Ë
|
nñúhm ã ó d dl mZ d dlmZmZ d dlZd dlmZ d dlm Z d dl
|
m Z d dl m Z d dlmZ e r d d lmZmZmZmZ d d
|
lmZmZ d d lmZmZm Z ed « Z ed « Z ed« Z ed« Z dde e e e ddZ! ed« Z" ed« Z#ddde e"e#ddZ$ ed« Z%d9dZ&d:dZ' d; d<dZ( G d d e« Z) G d d e)« Z* G d d!e)« Z+ G d" d#« Z, G d$ d%e,« Z- G d& d'e,« Z. G d( d)e« Z/ G d* d+e/« Z0 G d, d-e0« Z1 G d. d/e/« Z2 G d0 d1e0e2« Z3 G d2 d3e/« Z4 G d4 d5e4« Z5 G d6 d7e4e2« Z6d=d8Z7y)>é )Ú annotations)ÚABCÚabstractmethodN)Údedent)Ú TYPE_CHECKING©Ú
|
get_option)Úformat)Ú pprint_thing)ÚIterableÚIteratorÚMappingÚSequence)ÚDtypeÚ WriteBuffer)Ú DataFrameÚIndexÚSeriesa 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)
|
<class 'pandas.core.frame.DataFrame'>
|
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)
|
<class 'pandas.core.frame.DataFrame'>
|
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()
|
<class 'pandas.core.frame.DataFrame'>
|
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')
|
<class 'pandas.core.frame.DataFrame'>
|
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.r z and columnsÚ )ÚklassÚtype_subÚ max_cols_subÚshow_counts_subÚ examples_subÚ see_also_subÚversion_added_subaî >>> int_values = [1, 2, 3, 4, 5]
|
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
|
>>> s = pd.Series(text_values, index=int_values)
|
>>> s.info()
|
<class 'pandas.core.series.Series'>
|
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)
|
<class 'pandas.core.series.Series'>
|
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()
|
<class 'pandas.core.series.Series'>
|
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')
|
<class 'pandas.core.series.Series'>
|
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.r z
|
.. versionadded:: 1.4.0
|
aÅ
|
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 <df-memory-usage>` 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}
|
c ó< t | « d| j |« 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)ÚstrÚljust)ÚsÚspaces úIH:\Change_password\venv_build\Lib\site-packages\pandas/io/formats/info.pyÚ_put_strr# % s ô. q6&5>× Ñ Ó &Ð&ó c óL dD ] }| dk r | d| d| c S | dz } | 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'
|
)ÚbytesÚKBÚMBÚGBÚTBg @z3.1fú z PB© )ÚnumÚsize_qualifierÚxs r" Ú _sizeof_fmtr0 ? sQ ð0 /ò Ø <Ø$Z Ð /¨q°°Ð4Ò 4Ø v ðð $ZÐ' sÐ +Ð+r$ c ó | t d« } | S )z5Get memory usage based on inputs and display options.zdisplay.memory_usager )Ú memory_usages r" Ú_initialize_memory_usager3 ^ s ð ÐÜ!Ð"8Ó9 Ø Ðr$ c óà e Zd ZU dZded< ded< eedd« « Zeedd« « Zeedd« « Z eedd « « Z
|
edd
|
« Z edd « Z e dd « Z y )Ú _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.
|
úDataFrame | SeriesÚdataú
|
bool | strr2 c ó y)z¡
|
Dtypes.
|
|
Returns
|
-------
|
dtypes : sequence
|
Dtype of each of the DataFrame's columns (or one series column).
|
Nr, ©Úselfs r" Údtypesz_BaseInfo.dtypesx ó r$ c ó y)ú!Mapping dtype - number of counts.Nr, r: s r" Ú dtype_countsz_BaseInfo.dtype_counts r= r$ c ó y)úBSequence of non-null counts for all columns or column (if series).Nr, r: s r" Únon_null_countsz_BaseInfo.non_null_counts r= r$ c ó y)z
|
Memory usage in bytes.
|
|
Returns
|
-------
|
memory_usage_bytes : int
|
Object's total memory usage in bytes.
