U sVc @s0ddlmZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZmZddlmZdd lmZe rdd lmZmZmZed Zed Zed ZedZedZ ddeeeee ddZ!edZ"edZ#dddede"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_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)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 ---------- 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} 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)rrr:/tmp/pip-unpacked-wheel-xj8nt62q/pandas/io/formats/info.py_put_str-sr 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' )bytesZKBZMBZGBZTBg@z3.1f z PBr)r"r#xrrr _sizeof_fmtGs  r'bool | str | None bool | str) memory_usagercCs|dkrtd}|S)z5Get memory usage based on inputs and display options.Nzdisplay.memory_usager )r*rrr_initialize_memory_usagefsr+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.Nrr1rrr dtype_countsszBaseInfo.dtype_counts Sequence[int]cCsdS)BSequence of non-null counts for all columns or column (if series).Nrr1rrrnon_null_countsszBaseInfo.non_null_countsrcCsdS)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nrr1rrrmemory_usage_bytesszBaseInfo.memory_usage_bytesrcCst|j|jdS)z0Memory usage in a form of human readable string. )r'r:r#r1rrrmemory_usage_stringszBaseInfo.memory_usage_stringcCs2d}|jr.|jdkr.d|jks*|jjr.d}|S)Nrdeepobject+)r*r6r.indexZ_is_memory_usage_qualified)r2r#rrrr#s  zBaseInfo.size_qualifierWriteBuffer[str] | None int | None bool | NoneNonebufmax_colsverbose show_countsrcCsdSNr)r2rFrGrHrIrrrrenders zBaseInfo.renderN)__name__ __module__ __qualname____doc____annotations__propertyrr3r6r9r:r<r#rKrrrrr,os*    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(rDr.r*rcCs||_t||_dSrJr.r+r*r2r.r*rrr__init__szDataFrameInfo.__init__r4r0cCs t|jSrJ)_get_dataframe_dtype_countsr.r1rrrr6szDataFrameInfo.dtype_countsr/cCs|jjS)z Dtypes. Returns ------- dtypes Dtype of each of the DataFrame's columns. r.r3r1rrrr3s zDataFrameInfo.dtypesrcCs|jjS)zz Column names. Returns ------- ids : Index DataFrame's column names. )r.columnsr1rrridss zDataFrameInfo.idsrcCs t|jSz#Number of columns to be summarized.)lenrZr1rrr col_countszDataFrameInfo.col_countr7cCs |jS)r8r.countr1rrrr9szDataFrameInfo.non_null_countscCs(|jdkrd}nd}|jjd|dS)Nr=TFr@r=)r*r.sumr2r=rrrr:s z DataFrameInfo.memory_usage_bytesrArBrCrEcCst||||d}||dS)N)inforGrHrI)DataFrameInfoPrinter to_bufferr2rFrGrHrIprinterrrrrKszDataFrameInfo.render)N) rLrMrNrOrVrQr6r3rZr]r9r:rKrrrrrRs   rRc@seZdZdZdddddddZddddd d d d d dd ddZeddddZeddddZeddddZ eddddZ dS) SeriesInfoz- Class storing series-specific info. Nrr(rDrScCs||_t||_dSrJrTrUrrrrVszSeriesInfo.__init__)rFrGrHrIrArBrCrEcCs,|dk rtdt|||d}||dS)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rcrHrI) ValueErrorSeriesInfoPrinterrerfrrrrKszSeriesInfo.renderr7r0cCs |jgSrJr^r1rrrr9/szSeriesInfo.non_null_countsr/cCs |jjgSrJrXr1rrrr33szSeriesInfo.dtypesr4cCsddlm}t||jS)Nr)r)Zpandas.core.framerrWr.)r2rrrrr67s zSeriesInfo.dtype_countsrcCs$|jdkrd}nd}|jjd|dS)zMemory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. r=TFr`)r*r.rbrrrr:=s zSeriesInfo.memory_usage_bytes)N) rLrMrNrOrVrKrQr9r3r6r:rrrrrhs  rhc@s4eZdZdZd dddddZedd d d ZdS) InfoPrinterAbstractz6 Class for printing dataframe or series info. NrArD)rFrcCs.|}|}|dkrtj}t||dS)z Save dataframe info into buffer.N)_create_table_builder get_linessysstdoutfmtZbuffer_put_lines)r2rFZ table_builderlinesrrrreRs zInfoPrinterAbstract.to_bufferTableBuilderAbstractr0cCsdS)z!Create instance of table builder.Nrr1rrrrlZsz)InfoPrinterAbstract._create_table_builder)N)rLrMrNrOrerrlrrrrrkMsrkc@seZdZdZddddddddd Zed d d d Zedd ddZedd ddZed d ddZ dd dddZ dddddZ dd ddZ dS)rda{ Class for printing dataframe info. Parameters ---------- info : DataFrameInfo Instance of DataFrameInfo. max_cols : int, optional When to switch from the verbose to the truncated output. verbose : bool, optional Whether to print the full summary. show_counts : bool, optional Whether to show the non-null counts. NrRrBrCrD)rcrGrHrIrcCs0||_|j|_||_|||_|||_dSrJ)rcr.rH_initialize_max_colsrG_initialize_show_countsrI)r2rcrGrHrIrrrrVos  zDataFrameInfoPrinter.__init__rr0cCstdt|jdS)z"Maximum info rows to be displayed.zdisplay.max_info_rows)r r\r.r1rrrmax_rows|szDataFrameInfoPrinter.max_rowsboolcCst|j|jkS)zDCheck if number of columns to be summarized does not exceed maximum.)rwr]rGr1rrrexceeds_info_colssz&DataFrameInfoPrinter.exceeds_info_colscCstt|j|jkS)zACheck if number of rows to be summarized does not exceed maximum.)rwr\r.rvr1rrrexceeds_info_rowssz&DataFrameInfoPrinter.exceeds_info_rowscCs|jjSr[rcr]r1rrrr]szDataFrameInfoPrinter.col_count)rGrcCs|dkrtd|jdS|S)Nzdisplay.max_info_columnsru)r r])r2rGrrrrssz)DataFrameInfoPrinter._initialize_max_colsrIrcCs$|dkrt|j o|j S|SdSrJ)rwrxryr2rIrrrrtsz,DataFrameInfoPrinter._initialize_show_countsDataFrameTableBuildercCsR|jrt|j|jdS|jdkr,t|jdS|jr>t|jdSt|j|jdSdS)z[ Create instance of table builder based on verbosity and display settings. rc with_countsFrcN)rHDataFrameTableBuilderVerbosercrIDataFrameTableBuilderNonVerboserxr1rrrrls   z*DataFrameInfoPrinter._create_table_builder)NNN) rLrMrNrOrVrQrvrxryr]rsrtrlrrrrrd_s  rdc@sDeZdZdZddddddddZd d d d Zdd dddZdS)rjaClass for printing series info. 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Nr~r)rHSeriesTableBuilderVerbosercrISeriesTableBuilderNonVerboser1rrrrls z'SeriesInfoPrinter._create_table_builderrwr{cCs|dkr dS|SdS)NTrr|rrrrtsz)SeriesInfoPrinter._initialize_show_counts)NN)rLrMrNrOrVrlrtrrrrrjs   rjc@seZdZUdZded<ded<eddddZed dd d Zed dd dZ eddddZ eddddZ eddddZ eddddZ ddddZddddZddd d!Zd"S)#rrz* Abstract builder for info table. list[str]_linesr,rcr0cCsdS)z-Product in a form of list of lines (strings).Nrr1rrrrmszTableBuilderAbstract.get_linesr-cCs|jjSrJrcr.r1rrrr.szTableBuilderAbstract.datar/cCs|jjS)z*Dtypes of each of the DataFrame's columns.)rcr3r1rrrr3szTableBuilderAbstract.dtypesr4cCs|jjS)r5)rcr6r1rrrr6sz!TableBuilderAbstract.dtype_countsrwcCs t|jjS)z Whether to display memory usage.)rwrcr*r1rrrdisplay_memory_usagesz)TableBuilderAbstract.display_memory_usagercCs|jjS)z/Memory usage string with proper size qualifier.)rcr<r1rrrr<sz(TableBuilderAbstract.memory_usage_stringr7cCs|jjSrJ)rcr9r1rrrr9sz$TableBuilderAbstract.non_null_countsrDcCs|jtt|jdS)z>Add line with string representation of dataframe to the table.N)rappendrtyper.r1rrradd_object_type_linesz)TableBuilderAbstract.add_object_type_linecCs|j|jjdS)z,Add line with range of indices to the table.N)rrr.r@_summaryr1rrradd_index_range_linesz)TableBuilderAbstract.add_index_range_linecCs4ddt|jD}|jdd|dS)z2Add summary line with dtypes present in dataframe.cSs"g|]\}}|d|ddqS)(d)r).0keyvalrrr sz8TableBuilderAbstract.add_dtypes_line..zdtypes: z, N)sortedr6itemsrrjoin)r2Zcollected_dtypesrrradd_dtypes_line s z$TableBuilderAbstract.add_dtypes_lineN)rLrMrNrOrPrrmrQr.r3r6rr<r9rrrrrrrrrs( rrc@seZdZdZdddddZddd d Zddd d Zeddd dZe ddddZ e ddddZ e ddddZ ddddZ dS)r}z Abstract builder for dataframe info table. 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