aVg ddlmZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZmZddlmZdd lmZe r dd lmZmZmZed Zed Zed ZedZedZ ddeeeee ddZ!edZ"edZ#dddede"e#ddZ$edZ%d9dZ&d:dZ' d; d) annotations)ABCabstractmethodN)dedent) TYPE_CHECKINGIterableIteratorMappingSequence get_option)Dtype WriteBuffer)format) pprint_thing) 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.zd null_counts : bool, optional .. deprecated:: 1.2.0 Use show_counts instead.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_subnull_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() Int64Index: 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) Int64Index: 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}{null_counts_sub} Returns ------- None This method prints a summary of a {klass} and returns None. See Also -------- {see_also_sub} Examples -------- {examples_sub} c<t|d|j|S)a 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 W/var/www/html/hubwallet-dev/venv/lib/python3.12/site-packages/pandas/io/formats/info.py_put_strr$-s. q6&5>   &&cLdD]}|dkr |d|d|cS|dz}|d|dS)a{ 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_fmtr1GsQ0/ <$Z/q4 4 v $Z's ++r%c | td}|S)z5Get memory usage based on inputs and display options.zdisplay.memory_usager ) memory_usages r#_initialize_memory_usager4fs!"89 r%ceZdZUdZded<ded<eeddZeeddZeeddZ eedd Z edd Z edd Z e dd Z y )BaseInfoaj Base class for DataFrameInfo and SeriesInfo. Parameters ---------- data : DataFrame or Series Either dataframe or series. memory_usage : bool or str, optional If "deep", introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values. DataFrame | Seriesdata bool | strr3cy)z Dtypes. Returns ------- dtypes : sequence Dtype of each of the DataFrame's columns (or one series column). Nr-selfs r#dtypeszBaseInfo.dtypesr%cy)!Mapping dtype - number of counts.Nr-r;s r# dtype_countszBaseInfo.dtype_countsr>r%cy)BSequence of non-null counts for all columns or column (if series).Nr-r;s r#non_null_countszBaseInfo.non_null_countsr>r%cy)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nr-r;s r#memory_usage_byteszBaseInfo.memory_usage_bytesr>r%cHt|j|jdS)z0Memory usage in a form of human readable string. )r1rFr/r;s r#memory_usage_stringzBaseInfo.memory_usage_strings%d55t7J7JKLBOOr%cd}|jrC|jdk7r4d|jvs$|jjj rd}|S)Nrdeepobject+)r3rAr8index_is_memory_usage_qualified)r<r/s r#r/zBaseInfo.size_qualifiersM     F*  1 11yyAAC%(Nr%cyNr-)r<bufmax_colsverbose show_countss r#renderzBaseInfo.renders r%NreturnzIterable[Dtype]rXMapping[str, int]rX Sequence[int]rXintrXr rRWriteBuffer[str] | NonerS int | NonerT bool | NonerUrcrXNone)__name__ __module__ __qualname____doc____annotations__propertyrr=rArDrFrIr/rVr-r%r#r6r6os     00QQ  PP   %     !     r%r6ceZdZdZ d d dZed dZeddZeddZeddZ eddZ edd Z dd Z y) DataFrameInfoz0 Class storing dataframe-specific info. Nc2||_t||_yrQr8r4r3r<r8r3s r#__init__zDataFrameInfo.__init__s $ 4\Br%c,t|jSrQ)_get_dataframe_dtype_countsr8r;s r#rAzDataFrameInfo.dtype_countss*49955r%c.|jjS)z Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r8r=r;s r#r=zDataFrameInfo.dtypessyyr%c.|jjS)zz Column names. Returns ------- ids : Index DataFrame's column names. )r8columnsr;s r#idszDataFrameInfo.idssyy   r%c,t|jSz#Number of columns to be summarized.)lenrwr;s r# col_countzDataFrameInfo.col_counts488}r%c6|jjS)rCr8countr;s r#rDzDataFrameInfo.non_null_countssyy  r%c|jdk(rd}nd}|jjd|jS)NrKTFrNrK)r3r8sumr<rKs r#rFz DataFrameInfo.memory_usage_bytess=    &DDyy%%Dt%<@@BBr%cDt||||}|j|y)N)inforSrTrU)DataFrameInfoPrinter to_bufferr<rRrSrTrUprinters r#rVzDataFrameInfo.renders*'#   #r%rQ)r8rr3bool | str | NonerXrdrYrWrXrr]r[r`) rerfrgrhrprjrAr=rwr{rDrFrVr-r%r#rlrls+/CC(C  C66     ! !!!CC%    !  r%rlceZdZdZ d d dZddddd d dZed dZeddZeddZ edd Z y) SeriesInfoz- Class storing series-specific info. Nc2||_t||_yrQrnros r#rpzSeriesInfo.__init__s ! 4\Br%)rRrSrTrUc\| tdt|||}|j|y)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rrTrU) ValueErrorSeriesInfoPrinterrrs r#rVzSeriesInfo.rendersA  5 $#  #r%c8|jjgSrQr}r;s r#rDzSeriesInfo.non_null_counts/s !""r%c0|jjgSrQrtr;s r#r=zSeriesInfo.dtypes3s   !!r%cDddlm}t||jS)Nr)r)pandas.core.framerrrr8)r<rs r#rAzSeriesInfo.dtype_counts7s/*9TYY+?@@r%cd|jdk(rd}nd}|jjd|S)zMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. rKTFr)r3r8rs r#rFzSeriesInfo.memory_usage_bytes=s6    &DDyy%%Dt%<r%rQ)rRrarXrd)rXTableBuilderAbstract)rerfrgrhrrrr-r%r#rrMs )00r%rceZdZdZ d d dZed dZeddZeddZed dZ ddZ dd Z dd Z y)ra{ Class for printing dataframe info. Parameters ---------- info : DataFrameInfo Instance of DataFrameInfo. max_cols : int, optional When to switch from the verbose to the truncated output. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. Nc||_|j|_||_|j||_|j ||_yrQ)rr8rT_initialize_max_colsrS_initialize_show_countsrU)r<rrSrTrUs r#rpzDataFrameInfoPrinter.__init__osB II  11(; 77 Dr%cFtdt|jdzS)z"Maximum info rows to be displayed.zdisplay.max_info_rows)r rzr8r;s r#max_rowszDataFrameInfoPrinter.max_rows|s13tyy>A3EFFr%cFt|j|jkDS)zDCheck if number of columns to be summarized does not exceed maximum.)boolr{rSr;s r#exceeds_info_colsz&DataFrameInfoPrinter.exceeds_info_colssDNNT]]233r%cXtt|j|jkDS)zACheck if number of rows to be summarized does not exceed maximum.)rrzr8rr;s r#exceeds_info_rowsz&DataFrameInfoPrinter.exceeds_info_rowss C NT]]233r%c.|jjSryrr{r;s r#r{zDataFrameInfoPrinter.col_countyy"""r%c<|td|jdzS|S)Nzdisplay.max_info_columnsr)r r{)r<rSs r#rz)DataFrameInfoPrinter._initialize_max_colss$  8$..1:LM Mr%cT|%t|j xr |j S|SrQ)rrrr<rUs r#rz,DataFrameInfoPrinter._initialize_show_countss0  D222Q4;Q;Q7QR R r%c*|jr!t|j|jS|jdurt |jS|j rt |jSt|j|jS)z[ Create instance of table builder based on verbosity and display settings. r with_countsFr)rTDataFrameTableBuilderVerboserrUDataFrameTableBuilderNonVerboserr;s r#rz*DataFrameInfoPrinter._create_table_buildersz <</YY ,, \\U "2 B B%%6DIIFF3 $ 0 0r%)NNN) rrlrSrbrTrcrUrcrXrdr]rXr)rSrbrXr^rUrcrXr)rXDataFrameTableBuilder) rerfrgrhrprjrrrr{rrrr-r%r#rr_s $ $##' E E E E ! E  EGG4444##  r%rc<eZdZdZ d ddZddZd dZy) raClass 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. Ncn||_|j|_||_|j||_yrQ)rr8rTrrU)r<rrTrUs r#rpzSeriesInfoPrinter.__init__s0  II  77 Dr%c|js |j!t|j|jSt |jS)zF Create instance of table builder based on verbosity. rr)rTSeriesTableBuilderVerboserrUSeriesTableBuilderNonVerboser;s r#rz'SeriesInfoPrinter._create_table_buildersB <<4<</,YY ,,  0TYY? ?r%c |y|S)NTr-rs r#rz)SeriesInfoPrinter._initialize_show_countss   r%)NN)rrrTrcrUrcrXrd)rXSeriesTableBuilderr)rerfrgrhrprrr-r%r#rrsJ  $#' E E E! E  E @r%rceZdZUdZded<ded<eddZeddZeddZ edd Z edd Z edd Z edd Z dd ZddZddZy)rz* Abstract builder for info table. list[str]_linesr6rcy)z-Product in a form of list of lines (strings).Nr-r;s r#rzTableBuilderAbstract.get_linesr>r%c.|jjSrQrr8r;s r#r8zTableBuilderAbstract.datasyy~~r%c.|jjS)z*Dtypes of each of the DataFrame's columns.)rr=r;s r#r=zTableBuilderAbstract.dtypessyyr%c.|jjS)r@)rrAr;s r#rAz!TableBuilderAbstract.dtype_countssyy%%%r%c@t|jjS)z Whether to display memory usage.)rrr3r;s r#display_memory_usagez)TableBuilderAbstract.display_memory_usagesDII**++r%c.|jjS)z/Memory usage string with proper size qualifier.)rrIr;s r#rIz(TableBuilderAbstract.memory_usage_stringsyy,,,r%c.|jjSrQ)rrDr;s r#rDz$TableBuilderAbstract.non_null_countssyy(((r%cr|jjtt|jy)z>Add line with string representation of dataframe to the table.N)rappendrtyper8r;s r#add_object_type_linez)TableBuilderAbstract.add_object_type_lines! 3tDII/0r%c~|jj|jjj y)z,Add line with range of indices to the table.N)rrr8rN_summaryr;s r#add_index_range_linez)TableBuilderAbstract.add_index_range_lines% 499??3356r%ct|jjDcgc]\}}|d|dd}}}|jj ddj |ycc}}w)z2Add summary line with dtypes present in dataframe.(d)zdtypes: z, N)sortedrAitemsrrjoin)r<keyvalcollected_dtypess r#add_dtypes_linez$TableBuilderAbstract.add_dtypes_line sq/5T5F5F5L5L5N.O "*#sse1SG1    Xdii0@&A%BCD sA+NrXr)rXr7rWrYrr_r[rXrd)rerfrgrhrirrrjr8r=rArrIrDrrrr-r%r#rrs  N<<  &&,,--))17Er%rcxeZdZdZd dZd dZd dZed dZe ddZ e ddZ e ddZ d d Z y )rz Abstract builder for dataframe info table. Parameters ---------- info : DataFrameInfo. Instance of DataFrameInfo. c||_yrQrr<rs r#rpzDataFrameTableBuilder.__init__s #' r%cg|_|jdk(r|j|jS|j|jS)Nr)rr{_fill_empty_info_fill_non_empty_infor;s r#rzDataFrameTableBuilder.get_lines sE >>Q   ! ! #{{  % % '{{r%c|j|j|jjdt |j j dy)z;Add lines to the info table, pertaining to empty dataframe.zEmpty rHN)rrrrrr8rer;s r#rz&DataFrameTableBuilder._fill_empty_info(sD !!# !!# VDO$<$<#=R@Ar%cyz?Add lines to the info table, pertaining to non-empty dataframe.Nr-r;s r#rz*DataFrameTableBuilder._fill_non_empty_info.r>r%c.|jjS)z DataFrame.rr;s r#r8zDataFrameTableBuilder.data2yy~~r%c.|jjS)zDataframe columns.)rrwr;s r#rwzDataFrameTableBuilder.ids7syy}}r%c.|jjS)z-Number of dataframe columns to be summarized.rr;s r#r{zDataFrameTableBuilder.col_count<rr%cT|jjd|jyz!Add line containing memory usage.zmemory usage: NrrrIr;s r#add_memory_usage_linez+DataFrameTableBuilder.add_memory_usage_lineA" ^D,D,D+EFGr%N)rrlrXrdrr)rXrrr])rerfrgrhrprrrrrjr8rwr{rr-r%r#rrso(B NN##Hr%rc eZdZdZddZddZy)rz> Dataframe info table builder for non-verbose output. c|j|j|j|j|jr|j yyr)rradd_columns_summary_linerrrr;s r#rz4DataFrameTableBuilderNonVerbose._fill_non_empty_infoKsL !!# !!# %%'   $ $  & & ( %r%cn|jj|jjdy)NColumnsname)rrrwrr;s r#rz8DataFrameTableBuilderNonVerbose.add_columns_summary_lineTs& 488,,),<=r%Nr)rerfrgrhrrr-r%r#rrFs)>r%rceZdZUdZdZded<ded<ded<d ed <eedd Zedd Z dd Z ddZ ddZ eddZ eddZddZddZddZddZddZy)TableBuilderVerboseMixinz( Mixin for verbose info output. z rSPACINGzSequence[Sequence[str]]strrowsr\gross_column_widthsrrcy).Headers names of the columns in verbose table.Nr-r;s r#headersz TableBuilderVerboseMixin.headersbr>r%cR|jDcgc] }t|c}Scc}w)z'Widths of header columns (only titles).)rrzr<cols r#header_column_widthsz-TableBuilderVerboseMixin.header_column_widthsgs %)LL1SC111s$c|j}t|j|Dcgc] }t| c}Scc}w)zAGet widths of columns containing both headers and actual content.)_get_body_column_widthszipr max)r<body_column_widthswidthss r#_get_gross_column_widthsz1TableBuilderVerboseMixin._get_gross_column_widthslsE!99;d779KL  L   s;ctt|j}|Dcgc]}td|Dc}Scc}w)z$Get widths of table content columns.c32K|]}t|ywrQ)rz).0r0s r# zCTableBuilderVerboseMixin._get_body_column_widths..ws(qCF(s)listr rr )r<strcolsrs r#r z0TableBuilderVerboseMixin._get_body_column_widthsts4+/T\\0B+C4;r%cyz@Iterator with string representation of body data without counts.Nr-r;s r#rz1TableBuilderVerboseMixin._gen_rows_without_countsr>r%c |jjt|j|jDcgc]\}}t ||c}}}|j j|ycc}}wrQ)rrr rrr$rr)r<header col_width header_lines r#add_header_linez(TableBuilderVerboseMixin.add_header_linescll''*-T\\4;S;S)T %FI+  ;'  sA3 c |jjt|j|jDcgc]\}}t d|z|c}}}|j j|ycc}}w)N-)rrr r rr$rr)r<header_colwidthgross_colwidthseparator_lines r#add_separator_linez+TableBuilderVerboseMixin.add_separator_linesm**8;--t/G/G8 3O^.?   >* sA6 c |jD]i}|jjt||jDcgc]\}}t ||c}}}|j j|kycc}}wrQ)rrrr rr$rr)r<rowrr' body_lines r#add_body_linesz'TableBuilderVerboseMixin.add_body_linessr<< *C ))0338P8P/Q+^S.1I KK  y ) *sA: c#<K|jD] }|d yw)z7Iterator with string representation of non-null counts.z non-nullN)rD)r<r~s r#_gen_non_null_countsz-TableBuilderVerboseMixin._gen_non_null_countss()) &EG9% % &sc#HK|jD]}t|yw)z5Iterator with string representation of column dtypes.N)r=r)r<dtypes r# _gen_dtypesz$TableBuilderVerboseMixin._gen_dtypess$[[ &Eu% % & "NrXz Sequence[str]r[rXzIterator[Sequence[str]]rrXz Iterator[str])rerfrgrhrrirjrrr rr rrrr#r)r-r/r2r-r%r#rrXsGS $$&& ==22 = 3LLOO( +*& &r%rcfeZdZdZ d dZd dZed dZd dZddZ ddZ ddZ dd Z y )rz: Dataframe info table builder for verbose output. c||_||_t|j|_|j |_yrQrrrrrrrr<rrs r#rpz%DataFrameTableBuilderVerbose.__init__7  &04T^^5E0F 262O2O2Q r%c|j|j|j|j|j |j |j |jr|jyyr) rrrr#r)r-rrrr;s r#rz1DataFrameTableBuilderVerbose._fill_non_empty_infosp !!# !!# %%'  !    $ $  & & ( %r%c*|jrgdSgdS)r) # ColumnNon-Null Countr)r>r?rrr;s r#rz$DataFrameTableBuilderVerbose.headerss   ? ?))r%cV|jjd|jdy)NzData columns (total z columns):)rrr{r;s r#rz5DataFrameTableBuilderVerbose.add_columns_summary_lines# 1$..1ALMr%c#Kt|j|j|jEd{y7wr)r _gen_line_numbers _gen_columnsr2r;s r#rz5DataFrameTableBuilderVerbose._gen_rows_without_countss<  " " $           s;AAAc#Kt|j|j|j|j Ed{y7wr)r rDrEr/r2r;s r#rz2DataFrameTableBuilderVerbose._gen_rows_with_countssH  " " $      % % '        sA A A Ac#TKt|jD] \}}d|yw)z6Iterator with string representation of column numbers.r,N) enumeraterw)r<i_s r#rDz.DataFrameTableBuilderVerbose._gen_line_numberss-dhh' DAqaS'M s&(c#HK|jD]}t|yw)z4Iterator with string representation of column names.N)rwrrs r#rEz)DataFrameTableBuilderVerbose._gen_columnss$88 $Cs# # $r3N)rrlrrrXrdrr4r5r6) rerfrgrhrprrjrrrrrDrEr-r%r#rrsa R R R  R )** N   $r%rcLeZdZdZddZd dZed dZd dZe d dZ y) rz Abstract builder for series info table. Parameters ---------- info : SeriesInfo. Instance of SeriesInfo. c||_yrQrrs r#rpzSeriesTableBuilder.__init__s $ r%cHg|_|j|jSrQ)rrr;s r#rzSeriesTableBuilder.get_liness  !!#{{r%c.|jjS)zSeries.rr;s r#r8zSeriesTableBuilder.data rr%cT|jjd|jyrrr;s r#rz(SeriesTableBuilder.add_memory_usage_linerr%cyzr%N)rrrXrdr)rXrr) rerfrgrhrprrjr8rrrr-r%r#rrsA% HKKr%rceZdZdZddZy)rz; Series info table builder for non-verbose output. c|j|j|j|jr|j yyrR)rrrrrr;s r#rz1SeriesTableBuilderNonVerbose._fill_non_empty_infos@ !!# !!#   $ $  & & ( %r%Nr)rerfrgrhrr-r%r#rrs )r%rcVeZdZdZ d dZd dZd dZed dZd dZ d dZ y) rz7 Series info table builder for verbose output. c||_||_t|j|_|j |_yrQr9r:s r#rpz"SeriesTableBuilderVerbose.__init__*r;r%c|j|j|j|j|j |j |j |jr|jyyrR) rradd_series_name_liner#r)r-rrrr;s r#rz.SeriesTableBuilderVerbose._fill_non_empty_info5sp !!# !!# !!#  !    $ $  & & ( %r%ch|jjd|jjy)Nz Series name: )rrr8rr;s r#rXz.SeriesTableBuilderVerbose.add_series_name_lineAs$ ]499>>*:;z-_get_dataframe_dtype_counts..\s affr%)r= value_countsgroupbyr)dfs r#rrrrWs, 99 ! ! # + +,< = A A CCr%)r!z str | Dtyper"r^rXr)r.floatr/rrXrrQ)r3rrXr9)rbrrXrZ)8 __future__rabcrrrtextwraprtypingrrr r r pandas._configr pandas._typingrrpandas.io.formatsrrpandas.io.formats.printingrpandasrrrframe_max_cols_subrrframe_examples_subframe_see_also_subframe_sub_kwargsseries_examples_subseries_see_also_subseries_sub_kwargsINFO_DOCSTRINGr$r1r4r6rlrrrrrrrrrrrrrrr-r%r#rus" & ,3@?(QSlB&&&&& 9;|4&''6 .0f'4,@'+#P sP fIHIX<=<=~00$N.Nb(+(V5E35Ep0H00Hf>&;>$Z&3Z&z?$#8:R?$DK-K@ )#5 )/ 24L/ dDr%