a Ofm@s,ddlmZddlmZmZddlZddlmZddlm Z ddl m Z ddl m ZddlmZe rdd lmZmZmZmZdd lmZmZdd lmZmZmZed Zed ZedZedZ ddeeee ddZ!edZ"edZ#dddee"e#ddZ$edZ%ddddddZ&dddd d!d"Z'dHd#d$d%d&d'Z(Gd(d)d)eZ)Gd*d+d+e)Z*Gd,d-d-e)Z+Gd.d/d/Z,Gd0d1d1e,Z-Gd2d3d3e,Z.Gd4d5d5eZ/Gd6d7d7e/Z0Gd8d9d9e0Z1Gd:d;d;e/Z2Gdd?d?e/Z4Gd@dAdAe4Z5GdBdCdCe4e2Z6ddDdEdFdGZ7dS)I) 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)klassZtype_subZ max_cols_subshow_counts_subZ examples_subZ see_also_subZversion_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} z str | Dtypeintstr)sspacereturncCst|d||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)rljust)rrrO/var/www/ai-form-bot/venv/lib/python3.9/site-packages/pandas/io/formats/info.py_put_str%srfloat)numsize_qualifierrcCsBdD],}|dkr(|d|d|S|d}q|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' )bytesZKBMBGBTBg@z3.1f z PBr)r!r"xrrr _sizeof_fmt?s  r)bool | str | None bool | str) memory_usagercCs|durtd}|S)z5Get memory usage based on inputs and display options.Nzdisplay.memory_usager)r,rrr_initialize_memory_usage^sr-c@seZdZUdZded<ded<eedddd Zeed dd d Zeed dddZ eeddddZ eddddZ eddddZ eddddddddZ dS) _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 | Seriesdatar+r,Iterable[Dtype]rcCsdS)z Dtypes. Returns ------- dtypes : sequence Dtype of each of the DataFrame's columns (or one series column). Nrselfrrrdtypesxsz_BaseInfo.dtypesMapping[str, int]cCsdS)!Mapping dtype - number of counts.Nrr3rrr dtype_countssz_BaseInfo.dtype_counts Sequence[int]cCsdS)BSequence of non-null counts for all columns or column (if series).Nrr3rrrnon_null_countssz_BaseInfo.non_null_countsrcCsdS)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nrr3rrrmemory_usage_bytessz_BaseInfo.memory_usage_bytesrcCst|j|jdS)z0Memory usage in a form of human readable string. )r)r<r"r3rrrmemory_usage_stringsz_BaseInfo.memory_usage_stringcCs2d}|jr.|jdkr.d|jvs*|jjr.d}|S)Nrdeepobject+)r,r8r0indexZ_is_memory_usage_qualified)r4r"rrrr"s  z_BaseInfo.size_qualifierWriteBuffer[str] | None int | None bool | NoneNonebufmax_colsverbose show_countsrcCsdSNr)r4rHrIrJrKrrrrenders z_BaseInfo.renderN)__name__ __module__ __qualname____doc____annotations__propertyrr5r8r;r<r>r"rMrrrrr.gs*    r.c@seZdZdZd!ddddddZed d d d Zed d ddZedd ddZedd ddZ edd ddZ edd ddZ dddddddd Z dS)" DataFrameInfoz0 Class storing dataframe-specific info. Nrr*rFr0r,rcCs||_t||_dSrLr0r-r,r4r0r,rrr__init__szDataFrameInfo.