U
|
¬ý°d} ã @ s$ d dl mZ d dlmZmZ d dlZd dlmZ d dlm Z m
|
Z
|
m Z m Z m Z d dlmZ d dlmZmZ d dlmZ d d lmZ e rd d
|
lmZmZmZ ed Z ed Z ed Z edZ dde e e e ddZ edZ!edZ"ddde e!e"ddZ#edZ$ddddd d Z%d ddd d d!Z&dGd"d#d$d%d&Z'G d'd( d(eZ(G d)d* d*e(Z)G d+d, d,e(Z*G d-d. d.Z+G d/d0 d0e+Z,G d1d2 d2e+Z-G d3d4 d4eZ.G d5d6 d6e.Z/G d7d8 d8e/Z0G d9d: d:e.Z1G d;d< d<e/e1Z2G d=d> d>e.Z3G d?d@ d@e3Z4G dAdB dBe3e1Z5ddCdDdEdFZ6dS )Hé )Ú annotations)ÚABCÚabstractmethodN)Údedent)Ú TYPE_CHECKINGÚIterableÚIteratorÚMappingÚSequence©Ú
|
get_option)ÚDtypeÚ WriteBuffer)Úformat)Ú pprint_thing)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 )ÚklassZtype_subZ max_cols_subÚshow_counts_subZ examples_subZ see_also_subZversion_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}
|
z str | DtypeÚintÚstr)ÚsÚspaceÚreturnc C s t | 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 Úljust)r r © r úMd:\z\workplace\vscode\pyvenv\venv\Lib\site-packages\pandas/io/formats/info.pyÚ_put_str$ s r Úfloat)ÚnumÚsize_qualifierr c C sB dD ],}| dk r(| 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'
|
)ÚbytesZKBZMBZGBZTBg @z3.1fú z PBr )r! r" Úxr r r Ú _sizeof_fmt> s
|
|
r& úbool | str | Noneú
|
bool | str)Ú memory_usager c C s | dkrt d} | S )z5Get memory usage based on inputs and display options.Nzdisplay.memory_usager )r) r r r Ú_initialize_memory_usage] s r* c @ s¸ e Zd ZU dZded< ded< eedddd Zeed
|
dd d Zeed dddZ eeddddZ
|
eddddZ eddddZ edddddd d d Z d S ) ÚBaseInfoaj
|
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 | SeriesÚdatar( r) úIterable[Dtype]©r c C s dS )z¡
|
Dtypes.
|
|
Returns
|
-------
|
dtypes : sequence
|
Dtype of each of the DataFrame's columns (or one series column).
|
Nr ©Úselfr r r Údtypesw s zBaseInfo.dtypesúMapping[str, int]c C s dS )ú!Mapping dtype - number of counts.Nr r0 r r r Ú dtype_counts s zBaseInfo.dtype_countsú Sequence[int]c C s dS )úBSequence of non-null counts for all columns or column (if series).Nr r0 r r r Únon_null_counts s zBaseInfo.non_null_countsr c C s dS )z
|
Memory usage in bytes.
|
|
Returns
|
-------
|
memory_usage_bytes : int
|
Object's total memory usage in bytes.
|
Nr r0 r r r Úmemory_usage_bytes s zBaseInfo.memory_usage_bytesr c C s t | j| j dS )z0Memory usage in a form of human readable string.Ú
|
)r& r9 r" r0 r r r Úmemory_usage_string s z BaseInfo.memory_usage_stringc C s2 d}| j r.| j dkr.d| jks*| jj ¡ r.d}|S )Nr ÚdeepÚobjectú+)r) r5 r- ÚindexZ_is_memory_usage_qualified)r1 r" r r r r" s
|
ÿ
|
þzBaseInfo.size_qualifierúWriteBuffer[str] | Noneú
|
int | Noneú bool | NoneÚNone©ÚbufÚmax_colsÚverboseÚ show_countsr c C s d S ©Nr )r1 rE rF rG rH r r r Úrender s zBaseInfo.renderN)Ú__name__Ú
|
__module__Ú __qualname__Ú__doc__Ú__annotations__Úpropertyr r2 r5 r8 r9 r; r" rJ r r r r r+ f s*
|
|
|
r+ c @ s¦ e Zd ZdZd!ddddddZed d
|
d d Zed d
|
ddZedd
|
ddZedd
|
ddZ edd
|
ddZ
|
edd
|
ddZ dd d d dd d d Z dS )"Ú DataFrameInfoz0
|
Class storing dataframe-specific info.
