o 5c @sddlmZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZmZddlmZdd lmZe rLdd lmZmZmZed Zed Zed ZedZedZ ddeeeee ddZ!edZ"edZ#dddede"e#ddZ$edZ%dKdd Z&dLd$d%Z' dMdNd)d*Z(Gd+d,d,eZ)Gd-d.d.e)Z*Gd/d0d0e)Z+Gd1d2d2Z,Gd3d4d4e,Z-Gd5d6d6e,Z.Gd7d8d8eZ/Gd9d:d:e/Z0Gd;d<dd>e/Z2Gd?d@d@e0e2Z3GdAdBdBe/Z4GdCdDdDe4Z5GdEdFdFe4e2Z6dOdIdJZ7dS)P) 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} s str | DtypespaceintreturnstrcCst|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)r"ljust)rrr$L/var/www/html/gps/gps/lib/python3.10/site-packages/pandas/io/formats/info.py_put_str-sr&numfloatsize_qualifiercCsBdD]}|dkr|d|d|S|d}q|d|dS)a{ Return size in human readable format. Parameters ---------- num : int Size in bytes. size_qualifier : str Either empty, or '+' (if lower bound). Returns ------- str Size in human readable format. Examples -------- >>> _sizeof_fmt(23028, '') '22.5 KB' >>> _sizeof_fmt(23028, '+') '22.5+ KB' )bytesKBMBGBTBg@z3.1f z PBr$)r'r)xr$r$r% _sizeof_fmtGs  r1 memory_usagebool | str | None bool | strcCs|durtd}|S)z5Get memory usage based on inputs and display options.Nzdisplay.memory_usager )r2r$r$r%_initialize_memory_usagefsr5c@seZdZUdZded<ded<eed#dd Zeed$d d Zeed%ddZ eed&ddZ ed'ddZ ed'ddZ ed(d d!Z d"S))BaseInfoaj Base class for DataFrameInfo and SeriesInfo. Parameters ---------- data : DataFrame or Series Either dataframe or series. memory_usage : bool or str, optional If "deep", introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values. DataFrame | Seriesdatar4r2r!Iterable[Dtype]cCdS)z Dtypes. Returns ------- dtypes : sequence Dtype of each of the DataFrame's columns (or one series column). Nr$selfr$r$r%dtypeszBaseInfo.dtypesMapping[str, int]cCr:)!Mapping dtype - number of counts.Nr$r;r$r$r% dtype_countsr>zBaseInfo.dtype_counts Sequence[int]cCr:)BSequence of non-null counts for all columns or column (if series).Nr$r;r$r$r%non_null_countsr>zBaseInfo.non_null_countsr cCr:)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nr$r;r$r$r%memory_usage_bytesr>zBaseInfo.memory_usage_bytesr"cCst|j|jdS)z0Memory usage in a form of human readable string. )r1rEr)r;r$r$r%memory_usage_stringzBaseInfo.memory_usage_stringcCs2d}|jr|jdkrd|jvs|jjrd}|S)Nrdeepobject+)r2rAr8index_is_memory_usage_qualified)r<r)r$r$r%r)s  zBaseInfo.size_qualifierbufWriteBuffer[str] | Nonemax_cols int | Noneverbose bool | None show_countsNonecCsdSNr$)r<rNrPrRrTr$r$r%renders zBaseInfo.renderNr!r9r!r?r!rBr!r r!r" rNrOrPrQrRrSrTrSr!rU)__name__ __module__ __qualname____doc____annotations__propertyrr=rArDrErGr)rWr$r$r$r%r6os,     r6c@s|eZdZdZ d%d&d d Zed'd d Zed(ddZed)ddZed*ddZ ed+ddZ ed*ddZ d,d#d$Z dS)- DataFrameInfoz0 Class storing dataframe-specific info. Nr8rr2r3r!rUcC||_t||_dSrVr8r5r2r<r8r2r$r$r%__init__zDataFrameInfo.__init__r?cC t|jSrV)_get_dataframe_dtype_countsr8r;r$r$r%rA zDataFrameInfo.dtype_countsr9cC|jjS)z Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r8r=r;r$r$r%r= zDataFrameInfo.dtypesrcCrm)zz Column names. Returns ------- ids : Index DataFrame's column names. )r8columnsr;r$r$r%idsrozDataFrameInfo.idsr cCrjz#Number of columns to be summarized.)lenrqr;r$r$r% col_count zDataFrameInfo.col_countrBcCs |jS)rCr8countr;r$r$r%rDruzDataFrameInfo.non_null_countscCs(|jdkrd}nd}|jjd|dS)NrITFrLrI)r2r8sumr<rIr$r$r%rEs z DataFrameInfo.memory_usage_bytesrNrOrPrQrRrSrTcCst||||d}||dS)N)inforPrRrT)DataFrameInfoPrinter to_bufferr<rNrPrRrTprinterr$r$r%rWszDataFrameInfo.renderrV)r8rr2r3r!rUrYrXr!rr[rZr]) r^r_r`rarhrcrAr=rqrtrDrErWr$r$r$r%rds"       rdc@sleZdZdZ d!d"d d Zddddd d#ddZed$ddZed%ddZed&ddZ ed'dd Z dS)( SeriesInfoz- Class storing series-specific info. Nr8rr2r3r!rUcCrerVrfrgr$r$r%rhrizSeriesInfo.