|
Nr, r: s r" Úmemory_usage_bytesz _BaseInfo.memory_usage_bytes r= r$ c óH t | j | j « dS )z0Memory usage in a form of human readable string.ú
|
)r0 rE r. r: s r" Úmemory_usage_stringz _BaseInfo.memory_usage_string s% ô d× 5Ñ 5°t×7JÑ7JÓKÐLÈBÐOÐOr$ c ó¨ d}| j rC| j dk7 r4d| j v s$| j j j « rd}|S )Nr ÚdeepÚobjectú+)r2 r@ r7 ÚindexÚ_is_memory_usage_qualified)r; r. s r" r. z_BaseInfo.size_qualifier sM àØ × Ò Ø× Ñ FÒ*ð
|
× 1Ñ 1Ñ1Øyy×AÑAÔCà%(NØ Ð r$ c ó y ©Nr, )r; ÚbufÚmax_colsÚverboseÚ show_countss r" Úrenderz_BaseInfo.render® s ð r$ N©ÚreturnzIterable[Dtype]©rW úMapping[str, int]©rW ú Sequence[int]©rW Úint©rW r ©
|
rQ úWriteBuffer[str] | NonerR ú
|
int | NonerS ú bool | NonerT rb rW ÚNone)Ú__name__Ú
|
__module__Ú __qualname__Ú__doc__Ú__annotations__Úpropertyr r< r@ rC rE rH r. rU r, r$ r" r5 r5 g sõ
ñ ð Ó Ø Ó à Øò ó ó ð ð Øò0ó ó ð0ð ØòQó ó ðQð Øò ó ó ð ð òPó ðPð ò ó ð ð ð ð %ð ð ð ð
|
ð ð !ð ð
|
ò ó ñ r$ r5 c ó® e Zd ZdZ d d dZed d« Zedd« Zedd« Zedd« Z edd« Z
|
edd « Z dd
|
Z y)Ú DataFrameInfoz0
|
Class storing dataframe-specific info.
|
Nc ó2 || _ t |« | _ y rP ©r7 r3 r2 ©r; r7 r2 s r" Ú__init__zDataFrameInfo.__init__¿ s ð
|
$ Ü 4°\Ó BÕr$ c ó, t | j « S rP )Ú_get_dataframe_dtype_countsr7 r: s r" r@ zDataFrameInfo.dtype_countsÇ s ä*¨4¯9©9Ó5Ð5r$ c ó. | j j S )z
|
Dtypes.
|
|
Returns
|
-------
|
dtypes
|
Dtype of each of the DataFrame's columns.
|
©r7 r< r: s r" r< zDataFrameInfo.dtypesË s ð yy× Ñ Ð r$ c ó. | j j S )zz
|
Column names.
|
|
Returns
|
-------
|
ids : Index
|
DataFrame's column names.
|
)r7 Úcolumnsr: s r" ÚidszDataFrameInfo.ids× s ð yy× Ñ Ð r$ c ó, t | j « S ©z#Number of columns to be summarized.)Úlenrv r: s r" Ú col_countzDataFrameInfo.col_countã s ô 488}Ð r$ c ó6 | j j « S )rB ©r7 Úcountr: s r" rC z DataFrameInfo.non_null_countsè s ð yyÓ Ð r$ c óv | j dk( }| j j d|¬« j « S )NrJ T©rM rJ )r2 r7 Úsum©r; rJ s r" rE z DataFrameInfo.memory_usage_bytesí s5 à× Ñ FÑ*Øyy×%Ñ%¨D°tÐ%Ó<×@Ñ@ÓBÐBr$ c óD t | |||¬« }|j |« y )N)ÚinforR rS rT )Ú_DataFrameInfoPrinterÚ to_buffer©r; rQ rR rS rT Úprinters r" rU zDataFrameInfo.renderò s* ô (ØØ ØØ#ô
|
ð ×Ñ#Õ r$ rP )r7 r r2 úbool | str | NonerW rc rX rV ©rW r r\ rZ r_ ) rd re rf rg ro ri r@ r< rv rz rC rE rU r, r$ r" rk rk º sâ ñð +/ðCàðCð (ðCð
|
ó Cð ò6ó ð6ð ò ó ð ð ò !ó ð !ð ò ó ð ð ò!ó ð!ð òCó ðCð ð %ð ð ð ð
|
ð ð !ð ð
|
ô r$ rk c ó e Zd ZdZ d
|
d dZddddd d dZed d« Zedd« Zedd« Z edd « Z
|
y)Ú
|
SeriesInfoz-
|
Class storing series-specific info.
