hmSSKJr SSKJrJr SSKrSSKJr SSKJ r SSK J r SSK J r SSKJr \ (aSS KJrJrJrJr SS KJrJr SS KJrJrJr \"S 5r\"S 5r\"S5r\"S5r SS\\\\ SS.r!\"S5r"\"S5r#SSS\\"\#SS.r$\"S5r%S9Sjr&S:Sjr'S;S) annotations)ABCabstractmethodN)dedent) TYPE_CHECKING get_option)format) pprint_thing)IterableIteratorMappingSequence)Dtype WriteBuffer) DataFrameIndexSeriesa max_cols : int, optional When to switch from the verbose to the truncated output. If the DataFrame has more than `max_cols` columns, the truncated output is used. By default, the setting in ``pandas.options.display.max_info_columns`` is used.aR show_counts : bool, optional Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than ``pandas.options.display.max_info_rows`` and ``pandas.options.display.max_info_columns``. A value of True always shows the counts, and False never shows the counts.a >>> int_values = [1, 2, 3, 4, 5] >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon'] >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0] >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values, ... "float_col": float_values}) >>> df int_col text_col float_col 0 1 alpha 0.00 1 2 beta 0.25 2 3 gamma 0.50 3 4 delta 0.75 4 5 epsilon 1.00 Prints information of all columns: >>> df.info(verbose=True) RangeIndex: 5 entries, 0 to 4 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 int_col 5 non-null int64 1 text_col 5 non-null object 2 float_col 5 non-null float64 dtypes: float64(1), int64(1), object(1) memory usage: 248.0+ bytes Prints a summary of columns count and its dtypes but not per column information: >>> df.info(verbose=False) RangeIndex: 5 entries, 0 to 4 Columns: 3 entries, int_col to float_col dtypes: float64(1), int64(1), object(1) memory usage: 248.0+ bytes Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file: >>> import io >>> buffer = io.StringIO() >>> df.info(buf=buffer) >>> s = buffer.getvalue() >>> with open("df_info.txt", "w", ... encoding="utf-8") as f: # doctest: +SKIP ... f.write(s) 260 The `memory_usage` parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization: >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6) >>> df = pd.DataFrame({ ... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6), ... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6), ... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6) ... }) >>> df.info() RangeIndex: 1000000 entries, 0 to 999999 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 column_1 1000000 non-null object 1 column_2 1000000 non-null object 2 column_3 1000000 non-null object dtypes: object(3) memory usage: 22.9+ MB >>> df.info(memory_usage='deep') RangeIndex: 1000000 entries, 0 to 999999 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 column_1 1000000 non-null object 1 column_2 1000000 non-null object 2 column_3 1000000 non-null object dtypes: object(3) memory usage: 165.9 MBz DataFrame.describe: Generate descriptive statistics of