o hm@sddlmZddlmZmZddlZddlmZddlm Z ddl m Z ddl m ZddlmZe rPdd lmZmZmZmZdd lmZmZdd lmZmZmZed Zed ZedZedZ ddeeee ddZ!edZ"edZ#dddee"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_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} 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#j/var/www/html/construction_image-detection-poc/venv/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_fmt?s  r0 memory_usagebool | str | None bool | strcCs|durtd}|S)z5Get memory usage based on inputs and display options.Nzdisplay.memory_usager)r1r#r#r$_initialize_memory_usage^sr4c@seZdZUdZded<ded<eed#dd Zeed$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 | Seriesdatar3r1r Iterable[Dtype]cCdS)z Dtypes. Returns ------- dtypes : sequence Dtype of each of the DataFrame's columns (or one series column). Nr#selfr#r#r$dtypesxz_BaseInfo.dtypesMapping[str, int]cCr9)!Mapping dtype - number of counts.Nr#r:r#r#r$ dtype_countsr=z_BaseInfo.dtype_counts Sequence[int]cCr9)BSequence of non-null counts for all columns or column (if series).Nr#r:r#r#r$non_null_countsr=z_BaseInfo.non_null_countsrcCr9)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nr#r:r#r#r$memory_usage_bytesr=z_BaseInfo.memory_usage_bytesr!cCst|j|jdS)z0Memory usage in a form of human readable string. )r0rDr(r:r#r#r$memory_usage_stringz_BaseInfo.memory_usage_stringcCs2d}|jr|jdkrd|jvs|jjrd}|S)Nrdeepobject+)r1r@r7index_is_memory_usage_qualified)r;r(r#r#r$r(s   z_BaseInfo.size_qualifierbufWriteBuffer[str] | Nonemax_cols int | Noneverbose bool | None show_countsNonecCsdSNr#)r;rMrOrQrSr#r#r$renders z_BaseInfo.renderNr r8r r>r rAr rr r! rMrNrOrPrQrRrSrRr rT)__name__ __module__ __qualname____doc____annotations__propertyrr<r@rCrDrFr(rVr#r#r#r$r5gs,     r5c@s|eZdZdZ d%d&d d Zed'd d Zed(ddZed)ddZed*ddZ ed+ddZ ed*ddZ d,d#d$Z dS)- DataFrameInfoz0 Class storing dataframe-specific info. Nr7rr1r2r rTcC||_t||_dSrUr7r4r1r;r7r1r#r#r$__init__zDataFrameInfo.__init__r>cC t|jSrU)_get_dataframe_dtype_countsr7r:r#r#r$r@ zDataFrameInfo.dtype_countsr8cC|jjS)z Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r7r<r:r#r#r$r< zDataFrameInfo.dtypesrcCrl)zz Column names. Returns ------- ids : Index DataFrame's column names. )r7columnsr:r#r#r$idsrnzDataFrameInfo.idsrcCriz#Number of columns to be summarized.)lenrpr:r#r#r$ col_count zDataFrameInfo.col_countrAcCs |jS)rBr7countr:r#r#r$rCrtzDataFrameInfo.non_null_countscCs|jdk}|jjd|dS)NrHTrKrH)r1r7sumr;rHr#r#r$rDs z DataFrameInfo.memory_usage_bytesrMrNrOrPrQrRrScCst||||d}||dS)N)inforOrQrS)_DataFrameInfoPrinter to_bufferr;rMrOrQrSprinterr#r#r$rVszDataFrameInfo.renderrU)r7rr1r2r rTrXrWr rrZrYr\) r]r^r_r`rgrbr@r<rprsrCrDrVr#r#r#r$rcs"       rcc@sleZdZdZ d!d"d d Zddddd d#ddZed$ddZed%ddZed&ddZ ed'dd Z dS)( SeriesInfoz- Class storing series-specific info. Nr7rr1r2r rTcCrdrUrerfr#r#r$rgrhzSeriesInfo.__init__)rMrOrQrSrMrNrOrPrQrRrScCs,|durtdt|||d}||dS)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rzrQrS) ValueError_SeriesInfoPrinterr|r}r#r#r$rVszSeriesInfo.renderrAcCs |jgSrUrur:r#r#r$rC$s zSeriesInfo.non_null_countsr8cCs |jjgSrUrmr:r#r#r$r<(rkzSeriesInfo.dtypesr>cCsddlm}t||jS)Nr)r)pandas.core.framerrjr7)r;rr#r#r$r@,s zSeriesInfo.dtype_countsrcCs|jdk}|jjd|dS)zMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. rHTrw)r1r7ryr#r#r$rD2s zSeriesInfo.memory_usage_bytesrU)r7rr1r2r rTr\rYrWrXrZ) r]r^r_r`rgrVrbrCr<r@rDr#r#r#r$rs"     rc@s*eZdZdZd d ddZedd d ZdS)_InfoPrinterAbstractz6 Class for printing dataframe or series info. NrMrNr rTcCs.|}|}|durtj}t||dS)z Save dataframe info into buffer.N)_create_table_builder get_linessysstdoutfmtbuffer_put_lines)r;rM table_builderlinesr#r#r$r|Ds z_InfoPrinterAbstract.to_buffer_TableBuilderAbstractcCr9)z!Create instance of table builder.Nr#r:r#r#r$rLr=z*_InfoPrinterAbstract._create_table_builderrU)rMrNr rT)r r)r]r^r_r`r|rrr#r#r#r$r?s  rc@sxeZdZdZ   dd d d Zed!ddZed"ddZed"ddZed!ddZ d#ddZ d$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. NrzrcrOrPrQrRrSr rTcCs0||_|j|_||_|||_|||_dSrU)rzr7rQ_initialize_max_colsrO_initialize_show_countsrS)r;rzrOrQrSr#r#r$rgas  z_DataFrameInfoPrinter.__init__rcCstdt|jdS)z"Maximum info rows to be displayed.zdisplay.max_info_rows)rrrr7r:r#r#r$max_rowsnrGz_DataFrameInfoPrinter.max_rowsboolcCst|j|jkS)zDCheck if number of columns to be summarized does not exceed maximum.)rrsrOr:r#r#r$exceeds_info_colssz'_DataFrameInfoPrinter.exceeds_info_colscCstt|j|jkS)zACheck if number of rows to be summarized does not exceed maximum.)rrrr7rr:r#r#r$exceeds_info_rowsxrGz'_DataFrameInfoPrinter.exceeds_info_rowscCrlrqrzrsr:r#r#r$rs}z_DataFrameInfoPrinter.col_countcCs|dur td|jdS|S)Nzdisplay.max_info_columnsr)rrs)r;rOr#r#r$rsz*_DataFrameInfoPrinter._initialize_max_colscCs |durt|j o |j S|SrU)rrrr;rSr#r#r$rsz-_DataFrameInfoPrinter._initialize_show_counts_DataFrameTableBuildercCsN|jr t|j|jdS|jdurt|jdS|jrt|jdSt|j|jdS)z[ Create instance of table builder based on verbosity and display settings. rz with_countsFrz)rQ_DataFrameTableBuilderVerboserzrS _DataFrameTableBuilderNonVerboserr:r#r#r$rs   z+_DataFrameInfoPrinter._create_table_builder)NNN) rzrcrOrPrQrRrSrRr rTrZr r)rOrPr rrSrRr r)r r) r]r^r_r`rgrbrrrrsrrrr#r#r#r$r{Qs"       r{c@s4eZdZdZ  ddd d Zdd dZdddZdS)raClass for printing series info. Parameters ---------- info : SeriesInfo Instance of SeriesInfo. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. NrzrrQrRrSr rTcCs$||_|j|_||_|||_dSrU)rzr7rQrrS)r;rzrQrSr#r#r$rgsz_SeriesInfoPrinter.__init___SeriesTableBuildercCs,|js|jdurt|j|jdSt|jdS)zF Create instance of table builder based on verbosity. Nrr)rQ_SeriesTableBuilderVerboserzrS_SeriesTableBuilderNonVerboser:r#r#r$rs  z(_SeriesInfoPrinter._create_table_builderrcCs|durdS|S)NTr#rr#r#r$rsz*_SeriesInfoPrinter._initialize_show_counts)NN)rzrrQrRrSrRr rT)r rr)r]r^r_r`rgrrr#r#r#r$rs  rc@seZdZUdZded<ded<ed#ddZed$d d Zed%d dZ ed&ddZ ed'ddZ ed(ddZ ed)ddZ d*ddZd*ddZd*d d!Zd"S)+rz* Abstract builder for info table. list[str]_linesr5rzr cCr9)z-Product in a form of list of lines (strings).Nr#r:r#r#r$rr=z_TableBuilderAbstract.get_linesr6cCrlrUrzr7r:r#r#r$r7z_TableBuilderAbstract.datar8cCrl)z*Dtypes of each of the DataFrame's columns.)rzr<r:r#r#r$r<rz_TableBuilderAbstract.dtypesr>cCrl)r?)rzr@r:r#r#r$r@rz"_TableBuilderAbstract.dtype_countsrcCs t|jjS)z Whether to display memory usage.)rrzr1r:r#r#r$display_memory_usages z*_TableBuilderAbstract.display_memory_usager!cCrl)z/Memory usage string with proper size qualifier.)rzrFr:r#r#r$rFrz)_TableBuilderAbstract.memory_usage_stringrAcCrlrU)rzrCr:r#r#r$rCrz%_TableBuilderAbstract.non_null_countsrTcCs|jtt|jdS)z>Add line with string representation of dataframe to the table.N)rappendr!typer7r:r#r#r$add_object_type_linesz*_TableBuilderAbstract.add_object_type_linecCs|j|jjdS)z,Add line with range of indices to the table.N)rrr7rK_summaryr:r#r#r$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$ sz9_TableBuilderAbstract.add_dtypes_line..zdtypes: z, N)sortedr@itemsrrjoin)r;collected_dtypesr#r#r$add_dtypes_lines z%_TableBuilderAbstract.add_dtypes_lineNr r)r r6rWrXrr[rYr rT)r]r^r_r`rarrrbr7r<r@rrFrCrrrr#r#r#r$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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Parameters ---------- info : SeriesInfo. Instance of SeriesInfo. rzrr rTcCrrUrrr#r#r$rgrz_SeriesTableBuilder.__init__rcCsg|_||jSrU)rrr:r#r#r$rsz_SeriesTableBuilder.get_linesrcCrl)zSeries.rr:r#r#r$r7rz_SeriesTableBuilder.datacCrrrr:r#r#r$rrz)_SeriesTableBuilder.add_memory_usage_linecCr9zcCs|jddS)zK Create mapping between datatypes and their number of occurrences. cSs|jSrUr)r/r#r#r$Msz-_get_dataframe_dtype_counts..)r< value_countsgroupbyrx)rr#r#r$rjHsrj)rrrrr r!)r&r'r(r!r r!rU)r1r2r r3)rrr r>)8 __future__rabcrrrtextwraprtypingrpandas._configrpandas.io.formatsr rpandas.io.formats.printingr collections.abcr r r rpandas._typingrrpandasrrrframe_max_cols_subrframe_examples_subframe_see_also_subframe_sub_kwargsseries_examples_subseries_see_also_subseries_sub_kwargsINFO_DOCSTRINGr%r0r4r5rcrrr{rrrrrrrrrrjr#r#r#r$s        V  >  3   SI<P+83]B 2