|
Nc ó2 || _ t |« | _ y rP rm rn s r" ro zSeriesInfo.__init__ s ð
|
! Ü 4°\Ó BÕr$ )rQ rR rS rT c ó\ | t d« t | ||¬« }|j |« y )NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)r rS rT )Ú
|
ValueErrorÚ_SeriesInfoPrinterr
r s r" rU zSeriesInfo.render sA ð Ð Ü ð5óð ô %ØØØ#ô
|
ð
|
×Ñ#Õ r$ c ó8 | j j « gS rP r| r: s r" rC zSeriesInfo.non_null_counts$ s à Ó!Ð"Ð"r$ c ó0 | j j gS rP rs r: s r" r< zSeriesInfo.dtypes( s à × Ñ Ð!Ð!r$ c óD ddl m} t || j « « S )Nr )r )Úpandas.core.framer rq r7 )r; r s r" r@ zSeriesInfo.dtype_counts, s å/ä*©9°T·Y±YÓ+?Ó@Ð@r$ c óZ | j dk( }| j j d|¬« S )zMemory usage in bytes.
|
|
Returns
|
-------
|
memory_usage_bytes : int
|
Object's total memory usage in bytes.
|
rJ Tr )r2 r7 r s r" rE z SeriesInfo.memory_usage_bytes2 s. ð × Ñ FÑ*Øyy×%Ñ%¨D°tÐ%Ó<Ð<r$ rP )r7 r r2 r rW rc r_ rZ rV rX r\ ) rd re rf rg ro rU ri rC r< r@ rE r, r$ r" r r sÈ ñð +/ðCàðCð (ðCð
|
ó Cð (,Ø #Ø #Ø#'ñ ð %ð ð ð ð
|
ð ð !ð ð
|
ó ð( ò#ó ð#ð ò"ó ð"ð òAó ðAð
|
ò =ó ñ =r$ r c ó, e Zd ZdZdddZedd« Zy)Ú_InfoPrinterAbstractz6
|
Class for printing dataframe or series info.
|
Nc ó | j « }|j « }|t j }t j
|
||« y)z Save dataframe info into buffer.N)Ú_create_table_builderÚ get_linesÚsysÚstdoutÚfmtÚbuffer_put_lines)r; rQ Ú table_builderÚliness r" r
z _InfoPrinterAbstract.to_bufferD s< à ×2Ñ2Ó4 Ø ×'Ñ'Ó)Ø ;Ü**CÜ × Ñ S %Õ(r$ c ó y)z!Create instance of table builder.Nr, r: s r" r z*_InfoPrinterAbstract._create_table_builderL r= r$ rP )rQ r` rW rc )rW Ú_TableBuilderAbstract)rd re rf rg r
r r r, r$ r" r r ? s ñô)ð ò0ó ñ0r$ r c ó e Zd ZdZ d d dZed d« Zedd« Zedd« Zed d« Z ddZ
|
dd Z dd
|
Z y)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.