__init__r6r2cCs t|jSrL)_get_dataframe_dtype_countsr0r3rrrr8szDataFrameInfo.dtype_countsr1cCs|jjS)z Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r0r5r3rrrr5s zDataFrameInfo.dtypesrcCs|jjS)zz Column names. Returns ------- ids : Index DataFrame's column names. )r0columnsr3rrridss zDataFrameInfo.idsrcCs t|jSz#Number of columns to be summarized.)lenr\r3rrr col_countszDataFrameInfo.col_countr9cCs |jS)r:r0countr3rrrr;szDataFrameInfo.non_null_countscCs|jdk}|jjd|dS)Nr?TrBr?)r,r0sumr4r?rrrr<s z DataFrameInfo.memory_usage_bytesrCrDrErGcCst||||d}||dS)N)inforIrJrK)_DataFrameInfoPrinter to_bufferr4rHrIrJrKprinterrrrrMszDataFrameInfo.render)N) rNrOrPrQrXrSr8r5r\r_r;r<rMrrrrrTs   rTc@seZdZdZdddddddZddddd d d d d dd ddZeddddZeddddZeddddZ eddddZ dS) SeriesInfoz- Class storing series-specific info. Nrr*rFrUcCs||_t||_dSrLrVrWrrrrXszSeriesInfo.__init__)rHrIrJrKrCrDrErGcCs,|durtdt|||d}||dS)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rerJrK) ValueError_SeriesInfoPrinterrgrhrrrrMszSeriesInfo.renderr9r2cCs |jgSrLr`r3rrrr;$szSeriesInfo.non_null_countsr1cCs |jjgSrLrZr3rrrr5(szSeriesInfo.dtypesr6cCsddlm}t||jS)Nr)r)Zpandas.core.framerrYr0)r4rrrrr8,s zSeriesInfo.dtype_countsrcCs|jdk}|jjd|dS)zMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. r?Trb)r,r0rdrrrr<2s zSeriesInfo.memory_usage_bytes)N) rNrOrPrQrXrMrSr;r5r8r<rrrrrjs  rjc@s4eZdZdZd dddddZedd d d ZdS) _InfoPrinterAbstractz6 Class for printing dataframe or series info. NrCrF)rHrcCs.|}|}|durtj}t||dS)z Save dataframe info into buffer.N)_create_table_builder get_linessysstdoutfmtZbuffer_put_lines)r4rHZ table_builderlinesrrrrgDs z_InfoPrinterAbstract.to_buffer_TableBuilderAbstractr2cCsdS)z!Create instance of table builder.Nrr3rrrrnLsz*_InfoPrinterAbstract._create_table_builder)N)rNrOrPrQrgrrnrrrrrm?srmc@seZdZdZddddddddd Zed d d d Zedd ddZedd ddZed d ddZ dd dddZ dddddZ dd ddZ dS)rfa{ Class for printing dataframe info. Parameters ---------- info : DataFrameInfo Instance of DataFrameInfo. max_cols : int, optional When to switch from the verbose to the truncated output. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. NrTrDrErF)rerIrJrKrcCs0||_|j|_||_|||_|||_dSrL)rer0rJ_initialize_max_colsrI_initialize_show_countsrK)r4rerIrJrKrrrrXas  z_DataFrameInfoPrinter.__init__rr2cCstdt|jdS)z"Maximum info rows to be displayed.zdisplay.max_info_rows)rr^r0r3rrrmax_rowsnsz_DataFrameInfoPrinter.max_rowsboolcCst|j|jkS)zDCheck if number of columns to be summarized does not exceed maximum.)ryr_rIr3rrrexceeds_info_colsssz'_DataFrameInfoPrinter.exceeds_info_colscCstt|j|jkS)zACheck if number of rows to be summarized does not exceed maximum.)ryr^r0rxr3rrrexceeds_info_rowsxsz'_DataFrameInfoPrinter.exceeds_info_rowscCs|jjSr]rer_r3rrrr_}sz_DataFrameInfoPrinter.col_count)rIrcCs|durtd|jdS|S)Nzdisplay.max_info_columnsrw)rr_)r4rIrrrrusz*_DataFrameInfoPrinter._initialize_max_colsrKrcCs$|durt|j o|j S|SdSrL)ryrzr{r4rKrrrrvsz-_DataFrameInfoPrinter._initialize_show_counts_DataFrameTableBuildercCsR|jrt|j|jdS|jdur,t|jdS|jr>t|jdSt|j|jdSdS)z[ Create instance of table builder based on verbosity and display settings. re with_countsFreN)rJ_DataFrameTableBuilderVerbosererK _DataFrameTableBuilderNonVerboserzr3rrrrns   z+_DataFrameInfoPrinter._create_table_builder)NNN) rNrOrPrQrXrSrxrzr{r_rurvrnrrrrrfQs  rfc@sDeZdZdZddddddddZd d d d Zdd dddZdS)rlaClass 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. NrjrErF)rerJrKrcCs$||_|j|_||_|||_dSrL)rer0rJrvrK)r4rerJrKrrrrXsz_SeriesInfoPrinter.__init___SeriesTableBuilderr2cCs0|js|jdur t|j|jdSt|jdSdS)zF Create instance of table builder based on verbosity. Nrr)rJ_SeriesTableBuilderVerbosererK_SeriesTableBuilderNonVerboser3rrrrns z(_SeriesInfoPrinter._create_table_builderryr}cCs|dur dS|SdS)NTrr~rrrrvsz*_SeriesInfoPrinter._initialize_show_counts)NN)rNrOrPrQrXrnrvrrrrrls   rlc@seZdZUdZded<ded<eddddZed dd d Zed dd dZ eddddZ eddddZ eddddZ eddddZ ddddZddddZddd d!Zd"S)#rtz* Abstract builder for info table. list[str]_linesr.rer2cCsdS)z-Product in a form of list of lines (strings).Nrr3rrrrosz_TableBuilderAbstract.get_linesr/cCs|jjSrLrer0r3rrrr0sz_TableBuilderAbstract.datar1cCs|jjS)z*Dtypes of each of the DataFrame's columns.)rer5r3rrrr5sz_TableBuilderAbstract.dtypesr6cCs|jjS)r7)rer8r3rrrr8sz"_TableBuilderAbstract.dtype_countsrycCs t|jjS)z Whether to display memory usage.)ryrer,r3rrrdisplay_memory_usagesz*_TableBuilderAbstract.display_memory_usagercCs|jjS)z/Memory usage string with proper size qualifier.)rer>r3rrrr>sz)_TableBuilderAbstract.memory_usage_stringr9cCs|jjSrL)rer;r3rrrr;sz%_TableBuilderAbstract.non_null_countsrFcCs|jtt|jdS)z>Add line with string representation of dataframe to the table.N)rappendrtyper0r3rrradd_object_type_linesz*_TableBuilderAbstract.add_object_type_linecCs|j|jjdS)z,Add line with range of indices to the table.N)rrr0rB_summaryr3rrradd_index_range_linesz*_TableBuilderAbstract.add_index_range_linecCs4ddt|jD}|jdd|dS)z2Add summary line with dtypes present in dataframe.cSs"g|]\}}|d|ddqS)(d)r).0keyvalrrr sz9_TableBuilderAbstract.add_dtypes_line..zdtypes: z, N)sortedr8itemsrrjoin)r4Zcollected_dtypesrrradd_dtypes_lines z%_TableBuilderAbstract.add_dtypes_lineN)rNrOrPrQrRrrorSr0r5r8rr>r;rrrrrrrrts( rtc@seZdZdZdddddZddd d Zddd d Zeddd dZe ddddZ e ddddZ e ddddZ ddddZ dS)rz Abstract builder for dataframe info table. Parameters ---------- info : DataFrameInfo. Instance of DataFrameInfo. rTrFrercCs ||_dSrLrr4rerrrrXsz_DataFrameTableBuilder.