DataFrame columns. DataFrame.memory_usage: Memory usage of DataFrame columns.rz and columns)klasstype_sub max_cols_subshow_counts_sub examples_sub see_also_subversion_added_suba >>> int_values = [1, 2, 3, 4, 5] >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon'] >>> s = pd.Series(text_values, index=int_values) >>> s.info() Index: 5 entries, 1 to 5 Series name: None Non-Null Count Dtype -------------- ----- 5 non-null object dtypes: object(1) memory usage: 80.0+ bytes Prints a summary excluding information about its values: >>> s.info(verbose=False) Index: 5 entries, 1 to 5 dtypes: object(1) memory usage: 80.0+ bytes Pipe output of Series.info to buffer instead of sys.stdout, get buffer content and writes to a text file: >>> import io >>> buffer = io.StringIO() >>> s.info(buf=buffer) >>> s = buffer.getvalue() >>> with open("df_info.txt", "w", ... encoding="utf-8") as f: # doctest: +SKIP ... f.write(s) 260 The `memory_usage` parameter allows deep introspection mode, specially useful for big Series and fine-tune memory optimization: >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6) >>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6)) >>> s.info() RangeIndex: 1000000 entries, 0 to 999999 Series name: None Non-Null Count Dtype -------------- ----- 1000000 non-null object dtypes: object(1) memory usage: 7.6+ MB >>> s.info(memory_usage='deep') RangeIndex: 1000000 entries, 0 to 999999 Series name: None Non-Null Count Dtype -------------- ----- 1000000 non-null object dtypes: object(1) memory usage: 55.3 MBzp Series.describe: Generate descriptive statistics of Series. Series.memory_usage: Memory usage of Series.rz .. versionadded:: 1.4.0 a Print a concise summary of a {klass}. This method prints information about a {klass} including the index dtype{type_sub}, non-null values and memory usage. {version_added_sub} Parameters ---------- verbose : bool, optional Whether to print the full summary. By default, the setting in ``pandas.options.display.max_info_columns`` is followed. buf : writable buffer, defaults to sys.stdout Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output. {max_cols_sub} memory_usage : bool, str, optional Specifies whether total memory usage of the {klass} elements (including the index) should be displayed. By default, this follows the ``pandas.options.display.memory_usage`` setting. True always show memory usage. False never shows memory usage. A value of 'deep' is equivalent to "True with deep introspection". Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources. See the :ref:`Frequently Asked Questions ` for more details. {show_counts_sub} Returns ------- None This method prints a summary of a {klass} and returns None. See Also -------- {see_also_sub} Examples -------- {examples_sub} c<[U5SURU5$)as Make string of specified length, padding to the right if necessary. Parameters ---------- s : Union[str, Dtype] String to be formatted. space : int Length to force string to be of. Returns ------- str String coerced to given length. Examples -------- >>> pd.io.formats.info._put_str("panda", 6) 'panda ' >>> pd.io.formats.info._put_str("panda", 4) 'pand' N)strljust)sspaces ZC:\Users\psant\Downloads\Noiseback\school\venv\Lib\site-packages\pandas/io/formats/info.py_put_strr#%s. q6&5>   &&cRSHnUS:a USUSU3s $US-nM USUS3$)a3 Return size in human readable format. Parameters ---------- num : int Size in bytes. size_qualifier : str Either empty, or '+' (if lower bound). Returns ------- str Size in human readable format. Examples -------- >>> _sizeof_fmt(23028, '') '22.5 KB' >>> _sizeof_fmt(23028, '+') '22.5+ KB' )bytesKBMBGBTBg@z3.1f z PB)numsize_qualifierxs r" _sizeof_fmtr0?sL0/ <$Z/q4 4 v /$Z's ++r$c"Uc [S5nU$)z5Get memory usage based on inputs and display options.zdisplay.memory_usager) memory_usages r"_initialize_memory_usager3^s!"89 r$c\rSrSr%SrS\S'S\S'\\SSj55r\\SSj55r \\SS j55r \\SS j55r \SS j5r \SS j5r \SS j5rSrg) _BaseInfoigaB Base class for DataFrameInfo and SeriesInfo. Parameters ---------- data : DataFrame or Series Either dataframe or series. memory_usage : bool or str, optional If "deep", introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values. DataFrame | Seriesdata bool | strr2cg)zq Dtypes. Returns ------- dtypes : sequence Dtype of each of the DataFrame's columns (or one series column). Nr,selfs r"dtypes_BaseInfo.dtypesxr$cg)!Mapping dtype - number of counts.Nr,r:s r" dtype_counts_BaseInfo.dtype_countsr>r$cg)BSequence of non-null counts for all columns or column (if series).Nr,r:s r"non_null_counts_BaseInfo.non_null_countsr>r$cg)zl Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nr,r:s r"memory_usage_bytes_BaseInfo.memory_usage_bytesr>r$cH[URUR5S3$)z0Memory usage in a form of human readable string. )r0rHr.r:s r"memory_usage_string_BaseInfo.memory_usage_strings%d55t7J7JKLBOOr$cSnUR(aKURS:wa;SUR;d)URRR 5(aSnU$)Nrdeepobject+)r2rAr7index_is_memory_usage_qualified)r;r.s r"r._BaseInfo.size_qualifiersP     F*  1 11yyAACC%(Nr$cgNr,)r;bufmax_colsverbose show_countss r"render_BaseInfo.renders r$r,NreturnzIterable[Dtype]r^Mapping[str, int]r^ Sequence[int]r^intr^r rWWriteBuffer[str] | NonerX int | NonerY bool | NonerZrir^None)__name__ __module__ __qualname____firstlineno____doc____annotations__propertyrr<rArErHrLr.r[__static_attributes__r,r$r"r5r5gs     00QQ  PP   %     !     r$r5c\rSrSrSrS SSjjr\SSj5r\SSj5r\SSj5r \SSj5r \SS j5r \SS j5r SS jr S rg) DataFrameInfoiz( Class storing dataframe-specific info. Nc0Xl[U5UlgrVr7r3r2r;r7r2s r"__init__DataFrameInfo.