|
Nr r' rC ©r- r) r c C s || _ t|| _d S rI ©r- r* r) ©r1 r- r) r r r Ú__init__¾ s zDataFrameInfo.__init__r3 r/ c C s
|
t | jS rI )Ú_get_dataframe_dtype_countsr- r0 r r r r5 Æ s zDataFrameInfo.dtype_countsr. c C s | j jS )z
|
Dtypes.
|
|
Returns
|
-------
|
dtypes
|
Dtype of each of the DataFrame's columns.
|
©r- r2 r0 r r r r2 Ê s
|
zDataFrameInfo.dtypesr c C s | j jS )zz
|
Column names.
|
|
Returns
|
-------
|
ids : Index
|
DataFrame's column names.
|
)r- Úcolumnsr0 r r r ÚidsÖ s
|
zDataFrameInfo.idsr c C s
|
t | jS ©z#Number of columns to be summarized.)ÚlenrY r0 r r r Ú col_countâ s zDataFrameInfo.col_countr6 c C s
|
| j ¡ S )r7 ©r- Úcountr0 r r r r8 ç s z DataFrameInfo.non_null_countsc C s | j dk}| jj d|d ¡ S )Nr< T©r? r< )r) r- Úsum©r1 r< r r r r9 ì s
|
z DataFrameInfo.memory_usage_bytesr@ rA rB rD c C s t | |||d}| |¡ d S )N)ÚinforF rG rH )ÚDataFrameInfoPrinterÚ to_buffer©r1 rE rF rG rH Úprinterr r r rJ ñ s üzDataFrameInfo.render)N) rK rL rM rN rU rP r5 r2 rY r\ r8 r9 rJ r r r r rQ ¹ s ý rQ c @ s e Zd ZdZd ddddddZddddd d
|
d d d dd ddZeddddZeddddZeddddZ edddd Z
|
dS ) Ú
|
SeriesInfoz-
|
Class storing series-specific info.
|
Nr r' rC rR c C s || _ t|| _d S rI rS rT r r r rU s zSeriesInfo.__init__)rE rF rG rH r@ rA rB rD c C s, |d k rt dt| ||d}| |¡ d S )NzIArgument `max_cols` can only be passed in DataFrame.info, not Series.info)rb rG rH )Ú
|
ValueErrorÚSeriesInfoPrinterrd re r r r rJ s ÿýzSeriesInfo.renderr6 r/ c C s | j ¡ gS rI r] r0 r r r r8 # s zSeriesInfo.non_null_countsr. c C s
|
| j jgS rI rW r0 r r r r2 ' s zSeriesInfo.dtypesr3 c C s ddl m} t|| jS )Nr )r )Zpandas.core.framer rV r- )r1 r r r r r5 + s zSeriesInfo.dtype_countsr c C s | j dk}| jj d|dS )zMemory usage in bytes.
|
|
Returns
|
-------
|
memory_usage_bytes : int
|
Object's total memory usage in bytes.
|
r< Tr_ )r) r- ra r r r r9 1 s
|
z SeriesInfo.memory_usage_bytes)N) rK rL rM rN rU rJ rP r8 r2 r5 r9 r r r r rg s ý úrg c @ s4 e Zd ZdZd dddddZedd d
|
d ZdS ) ÚInfoPrinterAbstractz6
|
Class for printing dataframe or series info.
|
Nr@ rC )rE r c C s. | ¡ }| ¡ }|dkr tj}t ||¡ dS )z Save dataframe info into buffer.N)Ú_create_table_builderÚ get_linesÚsysÚstdoutÚfmtZbuffer_put_lines)r1 rE Z table_builderÚlinesr r r rd C s
|
z InfoPrinterAbstract.to_bufferÚTableBuilderAbstractr/ c C s dS )z!Create instance of table builder.Nr r0 r r r rk K s z)InfoPrinterAbstract._create_table_builder)N)rK rL rM rN rd r rk r r r r rj > s rj c @ s e Zd ZdZd dddddddd Zed
|
d d d Zedd ddZedd ddZed
|
d ddZ dd
|
dddZ
|
dddddZ dd d d Z dS ) rc 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.