__init__rr2cCs(g|_|jdkr|n||jS)Nr)rr__fill_empty_info_fill_non_empty_infor3rrrros   z _DataFrameTableBuilder.get_linescCs0|||jdt|jjddS)z;Add lines to the info table, pertaining to empty dataframe.zEmpty r=N)rrrrrr0rNr3rrrrsz'_DataFrameTableBuilder._fill_empty_infocCsdSz?Add lines to the info table, pertaining to non-empty dataframe.Nrr3rrrrsz+_DataFrameTableBuilder._fill_non_empty_inforcCs|jjS)z DataFrame.rr3rrrr0#sz_DataFrameTableBuilder.datarcCs|jjS)zDataframe columns.)rer\r3rrrr\(sz_DataFrameTableBuilder.idsrcCs|jjS)z-Number of dataframe columns to be summarized.r|r3rrrr_-sz _DataFrameTableBuilder.col_countcCs|jd|jdSz!Add line containing memory usage.zmemory usage: Nrrr>r3rrradd_memory_usage_line2sz,_DataFrameTableBuilder.add_memory_usage_lineN)rNrOrPrQrXrorrrrSr0r\r_rrrrrrs rc@s,eZdZdZddddZddddZdS) rz> Dataframe info table builder for non-verbose output. rFr2cCs2|||||jr.|dSr)rradd_columns_summary_linerrrr3rrrr<s z5_DataFrameTableBuilderNonVerbose._fill_non_empty_infocCs|j|jjdddS)NColumnsname)rrr\rr3rrrrEsz9_DataFrameTableBuilderNonVerbose.add_columns_summary_lineN)rNrOrPrQrrrrrrr7s rc@seZdZUdZdZded<ded<ded<d ed <eed d d dZedd ddZ dd ddZ dd ddZ dd ddZ edd ddZ edd ddZdd ddZdd dd Zdd d!d"Zd#d d$d%Zd#d d&d'Zd(S))_TableBuilderVerboseMixinz( Mixin for verbose info output. z rSPACINGzSequence[Sequence[str]]strrowsr9gross_column_widthsryr Sequence[str]r2cCsdS).Headers names of the columns in verbose table.Nrr3rrrheadersSsz!_TableBuilderVerboseMixin.headerscCsdd|jDS)z'Widths of header columns (only titles).cSsg|] }t|qSrr^rcolrrrr[zB_TableBuilderVerboseMixin.header_column_widths..)rr3rrrheader_column_widthsXsz._TableBuilderVerboseMixin.header_column_widthscCs|}ddt|j|DS)zAGet widths of columns containing both headers and actual content.cSsg|] }t|qSrmax)rwidthsrrrr`szF_TableBuilderVerboseMixin._get_gross_column_widths..)_get_body_column_widthszipr)r4Zbody_column_widthsrrr_get_gross_column_widths]s z2_TableBuilderVerboseMixin._get_gross_column_widthscCstt|j}dd|DS)z$Get widths of table content columns.cSsg|]}tdd|DqS)css|]}t|VqdSrLr)rr(rrr hrzO_TableBuilderVerboseMixin._get_body_column_widths...rrrrrrhrzE_TableBuilderVerboseMixin._get_body_column_widths..)listrr)r4Zstrcolsrrrresz1_TableBuilderVerboseMixin._get_body_column_widthsIterator[Sequence[str]]cCs|jr|S|SdS)z Generator function yielding rows content. Each element represents a row comprising a sequence of strings. N)r_gen_rows_with_counts_gen_rows_without_countsr3rrr _gen_rowsjsz#_TableBuilderVerboseMixin._gen_rowscCsdSz=Iterator with string representation of body data with counts.Nrr3rrrrusz/_TableBuilderVerboseMixin._gen_rows_with_countscCsdSz@Iterator with string representation of body data without counts.Nrr3rrrrysz2_TableBuilderVerboseMixin._gen_rows_without_countsrFcCs0|jddt|j|jD}|j|dS)NcSsg|]\}}t||qSrr)rheaderZ col_widthrrrrsz=_TableBuilderVerboseMixin.add_header_line..)rrrrrrr)r4Z header_linerrradd_header_line}s  z)_TableBuilderVerboseMixin.add_header_linecCs0|jddt|j|jD}|j|dS)NcSsg|]\}}td||qS)-r)rZheader_colwidthgross_colwidthrrrrsz@_TableBuilderVerboseMixin.add_separator_line..)rrrrrrr)r4Zseparator_linerrradd_separator_linesz,_TableBuilderVerboseMixin.add_separator_linecCs:|jD].