of the factors unless an array of values and an aggregation function are passed. Parameters ---------- index : array-like, Series, or list of arrays/Series Values to group by in the rows. columns : array-like, Series, or list of arrays/Series Values to group by in the columns. values : array-like, optional Array of values to aggregate according to the factors. Requires `aggfunc` be specified. rownames : sequence, default None If passed, must match number of row arrays passed. colnames : sequence, default None If passed, must match number of column arrays passed. aggfunc : function, optional If specified, requires `values` be specified as well. margins : bool, default False Add row/column margins (subtotals). margins_name : str, default 'All' Name of the row/column that will contain the totals when margins is True. dropna : bool, default True Do not include columns whose entries are all NaN. normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False Normalize by dividing all values by the sum of values. - If passed 'all' or `True`, will normalize over all values. - If passed 'index' will normalize over each row. - If passed 'columns' will normalize over each column. - If margins is `True`, will also normalize margin values. Returns ------- DataFrame Cross tabulation of the data. See Also -------- DataFrame.pivot : Reshape data based on column values. pivot_table : Create a pivot table as a DataFrame. Notes ----- Any Series passed will have their name attributes used unless row or column names for the cross-tabulation are specified. Any input passed containing Categorical data will have **all** of its categories included in the cross-tabulation, even if the actual data does not contain any instances of a particular category. In the event that there aren't overlapping indexes an empty DataFrame will be returned. Reference :ref:`the user guide ` for more examples. Examples -------- >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar", ... "bar", "bar", "foo", "foo", "foo"], dtype=object) >>> b = np.array(["one", "one", "one", "two", "one", "one", ... "one", "two", "two", "two", "one"], dtype=object) >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny", ... "shiny", "dull", "shiny", "shiny", "shiny"], ... dtype=object) >>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c']) b one two c dull shiny dull shiny a bar 1 2 1 0 foo 2 2 1 2 Here 'c' and 'f' are not represented in the data and will not be shown in the output because dropna is True by default. Set dropna=False to preserve categories with no data. >>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c']) >>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f']) >>> pd.crosstab(foo, bar) col_0 d e row_0 a 1 0 b 0 1 >>> pd.crosstab(foo, bar, dropna=False) col_0 d e f row_0 a 1 0 0 b 0 1 0 c 0 0 0 Nz&aggfunc cannot be used without values.z)values cannot be used without an aggfunc.cJg|] }t|ttf|!Sr)r<rr)rMr}s rFrzcrosstab..s,XXXqz!i=V/W/WXXXXrGTF) intersectr0row)prefixrrr!r __dummy__)r)r6r))r4r5r*r-r,r.)rr*r-)r4r9r$)r5r9)r) r_rr _get_names_build_names_mapperrr"rzipr]r# _normalize rename_axis)r4r5r3rownamescolnamesr)r*r-r,r common_idx pass_objsrownames_mapperunique_rownamescolnames_mapperunique_colnamesr"r(dfkwargsrEs rFcrosstabrBsT~'-ABBB goDEEE u % % w ' ')JXXEGOXXXIS+IERRR %%888H'8E:::H Hh// !       s?E** + +  s?G,, - - D 4z * * *B ~; 22 ;W% BN !     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D HI&AA H H H=>>C G H% Q Dk  *RU+0  /LO4S T I GGGHH H 3B37+ z"crc'*  3B38$5IuEEE  ! !)M,=,=,?,??ME=1:::ELLOOE)EMM ' ! !',*:*:*<*<> >7888 Ls,, A AA 'A00 B:B  Brrrc|ig}t|D]V\}}t|tr"|j||j<||d|WnSt |t |krt dt|tst|}|S)N_z*arrays and names must have the same length) enumerater<rrr?r]AssertionErrorr=)arrsrVrr{arrs rFrr?s }oo . .FAs#y)) .ch.B SX&&&& ____----  . u::T " " !MNN N%&& KKE LrGr list[str]r;tuple[dict[str, str], list[str], dict[str, str], list[str]]cd}t|t|}||||z|zfdt|D}fdt|D}fdt|D}fdt|D}||||fS)a Given the names of a DataFrame's rows and columns, returns a set of unique row and column names and mappers that convert to original names. A row or column name is replaced if it is duplicate among the rows of the inputs, among the columns of the inputs or between the rows and the columns. Parameters ---------- rownames: list[str] colnames: list[str] Returns ------- Tuple(Dict[str, str], List[str], Dict[str, str], List[str]) rownames_mapper: dict[str, str] a dictionary with new row names as keys and original rownames as values unique_rownames: list[str] a list of rownames with duplicate names replaced by dummy names colnames_mapper: dict[str, str] a dictionary with new column names as keys and original column names as values unique_colnames: list[str] a list of column names with duplicate names replaced by dummy names c<tfd|DS)Nch|]}|v| Srr)rMrseens rF z>_build_names_mapper..get_duplicates..ps#;;;$d*:*:*:*:*:rG)r)rVrs @rFget_duplicatesz+_build_names_mapper..get_duplicatesns&EE;;;;;;;;rGc*i|]\}}|v d||Srow_rrMr{r dup_namess rF z'_build_names_mapper..u6$Qty?P?P q D?P?P?PrGc,g|]\}}|vrd|n|Srrrs rFrz'_build_names_mapper..x>6=adi'' q TrGc*i|]\}}|v d||Scol_rrs rFrz'_build_names_mapper..|r rGc,g|]\}}|vrd|n|Sr$rrs rFrz'_build_names_mapper..r"rG)r intersectionr) rrr shared_namesrrrrrs @rFrrPs'<<<<x==--c(mm<>(+C+CClRI(1((;(;OAJ8ATATO(1((;(;OAJ8ATATO O_o MMrG)r(r"r)rr*r+r,r+r-rr.r/r0r+r1r")r(r"r)rHr*r+r,r+r-rr.r/r0r+r1r")r'N)rErr(r"r.r+r-r)r')r(r"r-r)r(r"r.r+r-r)rEr"r(r"r.r+r-r) r(r"r5r r4rr3rr1r")NNNNFr'TF) r*r+r-rr,r+rrr1r")rEr"r*r+r-rr1r")r)rr)rrrrr1r)E __future__rcollections.abcrrtypingrrrr renumpyrs pandas._libsr pandas.util._decoratorsr r pandas.util._exceptionsr pandas.core.dtypes.castrpandas.core.dtypes.commonrrrpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrrpandas.core.commoncorecommonrpandas.core.framerpandas.core.groupbyrpandas.core.indexes.apirrrpandas.core.reshape.concatrpandas.core.reshape.utilrpandas.core.seriesrpandas._typingrrrr rr"r`r#r>rvrrrr;rrrrrrrGrFr>s"""""" 544444;;;;;; 544444 ! ******'''''' .-----666666%%%%%%!!     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