|
Nc ó || _ |j | _ || _ | j |« | _ | j |« | _ y rP )r r7 rS Ú_initialize_max_colsrR Ú_initialize_show_countsrT )r; r rR rS rT s r" ro z _DataFrameInfoPrinter.__init__a sB ð ØII Ø Ø ×1Ñ1°(Ó; Ø ×7Ñ7¸ ÓDÕr$ c óF t dt | j « dz « S )z"Maximum info rows to be displayed.zdisplay.max_info_rowsé )r ry r7 r: s r" Úmax_rowsz _DataFrameInfoPrinter.max_rowsn s ô Ð1´3°t·y±y³>ÀAÑ3EÓFÐFr$ c óF t | j | j kD « S )zDCheck if number of columns to be summarized does not exceed maximum.)Úboolrz rR r: s r" Úexceeds_info_colsz'_DataFrameInfoPrinter.exceeds_info_colss s ô DNN T§]¡]Ñ2Ó3Ð3r$ c óX t t | j « | j kD « S )zACheck if number of rows to be summarized does not exceed maximum.)rª ry r7 r¨ r: s r" Úexceeds_info_rowsz'_DataFrameInfoPrinter.exceeds_info_rowsx s ô C N T§]¡]Ñ2Ó3Ð3r$ c ó. | j j S rx ©r rz r: s r" rz z _DataFrameInfoPrinter.col_count} ó ð yy×"Ñ"Ð"r$ c ó< |t d| j dz « S |S )Nzdisplay.max_info_columnsr§ )r rz )r; rR s r" r¤ z*_DataFrameInfoPrinter._initialize_max_cols s$ Ø Ð Ü Ð 8¸$¿.¹.È1Ñ:LÓMÐ MØr$ c óT |%t | j xr | j « S |S rP )rª r« r ©r; rT s r" r¥ z-_DataFrameInfoPrinter._initialize_show_counts s0 Ø Ð ÜD× 2Ñ 2Ð2ÒQ¸4×;QÑ;QÐ7QÓRÐ Rà Ð r$ c ó* | j r!t | j | j ¬« S | j du rt | j ¬« S | j
|
rt | j ¬« S t | j | j ¬« S )z[
|
Create instance of table builder based on verbosity and display settings.
|
©r Ú with_countsF©r )rS Ú _DataFrameTableBuilderVerboser rT Ú _DataFrameTableBuilderNonVerboser« r: s r" r z+_DataFrameInfoPrinter._create_table_builder sz ð <<Ü0ØYYØ × ,Ñ ,ôð ð \\UÑ "Ü3¸¿¹ÔCÐ CØ × #Ò #Ü3¸¿¹ÔCÐ Cä0ØYYØ × ,Ñ ,ôð r$ )NNN)
|
r rk rR ra rS rb rT rb rW rc r\ ©rW rª )rR ra rW r] ©rT rb rW rª )rW Ú_DataFrameTableBuilder) rd re rf rg ro ri r¨ r« r rz r¤ r¥ r r, r$ r" r r Q s® ñ ð$ $Ø #Ø#'ð Eàð Eð ð Eð ð Eð
|
!ð Eð
|
ó Eð òGó ðGð ò4ó ð4ð ò4ó ð4ð ò#ó ð#óó
|
ô r$ r c ó< e Zd ZdZ d ddZddZd dZy)
|
r a Class 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.
|
Nc ón || _ |j | _ || _ | j |« | _ y rP )r r7 rS r¥ rT )r; r rS rT s r" ro z_SeriesInfoPrinter.__init__® s0 ð ØII Ø Ø ×7Ñ7¸ ÓDÕr$ c ó | j s | j !t | j | j ¬« S t | j ¬« S )zF
|
Create instance of table builder based on verbosity.
|
rµ r· )rS Ú_SeriesTableBuilderVerboser rT Ú _SeriesTableBuilderNonVerboser: s r" r z(_SeriesInfoPrinter._create_table_builder¹ sB ð <<4<<Ð/Ü-ØYYØ × ,Ñ ,ôð ô
|
1°d·i±iÔ@Ð @r$ c ó |y|S )NTr, r³ s r" r¥ z*_SeriesInfoPrinter._initialize_show_countsÅ s Ø Ð Øà Ð r$ )NN)r r rS rb rT rb rW rc )rW Ú_SeriesTableBuilderr» )rd re rf rg ro r r¥ r, r$ r" r r ¡ sJ ñ
|
ð $Ø#'ð Eàð Eð ð Eð !ð Eð
|
|
ó Eó
|
Aô r$ r c ó¼ e Zd ZU dZded< ded< edd« Zedd« Zedd« Z edd « Z
|
edd
|
« Z edd « Z edd « Z dd ZddZddZy)r¡ z*
|
Abstract builder for info table.