|
NrQ rA rB rC )rb rF rG rH r c C s0 || _ |j| _|| _| |¡| _| |¡| _d S rI )rb r- rG Ú_initialize_max_colsrF Ú_initialize_show_countsrH )r1 rb rF rG rH r r r rU ` s
|
z DataFrameInfoPrinter.__init__r r/ c C s t dt| jd S )z"Maximum info rows to be displayed.zdisplay.max_info_rowsé )r r[ r- r0 r r r Úmax_rowsm s z DataFrameInfoPrinter.max_rowsÚboolc C s t | j| jkS )zDCheck if number of columns to be summarized does not exceed maximum.)rv r\ rF r0 r r r Úexceeds_info_colsr s z&DataFrameInfoPrinter.exceeds_info_colsc C s t t| j| jkS )zACheck if number of rows to be summarized does not exceed maximum.)rv r[ r- ru r0 r r r Úexceeds_info_rowsw s z&DataFrameInfoPrinter.exceeds_info_rowsc C s | j jS rZ ©rb r\ r0 r r r r\ | s z DataFrameInfoPrinter.col_count)rF r c C s |d krt d| jd S |S )Nzdisplay.max_info_columnsrt )r r\ )r1 rF r r r rr s z)DataFrameInfoPrinter._initialize_max_cols©rH r c C s$ |d kr t | j o| j S |S d S rI )rv rw rx ©r1 rH r r r rs s z,DataFrameInfoPrinter._initialize_show_countsÚDataFrameTableBuilderc C sR | j rt| j| jdS | j dkr,t| jdS | jr>t| jdS t| j| jdS dS )z[
|
Create instance of table builder based on verbosity and display settings.
|
©rb Ú with_countsF©rb N)rG Ú DataFrameTableBuilderVerboserb rH Ú DataFrameTableBuilderNonVerboserw r0 r r r rk s þ
|
þz*DataFrameInfoPrinter._create_table_builder)NNN) rK rL rM rN rU rP ru rw rx r\ rr rs rk r r r r rc P s û rc c @ sD e Zd ZdZddddddddZd d
|
d d Zdd dddZdS )ri 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.
|
Nrg rB rC )rb rG rH r c C s$ || _ |j| _|| _| |¡| _d S rI )rb r- rG rs rH )r1 rb rG rH r r r rU ® s zSeriesInfoPrinter.__init__ÚSeriesTableBuilderr/ c C s0 | j s| j dkr t| j| jdS t| jdS dS )zF
|
Create instance of table builder based on verbosity.
|
Nr} r )rG ÚSeriesTableBuilderVerboserb rH Ú SeriesTableBuilderNonVerboser0 r r r rk ¹ s þz'SeriesInfoPrinter._create_table_builderrv rz c C s |d kr dS |S d S )NTr r{ r r r rs Å s z)SeriesInfoPrinter._initialize_show_counts)NN)rK rL rM rN rU rk rs r r r r ri ¡ s ü ri c @ sÊ e Zd ZU dZded< ded< eddddZed dd
|
d Zed dd dZ eddddZ
|
eddddZ eddddZ eddddZ ddd d Zddd d Zddd d!Zd"S )#rq z*
|
Abstract builder for info table.
|
ú list[str]Ú_linesr+ rb r/ c C s dS )z-Product in a form of list of lines (strings).Nr r0 r r r rl Ô s z TableBuilderAbstract.get_linesr, c C s | j jS rI ©rb r- r0 r r r r- Ø s zTableBuilderAbstract.datar. c C s | j jS )z*Dtypes of each of the DataFrame's columns.)rb r2 r0 r r r r2 Ü s zTableBuilderAbstract.dtypesr3 c C s | j jS )r4 )rb r5 r0 r r r r5 á s z!TableBuilderAbstract.dtype_countsrv c C s t | jjS )z Whether to display memory usage.)rv rb r) r0 r r r Údisplay_memory_usageæ s z)TableBuilderAbstract.display_memory_usager c C s | j jS )z/Memory usage string with proper size qualifier.)rb r; r0 r r r r; ë s z(TableBuilderAbstract.memory_usage_stringr6 c C s | j jS rI )rb r8 r0 r r r r8 ð s z$TableBuilderAbstract.non_null_countsrC c C s | j tt| j¡ dS )z>Add line with string representation of dataframe to the table.N)r Úappendr Útyper- r0 r r r Úadd_object_type_lineô s z)TableBuilderAbstract.add_object_type_linec C s | j | jj ¡ ¡ dS )z,Add line with range of indices to the table.N)r r r- r? Ú_summaryr0 r r r Úadd_index_range_lineø s z)TableBuilderAbstract.add_index_range_linec C s4 dd t | j ¡ D }| j dd |¡ ¡ dS )z2Add summary line with dtypes present in dataframe.c S s" g | ]\}}| d |ddqS )ú(Údú)r )Ú.0ÚkeyÚvalr r r Ú
|
<listcomp>þ s z8TableBuilderAbstract.add_dtypes_line.<locals>.<listcomp>zdtypes: z, N)Úsortedr5 Úitemsr r Újoin)r1 Zcollected_dtypesr r r Úadd_dtypes_lineü s ÿz$TableBuilderAbstract.add_dtypes_lineN)rK rL rM rN rO r rl rP r- r2 r5 r r; r8 r r r r r r r rq Ì s(
|
rq c @ s e Zd ZdZdddddZddd d
|
Zddd d Zeddd dZe ddddZ
|
e ddddZ e ddddZ ddddZ dS )r| z
|
Abstract builder for dataframe info table.
|
|
Parameters
|
----------
|
info : DataFrameInfo.
|
Instance of DataFrameInfo.
|
rQ rC ©rb r c C s
|
|| _ d S rI r ©r1 rb r r r rU s z DataFrameTableBuilder.__init__r
r/ c C s( g | _ | jdkr| ¡ n| ¡ | j S )Nr )r r\ Ú_fill_empty_infoÚ_fill_non_empty_infor0 r r r rl s
|
|
|
z DataFrameTableBuilder.get_linesc C s0 | ¡ | ¡ | j dt| jj d¡ dS )z;Add lines to the info table, pertaining to empty dataframe.zEmpty r: N)r r r r r r- rK r0 r r r r s z&DataFrameTableBuilder._fill_empty_infoc C s dS ©z?Add lines to the info table, pertaining to non-empty dataframe.Nr r0 r r r r s z*DataFrameTableBuilder._fill_non_empty_infor c C s | j jS )z
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DataFrame.r r0 r r r r- # s zDataFrameTableBuilder.datar c C s | j jS )zDataframe columns.)rb rY r0 r r r rY ( s zDataFrameTableBuilder.idsr c C s | j jS )z-Number of dataframe columns to be summarized.ry r0 r r r r\ - s z DataFrameTableBuilder.col_countc C s | j d| j ¡ dS ©z!Add line containing memory usage.zmemory usage: N©r r r; r0 r r r Úadd_memory_usage_line2 s z+DataFrameTableBuilder.add_memory_usage_lineN)rK rL rM rN rU rl r r r rP r- rY r\ r r r r r r| s r| c @ s, e Zd ZdZddddZddddZdS ) r z>
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Dataframe info table builder for non-verbose output.
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rC r/ c C s2 | ¡ | ¡ | ¡ | ¡ | jr.| ¡ dS r )r r Úadd_columns_summary_liner r r r0 r r r r < s z4DataFrameTableBuilderNonVerbose._fill_non_empty_infoc C s | j | jjdd¡ d S )NZColumns©Úname)r r rY r r0 r r r r¡ E s z8DataFrameTableBuilderNonVerbose.add_columns_summary_lineN)rK rL rM rN r r¡ r r r r r 7 s r c @ sò e Zd ZU dZdZded< ded< ded< d ed
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< eed d d dZedd ddZ dd ddZ
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dd ddZ dd ddZ edd ddZ edd ddZd d d d Zd d d d Zd d d!d"Zd#d d$d%Zd#d d&d'Zd(S ))ÚTableBuilderVerboseMixinz(
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Mixin for verbose info output.