__init__)rNrPrRrTrNrOrPrQrRrSrTcCs,|durtdt|||d}||dS)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)r{rRrT) ValueErrorSeriesInfoPrinterr}r~r$r$r%rWszSeriesInfo.renderrBcCs |jgSrVrvr;r$r$r%rD/s zSeriesInfo.non_null_countsr9cCs |jjgSrVrnr;r$r$r%r=3rlzSeriesInfo.dtypesr?cCsddlm}t||jS)Nr)r)pandas.core.framerrkr8)r<rr$r$r%rA7s zSeriesInfo.dtype_countsr cCs$|jdkrd}nd}|jjd|dS)zMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. rITFrx)r2r8rzr$r$r%rE=s zSeriesInfo.memory_usage_bytesrV)r8rr2r3r!rUr]rZrXrYr[) r^r_r`rarhrWrcrDr=rArEr$r$r$r%rs"     rc@s*eZdZdZd d ddZedd d ZdS)InfoPrinterAbstractz6 Class for printing dataframe or series info. NrNrOr!rUcCs.|}|}|durtj}t||dS)z Save dataframe info into buffer.N)_create_table_builder get_linessysstdoutfmtbuffer_put_lines)r<rN table_builderlinesr$r$r%r}Rs zInfoPrinterAbstract.to_bufferTableBuilderAbstractcCr:)z!Create instance of table builder.Nr$r;r$r$r%rZr>z)InfoPrinterAbstract._create_table_builderrV)rNrOr!rU)r!r)r^r_r`rar}rrr$r$r$r%rMs  rc@sxeZdZdZ   dd d d Zed!ddZed"ddZed"ddZed!ddZ d#ddZ d$ddZ d%ddZ dS)&r|a{ Class for printing dataframe info. Parameters ---------- info : DataFrameInfo Instance of DataFrameInfo. max_cols : int, optional When to switch from the verbose to the truncated output. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. Nr{rdrPrQrRrSrTr!rUcCs0||_|j|_||_|||_|||_dSrV)r{r8rR_initialize_max_colsrP_initialize_show_countsrT)r<r{rPrRrTr$r$r%rhos  zDataFrameInfoPrinter.__init__r cCstdt|jdS)z"Maximum info rows to be displayed.zdisplay.max_info_rows)r rsr8r;r$r$r%max_rows|rHzDataFrameInfoPrinter.max_rowsboolcCst|j|jkS)zDCheck if number of columns to be summarized does not exceed maximum.)rrtrPr;r$r$r%exceeds_info_colsz&DataFrameInfoPrinter.exceeds_info_colscCstt|j|jkS)zACheck if number of rows to be summarized does not exceed maximum.)rrsr8rr;r$r$r%exceeds_info_rowsrHz&DataFrameInfoPrinter.exceeds_info_rowscCrmrrr{rtr;r$r$r%rtzDataFrameInfoPrinter.col_countcCs|dur td|jdS|S)Nzdisplay.max_info_columnsr)r rt)r<rPr$r$r%rsz)DataFrameInfoPrinter._initialize_max_colscCs |durt|j o |j S|SrV)rrrr<rTr$r$r%rsz,DataFrameInfoPrinter._initialize_show_countsDataFrameTableBuildercCsN|jr t|j|jdS|jdurt|jdS|jrt|jdSt|j|jdS)z[ Create instance of table builder based on verbosity and display settings. r{ with_countsFr{)rRDataFrameTableBuilderVerboser{rTDataFrameTableBuilderNonVerboserr;r$r$r%rs   z*DataFrameInfoPrinter._create_table_builder)NNN) r{rdrPrQrRrSrTrSr!rUr[r!r)rPrQr!r rTrSr!r)r!r) r^r_r`rarhrcrrrrtrrrr$r$r$r%r|_s"       r|c@s4eZdZdZ  ddd d Zdd dZdddZdS)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. Nr{rrRrSrTr!rUcCs$||_|j|_||_|||_dSrV)r{r8rRrrT)r<r{rRrTr$r$r%rhszSeriesInfoPrinter.__init__SeriesTableBuildercCs,|js|jdurt|j|jdSt|jdS)zF Create instance of table builder based on verbosity. Nrr)rRSeriesTableBuilderVerboser{rTSeriesTableBuilderNonVerboser;r$r$r%rs  z'SeriesInfoPrinter._create_table_builderrcCs|durdS|S)NTr$rr$r$r%rsz)SeriesInfoPrinter._initialize_show_counts)NN)r{rrRrSrTrSr!rU)r!rr)r^r_r`rarhrrr$r$r$r%rs  rc@seZdZUdZded<ded<ed#ddZed$d d Zed%d dZ ed&ddZ ed'ddZ ed(ddZ ed)ddZ d*ddZd*ddZd*d d!Zd"S)+rz* Abstract builder for info table. list[str]_linesr6r{r!cCr:)z-Product in a form of list of lines (strings).Nr$r;r$r$r%rr>zTableBuilderAbstract.get_linesr7cCrmrVr{r8r;r$r$r%r8zTableBuilderAbstract.datar9cCrm)z*Dtypes of each of the DataFrame's columns.)r{r=r;r$r$r%r=rzTableBuilderAbstract.dtypesr?cCrm)r@)r{rAr;r$r$r%rArz!TableBuilderAbstract.dtype_countsrcCs t|jjS)z Whether to display memory usage.)rr{r2r;r$r$r%display_memory_usages z)TableBuilderAbstract.display_memory_usager"cCrm)z/Memory usage string with proper size qualifier.)r{rGr;r$r$r%rGrz(TableBuilderAbstract.memory_usage_stringrBcCrmrV)r{rDr;r$r$r%rDrz$TableBuilderAbstract.non_null_countsrUcCs|jtt|jdS)z>Add line with string representation of dataframe to the table.N)rappendr"typer8r;r$r$r%add_object_type_linesz)TableBuilderAbstract.add_object_type_linecCs|j|jjdS)z,Add line with range of indices to the table.N)rrr8rL_summaryr;r$r$r%add_index_range_linez)TableBuilderAbstract.add_index_range_linecCs4ddt|jD}|jdd|dS)z2Add summary line with dtypes present in dataframe.cSs"g|] \}}|d|ddqS)(d)r$).0keyvalr$r$r% sz8TableBuilderAbstract.add_dtypes_line..zdtypes: z, N)sortedrAitemsrrjoin)r<collected_dtypesr$r$r%add_dtypes_line s z$TableBuilderAbstract.add_dtypes_lineNr!r)r!r7rXrYrr\rZr!rU)r^r_r`rarbrrrcr8r=rArrGrDrrrr$r$r$r%rs*          rc@speZdZdZdddZdd d Zdd d Zedd dZe dddZ e dddZ e d ddZ dddZ dS)!rz Abstract builder for dataframe info table. 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Instance of DataFrameInfo. r{rdr!rUcC ||_dSrVrr<r{r$r$r%rh zDataFrameTableBuilder.__init__rcCs,g|_|jdkr||jS||jS)Nr)rrt_fill_empty_info_fill_non_empty_infor;r$r$r%r s  zDataFrameTableBuilder.get_linescCs0|||jdt|jjddS)z;Add lines to the info table, pertaining to empty dataframe.zEmpty rFN)rrrrrr8r^r;r$r$r%r(s z&DataFrameTableBuilder._fill_empty_infocCr:z?Add lines to the info table, pertaining to non-empty dataframe.Nr$r;r$r$r%r.r>z*DataFrameTableBuilder._fill_non_empty_inforcCrm)z DataFrame.rr;r$r$r%r82rzDataFrameTableBuilder.datarcCrm)zDataframe columns.)r{rqr;r$r$r%rq7rzDataFrameTableBuilder.idsr cCrm)z-Number of dataframe columns to be summarized.rr;r$r$r%rt<rzDataFrameTableBuilder.col_countcC|jd|jdSz!Add line containing memory usage.zmemory usage: NrrrGr;r$r$r%add_memory_usage_lineArz+DataFrameTableBuilder.add_memory_usage_lineN)r{rdr!rUrr)r!rrr[)r^r_r`rarhrrrrrcr8rqrtrr$r$r$r%rs      rc@s$eZdZdZd ddZd ddZdS) rz> Dataframe info table builder for non-verbose output. r!rUcCs6|||||jr|dSdSr)rradd_columns_summary_linerrrr;r$r$r%rKs z4DataFrameTableBuilderNonVerbose._fill_non_empty_infocCs|j|jjdddS)NColumnsname)rrrqrr;r$r$r%rTz8DataFrameTableBuilderNonVerbose.add_columns_summary_lineNr)r^r_r`rarrr$r$r$r%rFs  rc@seZdZUdZdZded<ded<ded<d ed <eed)d dZed*ddZ d*ddZ d*ddZ d+ddZ ed+ddZ ed+ddZd,ddZd,dd Zd,d!d"Zd-d$d%Zd-d&d'Zd(S).TableBuilderVerboseMixinz( Mixin for verbose info output. z r"SPACINGzSequence[Sequence[str]]strrowsrBgross_column_widthsrrr! 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Parameters ---------- info : SeriesInfo. 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