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ú list[str]Ú_linesr5 r c ó y)z-Product in a form of list of lines (strings).Nr, r: s r" r z _TableBuilderAbstract.get_linesÔ r= r$ c ó. | j j S rP ©r r7 r: s r" r7 z_TableBuilderAbstract.dataØ s àyy~~Ð r$ c ó. | j j S )z*Dtypes of each of the DataFrame's columns.)r r< r: s r" r< z _TableBuilderAbstract.dtypesÜ s ð yy× Ñ Ð r$ c ó. | j j S )r? )r r@ r: s r" r@ z"_TableBuilderAbstract.dtype_countsá s ð yy×%Ñ%Ð%r$ c ó@ t | j j « S )z Whether to display memory usage.)rª r r2 r: s r" Údisplay_memory_usagez*_TableBuilderAbstract.display_memory_usageæ s ô DII×*Ñ*Ó+Ð+r$ c ó. | j j S )z/Memory usage string with proper size qualifier.)r rH r: s r" rH z)_TableBuilderAbstract.memory_usage_stringë s ð yy×,Ñ,Ð,r$ c ó. | j j S rP )r rC r: s r" rC z%_TableBuilderAbstract.non_null_countsð s àyy×(Ñ(Ð(r$ c ór | j j t t | j « « « y)z>Add line with string representation of dataframe to the table.N)rÆ Úappendr Útyper7 r: s r" Úadd_object_type_linez*_TableBuilderAbstract.add_object_type_lineô s! à ×Ñ3t D§I¡IÓ/Õ0r$ c ó~ | j j | j j j « « y)z,Add line with range of indices to the table.N)rÆ rÑ r7 rM Ú_summaryr: s r" Úadd_index_range_linez*_TableBuilderAbstract.add_index_range_lineø s% à ×Ñ499??×3Ñ3Ó5Õ6r$ c óâ t | j j « « D cg c] \ }}| d|dd }}}| j j ddj |« « yc c}}w )z2Add summary line with dtypes present in dataframe.ú(Údú)zdtypes: z, N)Úsortedr@ ÚitemsrÆ rÑ Újoin)r; ÚkeyÚvalÚcollected_dtypess r" Úadd_dtypes_linez%_TableBuilderAbstract.add_dtypes_lineü sq ô /5°T×5FÑ5F×5LÑ5LÓ5NÓ.O÷
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Abstract builder for dataframe info table.
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Parameters
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----------
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info : DataFrameInfo.
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Instance of DataFrameInfo.
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c ó || _ y rP r· ©r; r s r" ro z _DataFrameTableBuilder.__init__ s Ø#' r$ c ó g | _ | j dk( r | j « | j S | j « | j S )Nr )rÆ rz Ú_fill_empty_infoÚ_fill_non_empty_infor: s r" r z _DataFrameTableBuilder.get_lines sE Ø Ø >>QÒ Ø × !Ñ !Ô #ð {{Ðð × %Ñ %Ô 'Ø{{Ðr$ c ó¼ | j « | j « | j j dt | j
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« j d« y)z;Add lines to the info table, pertaining to empty dataframe.zEmpty rG N)rÓ rÖ rÆ rÑ rÒ r7 rd r: s r" rè z'_DataFrameTableBuilder._fill_empty_info sD à ×!Ñ!Ô#Ø ×!Ñ!Ô#Ø ×ÑV¤D¨¯©£O×$<Ñ$<Ð#=¸RÐ@ÕAr$ c ó y©z?Add lines to the info table, pertaining to non-empty dataframe.Nr, r: s r" ré z+_DataFrameTableBuilder._fill_non_empty_info r= r$ c ó. | j j S )z
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DataFrame.rÉ r: s r" r7 z_DataFrameTableBuilder.data# ó ð yy~~Ð r$ c ó. | j j S )zDataframe columns.)r rv r: s r" rv z_DataFrameTableBuilder.ids( s ð yy}}Ð r$ c ó. | j j S )z-Number of dataframe columns to be summarized.r¯ r: s r" rz z _DataFrameTableBuilder.col_count- r° r$ c óT | j j d| j « y©z!Add line containing memory usage.zmemory usage: N©rÆ rÑ rH r: s r" Úadd_memory_usage_linez,_DataFrameTableBuilder.add_memory_usage_line2 ó" à ×Ñ^¨D×,DÑ,DÐ+EÐFÕGr$ N)r rk rW rc râ rã )rW r r r\ )rd re rf rg ro r rè r ré ri r7 rv rz rô r, r$ r" r¼ r¼ so ñó(óóBð òNó ðNð ò ó ð ð ò ó ð ð ò#ó ð#ôHr$ r¼ c ó e Zd ZdZddZddZy)r¹ z>
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Dataframe info table builder for non-verbose output.
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c ó¾ | j « | j « | j « | j « | j r| j « yyrì )rÓ rÖ Úadd_columns_summary_linerá rÍ rô r: s r" ré z5_DataFrameTableBuilderNonVerbose._fill_non_empty_info< sL à ×!Ñ!Ô#Ø ×!Ñ!Ô#Ø ×%Ñ%Ô'Ø × Ñ Ô Ø × $Ò $Ø × &Ñ &Õ (ð %r$ c ón | j j | j j d¬« « y )NÚColumns©Úname)rÆ rÑ rv rÕ r: s r" rø z9_DataFrameTableBuilderNonVerbose.add_columns_summary_lineE s& Ø ×Ñ488×,Ñ,°)Ð,Ó<Õ=r$ Nrã )rd re rf rg ré rø r, r$ r" r¹ r¹ 7 s ñó)ô>r$ r¹ c óÐ e Zd ZU dZdZded< ded< ded< d ed
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ddZ ddZ edd« Z edd« ZddZddZddZd dZd dZy) Ú_TableBuilderVerboseMixinz(
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Mixin for verbose info output.
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z r ÚSPACINGzSequence[Sequence[str]]Ústrrowsr[ Úgross_column_widthsrª r¶ c ó y)ú.Headers names of the columns in verbose table.Nr, r: s r" Úheadersz!_TableBuilderVerboseMixin.headersS r= r$ c óR | j D cg c] }t |« c}S c c}w )z'Widths of header columns (only titles).)r ry ©r; Úcols r" Úheader_column_widthsz._TableBuilderVerboseMixin.header_column_widthsX s ð %)§L¡LÖ1SCÒ1Ð1ùÒ1s $c ó | j « }t | j |« D cg c]
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}t | c}S c c}w )zAGet widths of columns containing both headers and actual content.)Ú_get_body_column_widthsÚzipr Úmax)r; Úbody_column_widthsÚwidthss r" Ú_get_gross_column_widthsz2_TableBuilderVerboseMixin._get_gross_column_widths] sE à !× 9Ñ 9Ó ;Ðô d× 7Ñ 7Ð9KÓLö
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s ©;c ó t t | j « }|D cg c] }t d |D « « c}S c c}w )z$Get widths of table content columns.c 3 ó2 K | ] }t |« y wrP )ry )Ú.0r/ s r" ú <genexpr>zD_TableBuilderVerboseMixin._get_body_column_widths.<locals>.<genexpr>h s è ø Ò(qCFÑ(ùs )Úlistr r r )r; Ústrcolsr s r" r
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z1_TableBuilderVerboseMixin._get_body_column_widthse s4 ä+/´°T·\±\Ð0BÓ+CØ4;Ö<¨SÑ( CÔ(Õ(Ò<Ð<ùÒ<s <c óZ | j r| j « S | j « S )z
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Generator function yielding rows content.
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Each element represents a row comprising a sequence of strings.
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)r¶ Ú_gen_rows_with_countsÚ_gen_rows_without_countsr: s r" Ú _gen_rowsz#_TableBuilderVerboseMixin._gen_rowsj s+ ð × Ò Ø×-Ñ-Ó/Ð /à×0Ñ0Ó2Ð 2r$ c ó y©z=Iterator with string representation of body data with counts.Nr, r: s r" r z/_TableBuilderVerboseMixin._gen_rows_with_countsu r= r$ c ó y©z@Iterator with string representation of body data without counts.Nr, r: s r" r z2_TableBuilderVerboseMixin._gen_rows_without_countsy r= r$ c
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óò | j j t | j | j « D cg c] \ }}t ||« c}}« }| j j |« y c c}}w rP )rÿ rÝ r r r r# rÆ rÑ )r; ÚheaderÚ col_widthÚ header_lines r" Úadd_header_linez)_TableBuilderVerboseMixin.add_header_line} sc Øll×'Ñ'ô *-¨T¯\©\¸4×;SÑ;SÓ)T÷ á%FIô Õ+ó ó
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óø | j j t | j | j « D cg c] \ }}t d|z |« c}}« }| j j |« y c c}}w )Nú-)rÿ rÝ r r r r# rÆ rÑ )r; Úheader_colwidthÚgross_colwidthÚseparator_lines r" Úadd_separator_linez,_TableBuilderVerboseMixin.add_separator_line sm Ø ×*Ñ*ô 8;Ø×-Ñ-¨t×/GÑ/Gó8÷ á3O ^ô Ñ.°Õ?ó ó
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c ó | j D ]i }| j j t || j « D cg c] \ }}t ||« c}}« }| j j |« k y c c}}w rP )r rÿ rÝ r r r# rÆ rÑ )r; Úrowr r& Ú body_lines r" Úadd_body_linesz(_TableBuilderVerboseMixin.add_body_lines sr Ø<<ò *CØ ×)Ñ)ô 03°3¸×8PÑ8PÓ/Q÷á+^ô S .Õ1óóIð KK× Ñ yÕ )ñ *ùós ¾A: c # ó< K | j D ] }| d yw)z7Iterator with string representation of non-null counts.z non-nullN)rC )r; r} s r" Ú_gen_non_null_countsz._TableBuilderVerboseMixin._gen_non_null_counts s( è ø à×)Ñ)ò &EØG9Ð%Ó %ñ &ùs c # óH K | j D ] }t |« yw)z5Iterator with string representation of column dtypes.N)r< r )r; Údtypes r" Ú _gen_dtypesz%_TableBuilderVerboseMixin._gen_dtypes s$ è ø à[[ò &EÜ uÓ%Ó %ñ &ùó "N©rW z Sequence[str]rZ ©rW zIterator[Sequence[str]]rã ©rW z Iterator[str])rd re rf rg rÿ rh ri r r r r r
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Dataframe info table builder for verbose output.
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c ó || _ || _ t | j « « | _ | j « | _ y rP ©r r¶ r r r r r ©r; r r¶ s r" ro z&_DataFrameTableBuilderVerbose.__init__« ó7 ð Ø&ÔÜ04°T·^±^Ó5EÓ0F Ø26×2OÑ2OÓ2QÕ r$ c ó | j « | j « | j « | j « | j « | j « | j « | j r| j « yyrì ) rÓ rÖ rø r" r( r, rá rÍ rô r: s r" ré z2_DataFrameTableBuilderVerbose._fill_non_empty_info¶ sp à ×!Ñ!Ô#Ø ×!Ñ!Ô#Ø ×%Ñ%Ô'Ø × Ñ Ô Ø × Ñ Ô!Ø ×ÑÔ Ø × Ñ Ô Ø × $Ò $Ø × &Ñ &Õ (ð %r$ c ó* | j rg d¢S g d¢S )r )ú # ÚColumnúNon-Null Countr )r= r> r ©r¶ r: s r" r z%_DataFrameTableBuilderVerbose.headers s ð × Ò Ú?Ð ?Ú)Ð)r$ c óV | j j d| j d« y )NzData columns (total z
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columns):)rÆ rÑ rz r: s r" rø z6_DataFrameTableBuilderVerbose.add_columns_summary_lineÉ s# Ø ×ÑÐ 1°$·.±.Ð1AÀÐLÕMr$ c # ó K t | j « | j « | j « « E d{ y7 wr )r Ú_gen_line_numbersÚ _gen_columnsr1 r: s r" r z6_DataFrameTableBuilderVerbose._gen_rows_without_countsÌ s< è ø äØ × "Ñ "Ó $Ø × Ñ Ó Ø × Ñ Ó ó
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ús ;A½A¾Ac # ó¬ K t | j « | j « | j « | j « « E d{ y7 wr )r rC rD r. r1 r: s r" r z3_DataFrameTableBuilderVerbose._gen_rows_with_countsÔ sH è ø äØ × "Ñ "Ó $Ø × Ñ Ó Ø × %Ñ %Ó 'Ø × Ñ Ó ó
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AÁ AÁ Ac # óT K t | j « D ] \ }}d| yw)z6Iterator with string representation of column numbers.r+ N)Ú enumeraterv )r; ÚiÚ_s r" rC z/_DataFrameTableBuilderVerbose._gen_line_numbersÝ s- è ø ä dhhÓ'ò DAqØaS'Mñ ùs &(c # óH K | j D ] }t |« yw)z4Iterator with string representation of column names.N)rv r r s r" rD z*_DataFrameTableBuilderVerbose._gen_columnsâ s$ è ø à88ò $CÜ sÓ#Ó #ñ $ùr2 N)r rk r¶ rª rW rc rã r3 r4 r5 ) rd re rf rg ro ré ri r rø r r rC rD r, r$ r" r¸ r¸ ¦ sa ñð Rð ð Rð ð Rð
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Abstract builder for series info table.
