Pandas自定義選項option設(shè)置

更新時間：2021年07月23日 09:55:18 作者：程序那些事

pandas有一個option系統(tǒng)可以控制pandas的展示情況，一般來說我們不需要進行修改，但是不排除特殊情況下的修改需求。本文將會詳細講解pandas中的option設(shè)置，感興趣的可以了解下

簡介

pandas有一個option系統(tǒng)可以控制pandas的展示情況，一般來說我們不需要進行修改，但是不排除特殊情況下的修改需求。本文將會詳細講解pandas中的option設(shè)置。

常用選項

pd.options.display 可以控制展示選項，比如設(shè)置最大展示行數(shù)：

In [1]: import pandas as pd

In [2]: pd.options.display.max_rows
Out[2]: 15

In [3]: pd.options.display.max_rows = 999

In [4]: pd.options.display.max_rows
Out[4]: 999

除此之外，pd還有4個相關(guān)的方法來對option進行修改：

get_option() / set_option() - get/set 單個option的值
reset_option() - 重設(shè)某個option的值到默認值
describe_option() - 打印某個option的值
option_context() - 在代碼片段中執(zhí)行某些option的更改

如下所示：

In [5]: pd.get_option("display.max_rows")
Out[5]: 999

In [6]: pd.set_option("display.max_rows", 101)

In [7]: pd.get_option("display.max_rows")
Out[7]: 101

In [8]: pd.set_option("max_r", 102)

In [9]: pd.get_option("display.max_rows")
Out[9]: 102

get/set 選項

pd.get_option 和 pd.set_option 可以用來獲取和修改特定的option：

In [11]: pd.get_option("mode.sim_interactive")
Out[11]: False

In [12]: pd.set_option("mode.sim_interactive", True)

In [13]: pd.get_option("mode.sim_interactive")
Out[13]: True

使用 reset_option 來重置：

In [14]: pd.get_option("display.max_rows")
Out[14]: 60

In [15]: pd.set_option("display.max_rows", 999)

In [16]: pd.get_option("display.max_rows")
Out[16]: 999

In [17]: pd.reset_option("display.max_rows")

In [18]: pd.get_option("display.max_rows")
Out[18]: 60

使用正則表達式可以重置多條option：

In [19]: pd.reset_option("^display")

option_context 在代碼環(huán)境中修改option，代碼結(jié)束之后,option會被還原：

In [20]: with pd.option_context("display.max_rows", 10, "display.max_columns", 5):
   ....:     print(pd.get_option("display.max_rows"))
   ....:     print(pd.get_option("display.max_columns"))
   ....: 
10
5

In [21]: print(pd.get_option("display.max_rows"))
60

In [22]: print(pd.get_option("display.max_columns"))
0

經(jīng)常使用的選項

下面我們看一些經(jīng)常使用選項的例子：

最大展示行數(shù)

display.max_rows 和 display.max_columns 可以設(shè)置最大展示行數(shù)和列數(shù)：

In [23]: df = pd.DataFrame(np.random.randn(7, 2))

In [24]: pd.set_option("max_rows", 7)

In [25]: df
Out[25]: 
          0         1
0  0.469112 -0.282863
1 -1.509059 -1.135632
2  1.212112 -0.173215
3  0.119209 -1.044236
4 -0.861849 -2.104569
5 -0.494929  1.071804
6  0.721555 -0.706771

In [26]: pd.set_option("max_rows", 5)

In [27]: df
Out[27]: 
           0         1
0   0.469112 -0.282863
1  -1.509059 -1.135632
..       ...       ...
5  -0.494929  1.071804
6   0.721555 -0.706771

[7 rows x 2 columns]

超出數(shù)據(jù)展示

display.large_repr 可以選擇對于超出的行或者列的展示行為，可以是truncated frame：

In [43]: df = pd.DataFrame(np.random.randn(10, 10))

In [44]: pd.set_option("max_rows", 5)

In [45]: pd.set_option("large_repr", "truncate")

In [46]: df
Out[46]: 
           0         1         2         3         4         5         6         7         8         9
0  -0.954208  1.462696 -1.743161 -0.826591 -0.345352  1.314232  0.690579  0.995761  2.396780  0.014871
1   3.357427 -0.317441 -1.236269  0.896171 -0.487602 -0.082240 -2.182937  0.380396  0.084844  0.432390
..       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...
8  -0.303421 -0.858447  0.306996 -0.028665  0.384316  1.574159  1.588931  0.476720  0.473424 -0.242861
9  -0.014805 -0.284319  0.650776 -1.461665 -1.137707 -0.891060 -0.693921  1.613616  0.464000  0.227371

[10 rows x 10 columns]

也可以是統(tǒng)計信息：

In [47]: pd.set_option("large_repr", "info")

In [48]: df
Out[48]: 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   0       10 non-null     float64
 1   1       10 non-null     float64
 2   2       10 non-null     float64
 3   3       10 non-null     float64
 4   4       10 non-null     float64
 5   5       10 non-null     float64
 6   6       10 non-null     float64
 7   7       10 non-null     float64
 8   8       10 non-null     float64
 9   9       10 non-null     float64
dtypes: float64(10)
memory usage: 928.0 bytes

最大列的寬度

display.max_colwidth 用來設(shè)置最大列的寬度。
In [51]: df = pd.DataFrame(
   ....:     np.array(
   ....:         [
   ....:             ["foo", "bar", "bim", "uncomfortably long string"],
   ....:             ["horse", "cow", "banana", "apple"],
   ....:         ]
   ....:     )
   ....: )
   ....: 

In [52]: pd.set_option("max_colwidth", 40)

In [53]: df
Out[53]: 
       0    1       2                          3
0    foo  bar     bim  uncomfortably long string
1  horse  cow  banana                      apple

In [54]: pd.set_option("max_colwidth", 6)

In [55]: df
Out[55]: 
       0    1      2      3
0    foo  bar    bim  un...
1  horse  cow  ba...  apple

顯示精度

display.precision 可以設(shè)置顯示的精度：

In [70]: df = pd.DataFrame(np.random.randn(5, 5))

In [71]: pd.set_option("precision", 7)

In [72]: df
Out[72]: 
           0          1          2          3          4
0 -1.1506406 -0.7983341 -0.5576966  0.3813531  1.3371217
1 -1.5310949  1.3314582 -0.5713290 -0.0266708 -1.0856630
2 -1.1147378 -0.0582158 -0.4867681  1.6851483  0.1125723
3 -1.4953086  0.8984347 -0.1482168 -1.5960698  0.1596530
4  0.2621358  0.0362196  0.1847350 -0.2550694 -0.2710197

零轉(zhuǎn)換的門檻

display.chop_threshold 可以設(shè)置將Series或者DF中數(shù)據(jù)展示為0的門檻：

In [75]: df = pd.DataFrame(np.random.randn(6, 6))

In [76]: pd.set_option("chop_threshold", 0)

In [77]: df
Out[77]: 
        0       1       2       3       4       5
0  1.2884  0.2946 -1.1658  0.8470 -0.6856  0.6091
1 -0.3040  0.6256 -0.0593  0.2497  1.1039 -1.0875
2  1.9980 -0.2445  0.1362  0.8863 -1.3507 -0.8863
3 -1.0133  1.9209 -0.3882 -2.3144  0.6655  0.4026
4  0.3996 -1.7660  0.8504  0.3881  0.9923  0.7441
5 -0.7398 -1.0549 -0.1796  0.6396  1.5850  1.9067

In [78]: pd.set_option("chop_threshold", 0.5)

In [79]: df
Out[79]: 
        0       1       2       3       4       5
0  1.2884  0.0000 -1.1658  0.8470 -0.6856  0.6091
1  0.0000  0.6256  0.0000  0.0000  1.1039 -1.0875
2  1.9980  0.0000  0.0000  0.8863 -1.3507 -0.8863
3 -1.0133  1.9209  0.0000 -2.3144  0.6655  0.0000
4  0.0000 -1.7660  0.8504  0.0000  0.9923  0.7441
5 -0.7398 -1.0549  0.0000  0.6396  1.5850  1.9067

上例中，絕對值< 0.5 的都會被展示為0 。

列頭的對齊方向

display.colheader_justify 可以修改列頭部文字的對齊方向：

In [81]: df = pd.DataFrame(
   ....:     np.array([np.random.randn(6), np.random.randint(1, 9, 6) * 0.1, np.zeros(6)]).T,
   ....:     columns=["A", "B", "C"],
   ....:     dtype="float",
   ....: )
   ....: 

In [82]: pd.set_option("colheader_justify", "right")

In [83]: df
Out[83]: 
        A    B    C
0  0.1040  0.1  0.0
1  0.1741  0.5  0.0
2 -0.4395  0.4  0.0
3 -0.7413  0.8  0.0
4 -0.0797  0.4  0.0
5 -0.9229  0.3  0.0

In [84]: pd.set_option("colheader_justify", "left")

In [85]: df
Out[85]: 
   A       B    C  
0  0.1040  0.1  0.0
1  0.1741  0.5  0.0
2 -0.4395  0.4  0.0
3 -0.7413  0.8  0.0
4 -0.0797  0.4  0.0
5 -0.9229  0.3  0.0

