U md0@s$ddlmZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZddlmZmZddlmZdd lmZe rdd lmZmZmZed Zed Zed ZedZddeeeeddZ edZ!edZ"dddee!e"ddZ#edZ$ddddddZ%ddddd d!Z&dGd"d#d$d%d&Z'Gd'd(d(eZ(Gd)d*d*e(Z)Gd+d,d,e(Z*Gd-d.d.Z+Gd/d0d0e+Z,Gd1d2d2e+Z-Gd3d4d4eZ.Gd5d6d6e.Z/Gd7d8d8e/Z0Gd9d:d:e.Z1Gd;d<dd>e.Z3Gd?d@d@e3Z4GdAdBdBe3e1Z5ddCdDdEdFZ6dS)H) 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.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/home/sam/Atlas/atlas_env/lib/python3.8/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|dkrtd}|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). NrselfrrrdtypeswszBaseInfo.dtypesMapping[str, int]cCsdS)!Mapping dtype - number of counts.Nrr3rrr dtype_countsszBaseInfo.dtype_counts Sequence[int]cCsdS)BSequence of non-null counts for all columns or column (if series).Nrr3rrrnon_null_countsszBaseInfo.non_null_countsrcCsdS)z Memory usage in bytes. Returns ------- memory_usage_bytes : int Object's total memory usage in bytes. Nrr3rrrmemory_usage_bytesszBaseInfo.memory_usage_bytesrcCst|j|jdS)z0Memory usage in a form of human readable string. )r)r<r"r3rrrmemory_usage_stringszBaseInfo.memory_usage_stringcCs2d}|jr.|jdkr.d|jks*|jjr.d}|S)Nrdeepobject+)r,r8r0indexZ_is_memory_usage_qualified)r4r"rrrr"s  zBaseInfo.size_qualifierWriteBuffer[str] | None int | None bool | NoneNonebufmax_colsverbose show_countsrcCsdSNr)r4rHrIrJrKrrrrenders zBaseInfo.renderN)__name__ __module__ __qualname____doc____annotations__propertyrr5r8r;r<r>r"rMrrrrr.fs*    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,|dk rtdt|||d}||dS)NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rerJrK) ValueErrorSeriesInfoPrinterrgrhrrrrMszSeriesInfo.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<1s zSeriesInfo.memory_usage_bytes)N) rNrOrPrQrXrMrSr;r5r8r<rrrrrjs  rjc@s4eZdZdZd dddddZedd d d ZdS) InfoPrinterAbstractz6 Class for printing dataframe or series info. NrCrF)rHrcCs.|}|}|dkrtj}t||dS)z Save dataframe info into buffer.N)_create_table_builder get_linessysstdoutfmtZbuffer_put_lines)r4rHZ table_builderlinesrrrrgCs zInfoPrinterAbstract.to_bufferTableBuilderAbstractr2cCsdS)z!Create instance of table builder.Nrr3rrrrnKsz)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. 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