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Parameters
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----------
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info : SeriesInfo.
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Instance of SeriesInfo.
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c ó || _ y rP r· ræ s r" ro z _SeriesTableBuilder.__init__ò s Ø $ r$ c óH g | _ | j « | j S rP )rÆ ré r: s r" r z _SeriesTableBuilder.get_linesõ s Ø Ø ×!Ñ!Ô#Ø{{Ðr$ c ó. | j j S )zSeries.rÉ r: s r" r7 z_SeriesTableBuilder.dataú rî r$ c óT | j j d| j « yrò ró r: s r" rô z)_SeriesTableBuilder.add_memory_usage_lineÿ rõ r$ c ó y©z<Add lines to the info table, pertaining to non-empty series.Nr, r: s r" ré z(_SeriesTableBuilder._fill_non_empty_info r= r$ N)r r rW rc râ )rW r rã ) rd re rf rg ro r ri r7 rô r ré r, r$ r" rà rà è sA ñó%óð
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ò ó ð óHð òKó ñKr$ rà c ó e Zd ZdZddZy)rÁ z;
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Series info table builder for non-verbose output.
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c ó | j « | j « | j « | j r| j « yyrQ )rÓ rÖ rá rÍ rô r: s r" ré z2_SeriesTableBuilderNonVerbose._fill_non_empty_info s@ à ×!Ñ!Ô#Ø ×!Ñ!Ô#Ø × Ñ Ô Ø × $Ò $Ø × &Ñ &Õ (ð %r$ Nrã )rd re rf rg ré r, r$ r" rÁ rÁ s ñô)r$ rÁ c óV e Zd ZdZ d dZd
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Series info table builder for verbose output.
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c ó || _ || _ t | j « « | _ | j « | _ y rP r8 r9 s r" ro z#_SeriesTableBuilderVerbose.__init__ r: r$ c ó | j « | j « | j « | j « | j « | j « | j « | j r| j « yyrQ ) rÓ rÖ Úadd_series_name_liner" r( r, rá rÍ rô r: s r" ré z/_SeriesTableBuilderVerbose._fill_non_empty_info& sp à ×!Ñ!Ô#Ø ×!Ñ!Ô#Ø ×!Ñ!Ô#Ø × Ñ Ô Ø × Ñ Ô!Ø ×ÑÔ Ø × Ñ Ô Ø × $Ò $Ø × &Ñ &Õ (ð %r$ c óh | j j d| j j « y )Nz Series name: )rÆ rÑ r7 rü r: s r" rW z/_SeriesTableBuilderVerbose.add_series_name_line2 s$ Ø ×Ñ]¨4¯9©9¯>©>Ð*:Ð;Õ<r$ c ó( | j rddgS dgS )r r? r r@ r: s r" r z"_SeriesTableBuilderVerbose.headers5 s ð × Ò Ø$ gÐ.Ð .ØyÐr$ c # ó@ K | j « E d{ y7 wr )r1 r: s r" r z3_SeriesTableBuilderVerbose._gen_rows_without_counts< s è ø à×#Ñ#Ó%×%Ò%ús c # óp K t | j « | j « « E d{ y7 wr )r r. r1 r: s r" r z0_SeriesTableBuilderVerbose._gen_rows_with_counts@ s0 è ø äØ × %Ñ %Ó 'Ø × Ñ Ó ó
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Create mapping between datatypes and their number of occurrences.
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