Pandas自定義選項(xiàng)option設(shè)置
發(fā)布日期：2022-02-22 10:13 | 文章來源：gibhub
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(jìn)行修改，但是不排除特殊情況下的修改需求。本文將會詳細(xì)講解pandas中的option設(shè)置。
常用選項(xiàng)
pd.options.display 可以控制展示選項(xiàng)，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(jìn)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(rèn)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(xiàng)
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(dá)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(xiàng)
下面我們看一些經(jīng)常使用選項(xiàng)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]:
01
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]:
01
00.469112 -0.282863
1 -1.509059 -1.135632
.. ... ...
5 -0.494929 1.071804
60.721555 -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]:
0123456789
0 -0.954208 1.462696 -1.743161 -0.826591 -0.345352 1.314232 0.690579 0.995761 2.396780 0.014871
13.357427 -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(jì)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
--- ------ -------------- -----
00 10 non-null float64
11 10 non-null float64
22 10 non-null float64
33 10 non-null float64
44 10 non-null float64
55 10 non-null float64
66 10 non-null float64
77 10 non-null float64
88 10 non-null float64
99 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]:
0 123
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(xiàng)表格：
| 選項(xiàng) | 默認(rèn)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. |
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