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z r ÚSPACINGzSequence[Sequence[str]]Ústrrowsr6 Úgross_column_widthsrv r~ ú Sequence[str]r/ c C s dS )ú.Headers names of the columns in verbose table.Nr r0 r r r ÚheadersS s z TableBuilderVerboseMixin.headersc C s dd | j D S )z'Widths of header columns (only titles).c S s g | ] }t |qS r ©r[ ©r Úcolr r r r [ s zATableBuilderVerboseMixin.header_column_widths.<locals>.<listcomp>)rª r0 r r r Úheader_column_widthsX s z-TableBuilderVerboseMixin.header_column_widthsc C s | ¡ }dd t| j|D S )zAGet widths of columns containing both headers and actual content.c S s g | ] }t | qS r ©Úmax)r Zwidthsr r r r ` s ÿzETableBuilderVerboseMixin._get_gross_column_widths.<locals>.<listcomp>)Ú_get_body_column_widthsÚzipr® )r1 Zbody_column_widthsr r r Ú_get_gross_column_widths] s
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þz1TableBuilderVerboseMixin._get_gross_column_widthsc C s t t| j }dd |D S )z$Get widths of table content columns.c S s g | ]}t d d |D qS )c s s | ]}t |V qd S rI r« )r r% r r r Ú <genexpr>h s zNTableBuilderVerboseMixin._get_body_column_widths.<locals>.<listcomp>.<genexpr>r¯ r¬ r r r r h s zDTableBuilderVerboseMixin._get_body_column_widths.<locals>.<listcomp>)Úlistr² r¦ )r1 Zstrcolsr r r r± e s z0TableBuilderVerboseMixin._get_body_column_widthsúIterator[Sequence[str]]c C s | j r| ¡ S | ¡ S dS )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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N)r~ Ú_gen_rows_with_countsÚ_gen_rows_without_countsr0 r r r Ú _gen_rowsj s z"TableBuilderVerboseMixin._gen_rowsc C s dS ©z=Iterator with string representation of body data with counts.Nr r0 r r r r· u s z.TableBuilderVerboseMixin._gen_rows_with_countsc C s dS ©z@Iterator with string representation of body data without counts.Nr r0 r r r r¸ y s z1TableBuilderVerboseMixin._gen_rows_without_countsrC c C s0 | j dd t| j| jD ¡}| j |¡ d S )Nc S s g | ]\}}t ||qS r ©r )r ÚheaderZ col_widthr r r r s ÿz<TableBuilderVerboseMixin.add_header_line.<locals>.<listcomp>)r¥ r r² rª r§ r r )r1 Z header_liner r r Úadd_header_line} s þÿz(TableBuilderVerboseMixin.add_header_linec C s0 | j dd t| j| jD ¡}| j |¡ d S )Nc S s g | ]\}}t d | |qS )ú-r¼ )r Zheader_colwidthÚgross_colwidthr r r r s ÿz?TableBuilderVerboseMixin.add_separator_line.<locals>.<listcomp>)r¥ r r² r® r§ r r )r1 Zseparator_liner r r Úadd_separator_line s ÿþÿz+TableBuilderVerboseMixin.add_separator_linec C s: | j D ].}| j dd t|| jD ¡}| j |¡ qd S )Nc S s g | ]\}}t ||qS r r¼ )r r rÀ r r r r s ÿz;TableBuilderVerboseMixin.add_body_lines.<locals>.<listcomp>)r¦ r¥ r r² r§ r r )r1 ÚrowZ body_liner r r Úadd_body_lines s
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þÿz'TableBuilderVerboseMixin.add_body_linesú Iterator[str]c c s | j D ]}| dV qdS )z7Iterator with string representation of non-null counts.z non-nullN)r8 )r1 r^ r r r Ú_gen_non_null_counts s
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z-TableBuilderVerboseMixin._gen_non_null_countsc c s | j D ]}t|V qdS )z5Iterator with string representation of column dtypes.N)r2 r )r1 Zdtyper r r Ú _gen_dtypes s
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z$TableBuilderVerboseMixin._gen_dtypesN)rK rL rM rN r¥ rO rP r rª r® r³ r± r¹ r· r¸ r¾ rÁ rà rÅ rÆ r r r r r¤ I s,
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r¤ c @ s e Zd ZdZddddddZddd d
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Zed dd d ZddddZddddZ ddddZ
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ddddZ ddddZ dS )r z:
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Dataframe info table builder for verbose output.
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rQ rv rC ©rb r~ r c C s( || _ || _t| ¡ | _| ¡ | _d S rI ©rb r~ rµ r¹ r¦ r³ r§ ©r1 rb r~ r r r rU « s z%DataFrameTableBuilderVerbose.__init__r/ c C sJ | ¡ | ¡ | ¡ | ¡ | ¡ | ¡ | ¡ | jrF| ¡ dS r ) r r r¡ r¾ rÁ rà r r r r0 r r r r ¶ s z1DataFrameTableBuilderVerbose._fill_non_empty_infor¨ c C s | j rddddgS dddgS )r© z # ZColumnúNon-Null Countr ©r~ r0 r r r rª  s z$DataFrameTableBuilderVerbose.headersc C s | j d| j d¡ d S )NzData columns (total z
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columns):)r r r\ r0 r r r r¡ É s z5DataFrameTableBuilderVerbose.add_columns_summary_liner¶ c c s" t | ¡ | ¡ | ¡ E dH dS r» )r² Ú_gen_line_numbersÚ _gen_columnsrÆ r0 r r r r¸ Ì s
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ýz5DataFrameTableBuilderVerbose._gen_rows_without_countsc c s( t | ¡ | ¡ | ¡ | ¡ E dH dS rº )r² rÌ rÍ rÅ rÆ r0 r r r r· Ô s üz2DataFrameTableBuilderVerbose._gen_rows_with_countsrÄ c c s$ t | jD ]\}}d| V q
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dS )z6Iterator with string representation of column numbers.r$ N)Ú enumeraterY )r1 ÚiÚ_r r r rÌ Ý s z.DataFrameTableBuilderVerbose._gen_line_numbersc c s | j D ]}t|V qdS )z4Iterator with string representation of column names.N)rY r )r1 r r r r rÍ â s
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z)DataFrameTableBuilderVerbose._gen_columnsN) rK rL rM rN rU r rP rª r¡ r¸ r· rÌ rÍ r r r r r ¦ s r c @ s` e Zd ZdZdddddZddd d
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Zed dd d ZddddZe ddddZ
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dS )r z
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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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rg rC r c C s
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|| _ d S rI r r r r r rU ò s zSeriesTableBuilder.__init__r
r/ c C s g | _ | ¡ | j S rI )r r r0 r r r rl õ s z SeriesTableBuilder.get_linesr c C s | j jS )zSeries.r r0 r r r r- ú s zSeriesTableBuilder.datac C s | j d| j ¡ dS r r r0 r r r r ÿ s z(SeriesTableBuilder.add_memory_usage_linec C s dS ©z<Add lines to the info table, pertaining to non-empty series.Nr r0 r r r r s z'SeriesTableBuilder._fill_non_empty_infoN) rK rL rM rN rU rl rP r- r r r r r r r r è s r c @ s e Zd ZdZddddZdS )r z;
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Series info table builder for non-verbose output.
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rC r/ c C s* | ¡ | ¡ | ¡ | jr&| ¡ dS rÑ )r r r r r r0 r r r r s
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z1SeriesTableBuilderNonVerbose._fill_non_empty_infoN)rK rL rM rN r r r r r r s r c @ sl e Zd ZdZddddddZddd d
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Zddd d Zed dddZddddZ ddddZ
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dS )r z7
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Series info table builder for verbose output.
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rg rv rC rÇ c C s( || _ || _t| ¡ | _| ¡ | _d S rI rÈ rÉ r r r rU s z"SeriesTableBuilderVerbose.__init__r/ c C sJ | ¡ | ¡ | ¡ | ¡ | ¡ | ¡ | ¡ | jrF| ¡ dS rÑ ) r r Úadd_series_name_liner¾ rÁ rà r r r r0 r r r r & s z.SeriesTableBuilderVerbose._fill_non_empty_infoc C s | j d| jj ¡ d S )Nz Series name: )r r r- r£ r0 r r r rÒ 2 s z.SeriesTableBuilderVerbose.add_series_name_liner¨ c C s | j rddgS dgS )r© rÊ r rË r0 r r r rª 5 s z!SeriesTableBuilderVerbose.headersr¶ c c s | ¡ E dH dS r» )rÆ r0 r r r r¸ < s z2SeriesTableBuilderVerbose._gen_rows_without_countsc c s t | ¡ | ¡ E dH dS rº )r² rÅ rÆ r0 r r r r· @ s þz/SeriesTableBuilderVerbose._gen_rows_with_countsN) rK rL rM rN rU r rÒ rP rª r¸ r· r r r r r s r r3 )Údfr c C s | j ¡ dd ¡ ¡ S )zK
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Create mapping between datatypes and their number of occurrences.
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c S s | j S rI r¢ )r% r r r Ú<lambda>M ó z-_get_dataframe_dtype_counts.<locals>.<lambda>)r2 Z value_countsÚgroupbyr` )rÓ r r r rV H s rV )N)7Ú
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__future__r Úabcr r rm Útextwrapr Útypingr r r r r
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Zpandas._configr Zpandas._typingr r Zpandas.io.formatsr ro Zpandas.io.formats.printingr Zpandasr r r Zframe_max_cols_subr Zframe_examples_subZframe_see_also_subZframe_sub_kwargsZseries_examples_subZseries_see_also_subZseries_sub_kwargsZINFO_DOCSTRINGr r& r* r+ rQ rg rj rc ri rq r| r r¤ r r r r rV r r r r Ú<module> s ÿ
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ÿ ÿVÿ ù ÿ>ÿù ÿ3 ÿ SI<Q+83]B 2
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