__init__s $ 4\Br$c,[UR5$rV)_get_dataframe_dtype_countsr7r:s r"rADataFrameInfo.dtype_countss*49955r$c.URR$)zO Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r7r<r:s r"r<DataFrameInfo.dtypessyyr$c.URR$)zJ Column names. Returns ------- ids : Index DataFrame's column names. )r7columnsr:s r"idsDataFrameInfo.idssyy   r$c,[UR5$z#Number of columns to be summarized.)lenrr:s r" col_countDataFrameInfo.col_counts488}r$c6URR5$)rDr7countr:s r"rEDataFrameInfo.non_null_countssyy  r$cpURS:HnURRSUS9R5$)NrOTrRrO)r2r7sumr;rOs r"rH DataFrameInfo.memory_usage_bytess5  F*yy%%Dt%<@@BBr$c>[UUUUS9nURU5 g)N)inforXrYrZ)_DataFrameInfoPrinter to_bufferr;rWrXrYrZprinters r"r[DataFrameInfo.renders*(#   #r$r7r2rV)r7rr2bool | str | Noner^rjr_r]r^rrcrarf)rkrlrmrnrorxrqrAr<rrrErHr[rrr,r$r"rtrts+/CC(C  C66     ! !!!CC%    !  r$rtc\rSrSrSrS S SjjrSSSSS.SSjjr\SSj5r\SSj5r \SS j5r \SS j5r S r g) SeriesInfoiz% Class storing series-specific info. Nc0Xl[U5UlgrVrvrws r"rxSeriesInfo.__init__s ! 4\Br$)rWrXrYrZcXUb [S5e[UUUS9nURU5 g)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rrYrZ) ValueError_SeriesInfoPrinterrrs r"r[SeriesInfo.rendersA  5 %#  #r$c8URR5/$rVrr:s r"rESeriesInfo.non_null_counts$s !""r$c0URR/$rVr~r:s r"r<SeriesInfo.dtypes(s   !!r$cDSSKJn [U"UR55$)Nr)r)pandas.core.framerr{r7)r;rs r"rASeriesInfo.dtype_counts,s/*9TYY+?@@r$cTURS:HnURRSUS9$)zkMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. rOTr)r2r7rs r"rHSeriesInfo.memory_usage_bytes2s.  F*yy%%Dt%<r$r,rV)rWrgr^rj)r^_TableBuilderAbstract) rkrlrmrnrorrrrrr,r$r"rr?s )00r$rc\rSrSrSrS SSjjr\SSj5r\SSj5r\SSj5r \SSj5r SS jr SS jr SS jr S rg)riQaK Class for printing dataframe info. Parameters ---------- info : DataFrameInfo Instance of DataFrameInfo. max_cols : int, optional When to switch from the verbose to the truncated output. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. NcXlURUlX0lURU5UlUR U5UlgrV)rr7rY_initialize_max_colsrX_initialize_show_countsrZ)r;rrXrYrZs r"rx_DataFrameInfoPrinter.__init__as> II  11(; 77 Dr$cF[S[UR5S-5$)z"Maximum info rows to be displayed.zdisplay.max_info_rows)r rr7r:s r"max_rows_DataFrameInfoPrinter.max_rowsns13tyy>A3EFFr$cF[URUR:5$)zDCheck if number of columns to be summarized does not exceed maximum.)boolrrXr:s r"exceeds_info_cols'_DataFrameInfoPrinter.exceeds_info_colsssDNNT]]233r$cX[[UR5UR:5$)zACheck if number of rows to be summarized does not exceed maximum.)rrr7rr:s r"exceeds_info_rows'_DataFrameInfoPrinter.exceeds_info_rowsxs C NT]]233r$c.URR$rrrr:s r"r_DataFrameInfoPrinter.col_count}yy"""r$c>Uc[SURS-5$U$)Nzdisplay.max_info_columnsr)r r)r;rXs r"r*_DataFrameInfoPrinter._initialize_max_colss$  8$..1:LM Mr$cpUc2[UR(+=(a UR(+5$U$rV)rrrr;rZs r"r-_DataFrameInfoPrinter._initialize_show_countss0  D222Q4;Q;Q7QR R r$c(UR(a[URURS9$URSLa[ URS9$UR (a[ URS9$[URURS9$)zK Create instance of table builder based on verbosity and display settings. r with_countsFr)rY_DataFrameTableBuilderVerboserrZ _DataFrameTableBuilderNonVerboserr:s r"r+_DataFrameInfoPrinter._create_table_buildersz <<0YY ,, \\U "3C C  # #3C C0YY ,, r$)r7rrXrZrY)NNN) rrtrXrhrYrirZrir^rjrcr^r)rXrhr^rdrZrir^r)r^_DataFrameTableBuilder)rkrlrmrnrorxrqrrrrrrrrrr,r$r"rrQs $ $##' E E E E ! E  EGG4444##  r$rcL\rSrSrSrSS SjjrS SjrS SjrSrg) rizClass for printing series info. Parameters ---------- info : SeriesInfo Instance of SeriesInfo. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. NcjXlURUlX lURU5UlgrV)rr7rYrrZ)r;rrYrZs r"rx_SeriesInfoPrinter.__init__s,  II  77 Dr$cUR(d URc[URURS9$[ URS9$)z6 Create instance of table builder based on verbosity. rr)rY_SeriesTableBuilderVerboserrZ_SeriesTableBuilderNonVerboser:s r"r(_SeriesInfoPrinter._create_table_buildersB <<4<</-YY ,,  1dii@ @r$cUcgU$)NTr,rs r"r*_SeriesInfoPrinter._initialize_show_countss   r$)r7rrZrY)NN)rrrYrirZrir^rj)r^_SeriesTableBuilderr) rkrlrmrnrorxrrrrr,r$r"rrsJ  $#' E E E! E  E Ar$rc\rSrSr%SrS\S'S\S'\SSj5r\SSj5r \SS j5r \SS j5r \SS j5r \SS j5r \SS j5rSSjrSSjrSSjrSrg)riz" Abstract builder for info table. list[str]_linesr5rcg)z-Product in a form of list of lines (strings).Nr,r:s r"r_TableBuilderAbstract.get_linesr>r$c.URR$rVrr7r:s r"r7_TableBuilderAbstract.datasyy~~r$c.URR$)z*Dtypes of each of the DataFrame's columns.)rr<r:s r"r<_TableBuilderAbstract.dtypessyyr$c.URR$)r@)rrAr:s r"rA"_TableBuilderAbstract.dtype_countssyy%%%r$c@[URR5$)z Whether to display memory usage.)rrr2r:s r"display_memory_usage*_TableBuilderAbstract.display_memory_usagesDII**++r$c.URR$)z/Memory usage string with proper size qualifier.)rrLr:s r"rL)_TableBuilderAbstract.memory_usage_stringsyy,,,r$c.URR$rV)rrEr:s r"rE%_TableBuilderAbstract.non_null_countssyy(((r$crURR[[UR555 g)z>Add line with string representation of dataframe to the table.N)rappendrtyper7r:s r"add_object_type_line*_TableBuilderAbstract.add_object_type_lines! 3tDII/0r$c~URRURRR 55 g)z,Add line with range of indices to the table.N)rrr7rR_summaryr:s r"add_index_range_line*_TableBuilderAbstract.add_index_range_lines% 499??3356r$c[URR55VVs/sHupUSUSS3PM nnnURR SSR U535 gs snnf)z2Add summary line with dtypes present in dataframe.(d)zdtypes: z, N)sortedrAitemsrrjoin)r;keyvalcollected_dtypess r"add_dtypes_line%_TableBuilderAbstract.add_dtypes_linesq/5T5F5F5L5L5N.O .O(#se1SG1 .O   Xdii0@&A%BCD sA-r,Nr^r)r^r6r]r_rrerar^rj)rkrlrmrnrorprrrqr7r<rArrLrErrrrrr,r$r"rrs  O<<  &&,,--))17Er$rc\rSrSrSrS SjrSSjrSSjr\SSj5r \ SSj5r \ SSj5r \ SS j5r SS jrS rg )rizx Abstract builder for dataframe info table. Parameters ---------- info : DataFrameInfo. Instance of DataFrameInfo. cXlgrVrr;rs r"rx_DataFrameTableBuilder.__init__s#' r$c/UlURS:XaUR5 UR$UR5 UR$)Nr)rr_fill_empty_info_fill_non_empty_infor:s r"r _DataFrameTableBuilder.get_linessE >>Q   ! ! #{{  % % '{{r$cUR5 UR5 URRS[ UR 5R S35 g)z;Add lines to the info table, pertaining to empty dataframe.zEmpty rKN)rrrrrr7rkr:s r"r'_DataFrameTableBuilder._fill_empty_infosD !!# !!