常見的選項表格：

選項	默認值	描述
display.chop_threshold	None	If set to a float value, all float values smaller then the given threshold will be displayed as exactly 0 by repr and friends.
display.colheader_justify	right	Controls the justification of column headers. used by DataFrameFormatter.
display.column_space	12	No description available.
display.date_dayfirst	False	When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst	False	When True, prints and parses dates with the year first, eg 2005/01/20
display.encoding	UTF-8	Defaults to the detected encoding of the console. Specifies the encoding to be used for strings returned by to_string, these are generally strings meant to be displayed on the console.
display.expand_frame_repr	True	Whether to print out the full DataFrame repr for wide DataFrames across multiple lines, `max_columns` is still respected, but the output will wrap-around across multiple “pages” if its width exceeds `display.width`.
display.float_format	None	The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. See core.format.EngFormatter for an example.
display.large_repr	truncate	For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can show a truncated table (the default), or switch to the view from df.info() (the behaviour in earlier versions of pandas). allowable settings, [‘truncate', ‘info']
display.latex.repr	False	Whether to produce a latex DataFrame representation for Jupyter frontends that support it.
display.latex.escape	True	Escapes special characters in DataFrames, when using the to_latex method.
display.latex.longtable	False	Specifies if the to_latex method of a DataFrame uses the longtable format.
display.latex.multicolumn	True	Combines columns when using a MultiIndex
display.latex.multicolumn_format	‘l'	Alignment of multicolumn labels
display.latex.multirow	False	Combines rows when using a MultiIndex. Centered instead of top-aligned, separated by clines.
display.max_columns	0 or 20	max_rows and max_columns are used in repr() methods to decide if to_string() or info() is used to render an object to a string. In case Python/IPython is running in a terminal this is set to 0 by default and pandas will correctly auto-detect the width of the terminal and switch to a smaller format in case all columns would not fit vertically. The IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to do correct auto-detection, in which case the default is set to 20. ‘None' value means unlimited.
display.max_colwidth	50	The maximum width in characters of a column in the repr of a pandas data structure. When the column overflows, a “…” placeholder is embedded in the output. ‘None' value means unlimited.
display.max_info_columns	100	max_info_columns is used in DataFrame.info method to decide if per column information will be printed.
display.max_info_rows	1690785	df.info() will usually show null-counts for each column. For large frames this can be quite slow. max_info_rows and max_info_cols limit this null check only to frames with smaller dimensions then specified.
display.max_rows	60	This sets the maximum number of rows pandas should output when printing out various output. For example, this value determines whether the repr() for a dataframe prints out fully or just a truncated or summary repr. ‘None' value means unlimited.
display.min_rows	10	The numbers of rows to show in a truncated repr (when `max_rows` is exceeded). Ignored when `max_rows` is set to None or 0. When set to None, follows the value of `max_rows`.
display.max_seq_items	100	when pretty-printing a long sequence, no more then `max_seq_items` will be printed. If items are omitted, they will be denoted by the addition of “…” to the resulting string. If set to None, the number of items to be printed is unlimited.
display.memory_usage	True	This specifies if the memory usage of a DataFrame should be displayed when the df.info() method is invoked.
display.multi_sparse	True	“Sparsify” MultiIndex display (don't display repeated elements in outer levels within groups)
display.notebook_repr_html	True	When True, IPython notebook will use html representation for pandas objects (if it is available).
display.pprint_nest_depth	3	Controls the number of nested levels to process when pretty-printing
display.precision	6	Floating point output precision in terms of number of places after the decimal, for regular formatting as well as scientific notation. Similar to numpy's `precision` print option
display.show_dimensions	truncate	Whether to print out dimensions at the end of DataFrame repr. If ‘truncate' is specified, only print out the dimensions if the frame is truncated (e.g. not display all rows and/or columns)
display.width	80	Width of the display in characters. In case Python/IPython is running in a terminal this can be set to None and pandas will correctly auto-detect the width. Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to correctly detect the width.
display.html.table_schema	False	Whether to publish a Table Schema representation for frontends that support it.
display.html.border	1	A `border=value` attribute is inserted in the `<table>` tag for the DataFrame HTML repr.
display.html.use_mathjax	True	When True, Jupyter notebook will process table contents using MathJax, rendering mathematical expressions enclosed by the dollar symbol.
io.excel.xls.writer	xlwt	The default Excel writer engine for ‘xls' files.Deprecated since version 1.2.0: As xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. Since this is the only engine in pandas that supports writing to .xls files, this option will also be removed.
io.excel.xlsm.writer	openpyxl	The default Excel writer engine for ‘xlsm' files. Available options: ‘openpyxl' (the default).
io.excel.xlsx.writer	openpyxl	The default Excel writer engine for ‘xlsx' files.
io.hdf.default_format	None	default format writing format, if None, then put will default to ‘fixed' and append will default to ‘table'
io.hdf.dropna_table	True	drop ALL nan rows when appending to a table
io.parquet.engine	None	The engine to use as a default for parquet reading and writing. If None then try ‘pyarrow' and ‘fastparquet'
mode.chained_assignment	warn	Controls SettingWithCopyWarning: ‘raise', ‘warn', or None. Raise an exception, warn, or no action if trying to use chained assignment.
mode.sim_interactive	False	Whether to simulate interactive mode for purposes of testing.
mode.use_inf_as_na	False	True means treat None, NaN, -INF, INF as NA (old way), False means None and NaN are null, but INF, -INF are not NA (new way).
compute.use_bottleneck	True	Use the bottleneck library to accelerate computation if it is installed.
compute.use_numexpr	True	Use the numexpr library to accelerate computation if it is installed.
plotting.backend	matplotlib	Change the plotting backend to a different backend than the current matplotlib one. Backends can be implemented as third-party libraries implementing the pandas plotting API. They can use other plotting libraries like Bokeh, Altair, etc.
plotting.matplotlib.register_converters	True	Register custom converters with matplotlib. Set to False to de-register.