}|jddt||jD}|j|qdS)NcSsg|]\}}t||qSrr)rrrrrrrsz<_TableBuilderVerboseMixin.add_body_lines..)rrrrrrr)r4rowZ body_linerrradd_body_liness  z(_TableBuilderVerboseMixin.add_body_lines Iterator[str]ccs|jD]}|dVqdS)z7Iterator with string representation of non-null counts.z non-nullN)r;)r4rarrr_gen_non_null_countss z._TableBuilderVerboseMixin._gen_non_null_countsccs|jD]}t|VqdS)z5Iterator with string representation of column dtypes.N)r5r )r4Zdtyperrr _gen_dtypess z%_TableBuilderVerboseMixin._gen_dtypesN)rNrOrPrQrrRrSrrrrrrrrrrrrrrrrrrIs,      rc@seZdZdZddddddZddd d Zed dd d ZddddZddddZ ddddZ ddddZ ddddZ dS)rz: Dataframe info table builder for verbose output. rTryrFrerrcCs(||_||_t||_||_dSrLrerrrrrrr4rerrrrrXsz&_DataFrameTableBuilderVerbose.__init__r2cCsJ||||||||jrF|dSr) rrrrrrrrrr3rrrrsz2_DataFrameTableBuilderVerbose._fill_non_empty_inforcCs|jrgdSgdS)r) # ColumnNon-Null Countr)rrrrr3rrrrsz%_DataFrameTableBuilderVerbose.headerscCs|jd|jddS)NzData columns (total z columns):)rrr_r3rrrrsz6_DataFrameTableBuilderVerbose.add_columns_summary_linerccs"t|||EdHdSr)r_gen_line_numbers _gen_columnsrr3rrrrs z6_DataFrameTableBuilderVerbose._gen_rows_without_countsccs(t||||EdHdSr)rrrrrr3rrrrs z3_DataFrameTableBuilderVerbose._gen_rows_with_countsrccs$t|jD]\}}d|Vq dS)z6Iterator with string representation of column numbers.r'N) enumerater\)r4i_rrrrsz/_DataFrameTableBuilderVerbose._gen_line_numbersccs|jD]}t|VqdS)z4Iterator with string representation of column names.N)r\r )r4rrrrrs z*_DataFrameTableBuilderVerbose._gen_columnsN) rNrOrPrQrXrrSrrrrrrrrrrrs   rc@s`eZdZdZdddddZddd d Zed dd d ZddddZe ddddZ dS)rz Abstract builder for series info table. Parameters ---------- info : SeriesInfo. Instance of SeriesInfo. rjrFrcCs ||_dSrLrrrrrrXsz_SeriesTableBuilder.__init__rr2cCsg|_||jSrL)rrr3rrrrosz_SeriesTableBuilder.get_linesrcCs|jjS)zSeries.rr3rrrr0sz_SeriesTableBuilder.datacCs|jd|jdSrrr3rrrrsz)_SeriesTableBuilder.add_memory_usage_linecCsdSzMrz-_get_dataframe_dtype_counts..)r5Z value_countsgroupbyrc)rrrrrYHsrY)N)8 __future__rabcrrrptextwraprtypingrZpandas._configrZpandas.io.formatsr rrZpandas.io.formats.printingr collections.abcr r r rZpandas._typingrrZpandasrrrZframe_max_cols_subrZframe_examples_subZframe_see_also_subZframe_sub_kwargsZseries_examples_subZseries_see_also_subZseries_sub_kwargsZINFO_DOCSTRINGrr)r-r.rTrjrmrfrlrtrrrrrrrrYrrrrs        V  > 3  SI<P+83]B 2