# VDO$<$<#=R@Ar$cgz?Add lines to the info table, pertaining to non-empty dataframe.Nr,r:s r"r+_DataFrameTableBuilder._fill_non_empty_infor>r$c.URR$)z DataFrame.rr:s r"r7_DataFrameTableBuilder.data#yy~~r$c.URR$)zDataframe columns.)rrr:s r"r_DataFrameTableBuilder.ids(syy}}r$c.URR$)z-Number of dataframe columns to be summarized.rr:s r"r _DataFrameTableBuilder.col_count-rr$cTURRSUR35 gz!Add line containing memory usage.zmemory usage: NrrrLr:s r"add_memory_usage_line,_DataFrameTableBuilder.add_memory_usage_line2" ^D,D,D+EFGr$rrN)rrtr^rjrr)r^rrrc)rkrlrmrnrorxrrrrrqr7rrr*rrr,r$r"rrso(B NN##Hr$rc,\rSrSrSrSSjrSSjrSrg)ri7z6 Dataframe info table builder for non-verbose output. cUR5 UR5 UR5 UR5 UR(aUR 5 ggr)rradd_columns_summary_linerrr*r:s r"r5_DataFrameTableBuilderNonVerbose._fill_non_empty_info<sL !!# !!# %%'   $ $  & & ( %r$chURRURRSS95 g)NColumnsname)rrrrr:s r"r09_DataFrameTableBuilderNonVerbose.add_columns_summary_lineEs& 488,,),<=r$r,Nr)rkrlrmrnrorr0rrr,r$r"rr7s)>r$rc\rSrSr%SrSrS\S'S\S'S\S 'S \S '\\SS j55r \SS j5r SSjr SSjr SSjr \SSj5r\SSj5rSSjrSSjrSSjrSSjrSSjrSrg)_TableBuilderVerboseMixiniIz Mixin for verbose info output. z rSPACINGzSequence[Sequence[str]]strrowsrbgross_column_widthsrrcg).Headers names of the columns in verbose table.Nr,r:s r"headers!_TableBuilderVerboseMixin.headersSr>r$cXURVs/sHn[U5PM sn$s snf)z'Widths of header columns (only titles).)r>rr;cols r"header_column_widths._TableBuilderVerboseMixin.header_column_widthsXs$%)LL1LSCL111s'cUR5n[URU5Vs/sH n[U6PM sn$s snf)zAGet widths of columns containing both headers and actual content.)_get_body_column_widthsziprCmax)r;body_column_widthswidthss r"_get_gross_column_widths2_TableBuilderVerboseMixin._get_gross_column_widths]sJ!99;d779KL L LL   s>c[[UR65nUVs/sHn[SU55PM sn$s snf)z$Get widths of table content columns.c38# UHn[U5v M g7frV)r).0r/s r" D_TableBuilderVerboseMixin._get_body_column_widths..hs(CqCFFCs)listrGr:rH)r;strcolsrBs r"rF1_TableBuilderVerboseMixin._get_body_column_widthses8+/T\\0B+C4;r$cgz@Iterator with string representation of body data without counts.Nr,r:s r"rW2_TableBuilderVerboseMixin._gen_rows_without_countsyr>r$c URR[URUR5VVs/sHup[ X5PM snn5nUR RU5 gs snnfrV)r9r rGr>r;r#rr)r;header col_width header_lines r"add_header_line)_TableBuilderVerboseMixin.add_header_line}sell''*-T\\4;S;S)T )T%F+)T  ;'  sA4 c URR[URUR5VVs/sHup[ SU-U5PM snn5nUR RU5 gs snnf)N-)r9r rGrCr;r#rr)r;header_colwidthgross_colwidthseparator_lines r"add_separator_line,_TableBuilderVerboseMixin.add_separator_linesu**8;--t/G/G8 83O.?8   >* sA8 c URHjnURR[XR5VVs/sHup#[ X#5PM snn5nUR RU5 Ml gs snnfrV)r:r9r rGr;r#rr)r;rowrBri body_lines r"add_body_lines(_TableBuilderVerboseMixin.add_body_linessm<rCrKrFrXrVrWrdrkrprsrwrrr,r$r"r8r8IsGS $$&& ==22 = 3LLOO( +*& &r$r8c~\rSrSrSrS SjrSSjr\SSj5rSSjr SSjr SSjr SS jr SS jr S rg )riz2 Dataframe info table builder for verbose output. cXlX l[UR55UlUR 5UlgrVrrrRrXr:rKr;r;rrs r"rx&_DataFrameTableBuilderVerbose.__init__3  &04T^^5E0F 262O2O2Q r$c(UR5 UR5 UR5 UR5 UR 5 UR 5 UR 5 UR(aUR5 ggr) rrr0rdrkrprrr*r:s r"r2_DataFrameTableBuilderVerbose._fill_non_empty_infosp !!# !!# %%'  !    $ $  & & ( %r$c4UR(a/SQ$/SQ$)r=) # ColumnNon-Null Countr)rrrrr:s r"r>%_DataFrameTableBuilderVerbose.headerss   ? ?))r$cVURRSURS35 g)NzData columns (total z columns):)rrrr:s r"r06_DataFrameTableBuilderVerbose.add_columns_summary_lines# 1$..1ALMr$c## [UR5UR5UR55ShvN gN7fr^)rG_gen_line_numbers _gen_columnsrwr:s r"rW6_DataFrameTableBuilderVerbose._gen_rows_without_countss;  " " $           s;AAAc## [UR5UR5UR5UR 55ShvN gN7fr[)rGrrrsrwr:s r"rV3_DataFrameTableBuilderVerbose._gen_rows_with_countssG  " " $      % % '        sA A A Ac#V# [UR5H upSU3v M g7f)z6Iterator with string representation of column numbers.r+N) enumerater)r;i_s r"r/_DataFrameTableBuilderVerbose._gen_line_numberss%dhh'DAaS'M(s')c#L# URHn[U5v M g7f)z4Iterator with string representation of column names.N)rr rAs r"r*_DataFrameTableBuilderVerbose._gen_columnss88Cs# #ryr;rr:rN)rrtrrr^rjrrzr{r|)rkrlrmrnrorxrrqr>r0rWrVrrrrr,r$r"rrsa R R R  R )** N   $r$rc^\rSrSrSrS SjrS Sjr\S Sj5rS Sjr \ S Sj5r Sr g )rizo Abstract builder for series info table. Parameters ---------- info : SeriesInfo. Instance of SeriesInfo. cXlgrVrrs r"rx_SeriesTableBuilder.__init__s $ r$cH/UlUR5 UR$rV)rrr:s r"r_SeriesTableBuilder.get_liness  !!#{{r$c.URR$)zSeries.rr:s r"r7_SeriesTableBuilder.datar"r$cTURRSUR35 gr(r)r:s r"r*)_SeriesTableBuilder.add_memory_usage_liner,r$cgzr$r-N)rrr^rjr)r^rr) rkrlrmrnrorxrrqr7r*rrrrr,r$r"rrsA% HKKr$rc"\rSrSrSrSSjrSrg)riz3 Series info table builder for non-verbose output. cUR5 UR5 UR5 UR(aUR 5 ggr)rrrrr*r:s r"r2_SeriesTableBuilderNonVerbose._fill_non_empty_info s@ !!# !!#   $ $  & & ( %r$r,Nr)rkrlrmrnrorrrr,r$r"rrs )r$rcj\rSrSrSrS SjrS SjrS Sjr\S Sj5r SSjr SSjr S r g )riz/ Series info table builder for verbose output. cXlX l[UR55UlUR 5UlgrVrrs r"rx#_SeriesTableBuilderVerbose.__init__rr$c(UR5 UR5 UR5 UR5 UR 5 UR 5 UR 5 UR(aUR5 ggr) rradd_series_name_linerdrkrprrr*r:s r"r/_SeriesTableBuilderVerbose._fill_non_empty_info&sp !!# !!# !!#  !    $ $  & & ( %r$chURRSURR35 g)Nz Series name: )rrr7r5r:s r"r/_SeriesTableBuilderVerbose.add_series_name_line2s$ ]499>>*:;"_SeriesTableBuilderVerbose.headers5s    $g. .yr$c#@# UR5ShvN gN7fr^)rwr:s r"rW3_SeriesTableBuilderVerbose._gen_rows_without_counts<s##%%%s c#p# [UR5UR55ShvN gN7fr[)rGrsrwr:s r"rV0_SeriesTableBuilderVerbose._gen_rows_with_counts@s/  % % '       s ,646rN)rrrrr^rjrrzr{) rkrlrmrnrorxrrrqr>rWrVrrr,r$r"rrsV R R R  R )= & r$rcrURR5RS5R5$)zC Create mapping between datatypes and their number of occurrences. cUR$rVr4)r/s r"-_get_dataframe_dtype_counts..Msaffr$)r< value_countsgroupbyr)dfs r"r{r{Hs, 99 ! ! # + +,< = A A CCr$)r z str | Dtyper!rdr^r)r-floatr.rr^rrV)r2rr^r8)rrr^r`)8 __future__rabcrrrtextwraprtypingrpandas._configr pandas.io.formatsr rpandas.io.formats.printingr collections.abcr r rrpandas._typingrrpandasrrrframe_max_cols_subrframe_examples_subframe_see_also_subframe_sub_kwargsseries_examples_subseries_see_also_subseries_sub_kwargsINFO_DOCSTRINGr#r0r3r5rtrrrrrrrr8rrrrr{r,r$r"rs"  %+3 @?QSlB&&&&9;|4&''6.0f'4,@'+#P P fFIFR9=9=x00$M0M`(-(V5EC5Ep0H20Hf>'=>$Z& 5Z&z?$$: