a oEbB @s8ddlmZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZmZddlmZdd lmZdd lmZe rdd lmZmZed Zed ZedZedZ edZ!ddeeee e!ddZ"edZ#edZ$dddede#e$ddZ%edZ&ddddddZ'd ddd!d"d#Z(dId$d%d&d'd(Z)Gd)d*d*eZ*Gd+d,d,e*Z+Gd-d.d.e*Z,Gd/d0d0Z-Gd1d2d2e-Z.Gd3d4d4e-Z/Gd5d6d6eZ0Gd7d8d8e0Z1Gd9d:d:e1Z2Gd;d<dd>e1e3Z4Gd?d@d@e0Z5GdAdBdBe5Z6GdCdDdDe5e3Z7ddEdFdGdHZ8dS)J) annotations)ABCabstractmethodN)dedent) TYPE_CHECKINGIterableIteratorMappingSequence get_option)Dtype WriteBuffer)Index)format) pprint_thing) DataFrameSeriesa 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)klassZtype_subZ max_cols_subshow_counts_subnull_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() 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 ---------- data : {klass} {klass} to print information about. 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. {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} 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)rrrV/home/ja/django-apps/lartica_env/lib/python3.9/site-packages/pandas/io/formats/info.py_put_str.sr z int | float)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' )bytesKBMBGBTBg@z3.1f z PBr)r!r"xrrr _sizeof_fmtHs  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_usagegsr.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). NrselfrrrdtypesszBaseInfo.dtypesMapping[str, int]cCsdS)!Mapping dtype - number of counts.Nrr4rrr dtype_countsszBaseInfo.dtype_counts Sequence[int]cCsdS)BSequence of non-null counts for all columns or column (if series).Nrr4rrrnon_null_countsszBaseInfo.non_null_countsrcCsdS)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nrr4rrrmemory_usage_bytesszBaseInfo.memory_usage_bytesrcCst|j|jdS)z0Memory usage in a form of human readable string. )r*r=r"r4rrrmemory_usage_stringszBaseInfo.memory_usage_stringcCs2d}|jr.|jdkr.d|jvs*|jjr.d}|S)Nrdeepobject+)r-r9r1indexZ_is_memory_usage_qualified)r5r"rrrr"s  zBaseInfo.size_qualifierWriteBuffer[str] | None int | None bool | NoneNonebufmax_colsverbose show_countsrcCsdSNr)r5rIrJrKrLrrrrenders zBaseInfo.renderN)__name__ __module__ __qualname____doc____annotations__propertyrr6r9r<r=r?r"rNrrrrr/ps*    r/c@seZdZdZd!dddddZedd d d Zed d d dZedd ddZedd ddZ edd ddZ edd ddZ dddddddd Z dS)" DataFrameInfoz0 Class storing dataframe-specific info. Nrr+r1r-cCs||_t||_dSrMr1r.r-r5r1r-rrr__init__szDataFrameInfo.__init__r7r3cCs t|jSrM)_get_dataframe_dtype_countsr1r4rrrr9szDataFrameInfo.dtype_countsr2cCs|jjS)z Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r1r6r4rrrr6s zDataFrameInfo.dtypesrcCs|jjS)zz Column names. Returns ------- ids : Index DataFrame's column names. )r1columnsr4rrridss zDataFrameInfo.idsrcCs t|jSz#Number of columns to be summarized.)lenr]r4rrr col_countszDataFrameInfo.col_countr:cCs |jS)r;r1countr4rrrr<szDataFrameInfo.non_null_countscCs(|jdkrd}nd}|jjd|dS)Nr@TFrCr@)r-r1sumr5r@rrrr=s z DataFrameInfo.memory_usage_bytesrDrErFrGrHcCst||||d}||dS)NinforJrKrL)DataFrameInfoPrinter to_bufferr5rIrJrKrLprinterrrrrNszDataFrameInfo.render)N) rOrPrQrRrYrTr9r6r]r`r<r=rNrrrrrUs   rUc@seZdZdZddddddZdddddd d d d d d ddZeddddZeddddZeddZ eddddZ dS) SeriesInfoz- Class storing series-specific info. Nrr+rVcCs||_t||_dSrMrWrXrrrrYszSeriesInfo.__init__)rIrJrKrLrDrErFrGrHcCs,|durtdt|||d}||dS)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.inforgrKrL) ValueErrorSeriesInfoPrinterrirjrrrrNszSeriesInfo.renderr:r3cCs |jgSrMrar4rrrr<0szSeriesInfo.non_null_countsr2cCs |jjgSrMr[r4rrrr64szSeriesInfo.dtypescCsddlm}t||jS)Nr)r)pandas.core.framerrZr1)r5rrrrr98s zSeriesInfo.dtype_countsrcCs$|jdkrd}nd}|jjd|dS)zMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. r@TFrc)r-r1rerrrr=>s zSeriesInfo.memory_usage_bytes)N) rOrPrQrRrYrNrTr<r6r9r=rrrrrls   rlc@s4eZdZdZd dddddZedd d d ZdS) InfoPrinterAbstractz6 Class for printing dataframe or series info. NrDrG)rIrcCs.|}|}|durtj}t||dS)z Save dataframe info into buffer.N)_create_table_builder get_linessysstdoutfmtZbuffer_put_lines)r5rIZ table_builderlinesrrrriSs zInfoPrinterAbstract.to_bufferTableBuilderAbstractr3cCsdS)z!Create instance of table builder.Nrr4rrrrr[sz)InfoPrinterAbstract._create_table_builder)N)rOrPrQrRrirrrrrrrrqNsrqc@seZdZdZddddddddZed d d d Zed d ddZed d ddZed d ddZ dd dddZ dd dddZ dd ddZ dS)rha{ 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. NrUrErFrfcCs0||_|j|_||_|||_|||_dSrM)rgr1rK_initialize_max_colsrJ_initialize_show_countsrL)r5rgrJrKrLrrrrYps  zDataFrameInfoPrinter.__init__rr3cCstdt|jdS)z"Maximum info rows to be displayed.zdisplay.max_info_rows)r r_r1r4rrrmax_rows}szDataFrameInfoPrinter.max_rowsboolcCst|j|jkS)zDCheck if number of columns to be summarized does not exceed maximum.)r}r`rJr4rrrexceeds_info_colssz&DataFrameInfoPrinter.exceeds_info_colscCstt|j|jkS)zACheck if number of rows to be summarized does not exceed maximum.)r}r_r1r|r4rrrexceeds_info_rowssz&DataFrameInfoPrinter.exceeds_info_rowscCs|jjSr^rgr`r4rrrr`szDataFrameInfoPrinter.col_count)rJrcCs|durtd|jdS|S)Nzdisplay.max_info_columnsr{)r r`)r5rJrrrrysz)DataFrameInfoPrinter._initialize_max_colsrLrcCs$|durt|j o|j S|SdSrM)r}r~rr5rLrrrrzsz,DataFrameInfoPrinter._initialize_show_countsDataFrameTableBuildercCsR|jrt|j|jdS|jdur,t|jdS|jr>t|jdSt|j|jdSdS)z[ Create instance of table builder based on verbosity and display settings. rg with_countsFrgN)rKDataFrameTableBuilderVerbosergrLDataFrameTableBuilderNonVerboser~r4rrrrrs   z*DataFrameInfoPrinter._create_table_builder)NNN) rOrPrQrRrYrTr|r~rr`ryrzrrrrrrrh`s  rhc@sBeZdZdZdddddddZdd d d Zdd d ddZdS)roaClass for printing series info. 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Nrr)rKSeriesTableBuilderVerbosergrLSeriesTableBuilderNonVerboser4rrrrrs z'SeriesInfoPrinter._create_table_builderr}rcCs|dur dS|SdS)NTrrrrrrzsz)SeriesInfoPrinter._initialize_show_counts)NN)rOrPrQrRrYrrrzrrrrros   roc@seZdZUdZded<ded<eddddZed dd d Zed dd dZ eddddZ eddddZ eddddZ eddddZ ddddZddddZddd d!Zd"S)#rxz* Abstract builder for info table. list[str]_linesr/rgr3cCsdS)z-Product in a form of list of lines (strings).Nrr4rrrrsszTableBuilderAbstract.get_linesr0cCs|jjSrMrgr1r4rrrr1szTableBuilderAbstract.datar2cCs|jjS)z*Dtypes of each of the DataFrame's columns.)rgr6r4rrrr6szTableBuilderAbstract.dtypesr7cCs|jjS)r8)rgr9r4rrrr9sz!TableBuilderAbstract.dtype_countsr}cCs t|jjS)z Whether to display memory usage.)r}rgr-r4rrrdisplay_memory_usagesz)TableBuilderAbstract.display_memory_usagercCs|jjS)z/Memory usage string with proper size qualifier.)rgr?r4rrrr?sz(TableBuilderAbstract.memory_usage_stringr:cCs|jjSrM)rgr<r4rrrr<sz$TableBuilderAbstract.non_null_countsrGcCs|jtt|jdS)z>Add line with string representation of dataframe to the table.N)rappendrtyper1r4rrradd_object_type_linesz)TableBuilderAbstract.add_object_type_linecCs|j|jjdS)z,Add line with range of indices to the table.N)rrr1rC_summaryr4rrradd_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 sz8TableBuilderAbstract.add_dtypes_line..zdtypes: z, N)sortedr9itemsrrjoin)r5Zcollected_dtypesrrradd_dtypes_line s z$TableBuilderAbstract.add_dtypes_lineN)rOrPrQrRrSrrsrTr1r6r9rr?r<rrrrrrrrxs( rxc@seZdZdZddddZdddd Zd dd d Zed dd dZe ddddZ e ddddZ e ddddZ d dddZ dS)rz Abstract builder for dataframe info table. 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