Pandas高级教程之:自定义选项 _自定义

我自横刀向天笑，去留肝胆两昆仑。这篇文章主要讲述Pandas高级教程之:自定义选项相关的知识，希望能为你提供帮助。
简介pandas有一个option系统可以控制pandas的展示情况，一般来说我们不需要进行修改，但是不排除特殊情况下的修改需求。本文将会详细讲解pandas中的option设置。
常用选项pd.options.display 可以控制展示选项，比如设置最大展示行数：

In [1]: import pandas as pdIn [2]: pd.options.display.max_rows Out[2]: 15In [3]: pd.options.display.max_rows = 999In [4]: pd.options.display.max_rows Out[4]: 999

除此之外，pd还有4个相关的方法来对option进行修改：

get_option() / set_option() – get/set 单个option的值
reset_option() – 重设某个option的值到默认值
describe_option() – 打印某个option的值
option_context() – 在代码片段中执行某些option的更改

如下所示：

In [5]: pd.get_option("display.max_rows") Out[5]: 999In [6]: pd.set_option("display.max_rows", 101)In [7]: pd.get_option("display.max_rows") Out[7]: 101In [8]: pd.set_option("max_r", 102)In [9]: pd.get_option("display.max_rows") Out[9]: 102

get/set 选项pd.get_option 和 pd.set_option 可以用来获取和修改特定的option：

In [11]: pd.get_option("mode.sim_interactive") Out[11]: FalseIn [12]: pd.set_option("mode.sim_interactive", True)In [13]: pd.get_option("mode.sim_interactive") Out[13]: True

使用 reset_option 来重置：

In [14]: pd.get_option("display.max_rows") Out[14]: 60In [15]: pd.set_option("display.max_rows", 999)In [16]: pd.get_option("display.max_rows") Out[16]: 999In [17]: pd.reset_option("display.max_rows")In [18]: pd.get_option("display.max_rows") Out[18]: 60

【Pandas高级教程之:自定义选项】使用正则表达式可以重置多条option：

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

option_context 在代码环境中修改option，代码结束之后,option会被还原：

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 5In [21]: print(pd.get_option("display.max_rows")) 60In [22]: print(pd.get_option("display.max_columns")) 0

经常使用的选项下面我们看一些经常使用选项的例子：
最大展示行数display.max_rows 和 display.max_columns 可以设置最大展示行数和列数：

In [23]: df = pd.DataFrame(np.random.randn(7, 2))In [24]: pd.set_option("max_rows", 7)In [25]: df Out[25]: 01 00.469112 -0.282863 1 -1.509059 -1.135632 21.212112 -0.173215 30.119209 -1.044236 4 -0.861849 -2.104569 5 -0.4949291.071804 60.721555 -0.706771In [26]: pd.set_option("max_rows", 5)In [27]: df Out[27]: 01 00.469112 -0.282863 1-1.509059 -1.135632 ........ 5-0.4949291.071804 60.721555 -0.706771[7 rows x 2 columns]

超出数据展示display.large_repr 可以选择对于超出的行或者列的展示行为，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 0-0.9542081.462696 -1.743161 -0.826591 -0.3453521.3142320.6905790.9957612.3967800.014871 13.357427 -0.317441 -1.2362690.896171 -0.487602 -0.082240 -2.1829370.3803960.0848440.432390 ................................ 8-0.303421 -0.8584470.306996 -0.0286650.3843161.5741591.5889310.4767200.473424 -0.242861 9-0.014805 -0.2843190.650776 -1.461665 -1.137707 -0.891060 -0.6939211.6136160.4640000.227371[10 rows x 10 columns]

也可以是统计信息：

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): #ColumnNon-Null CountDtype ---------------------------- 0010 non-nullfloat64 1110 non-nullfloat64 2210 non-nullfloat64 3310 non-nullfloat64 4410 non-nullfloat64 5510 non-nullfloat64 6610 non-nullfloat64 7710 non-nullfloat64 8810 non-nullfloat64 9910 non-nullfloat64 dtypes: float64(10) memory usage: 928.0 bytes

最大列的宽度display.max_colwidth 用来设置最大列的宽度。

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]: 0123 0foobarbimuncomfortably long string 1horsecowbananaappleIn [54]: pd.set_option("max_colwidth", 6)In [55]: df Out[55]: 0123 0foobarbimun... 1horsecowba...apple

显示精度display.precision 可以设置显示的精度：

In [70]: df = pd.DataFrame(np.random.randn(5, 5))In [71]: pd.set_option("precision", 7)In [72]: df Out[72]: 01234 0 -1.1506406 -0.7983341 -0.55769660.38135311.3371217 1 -1.53109491.3314582 -0.5713290 -0.0266708 -1.0856630 2 -1.1147378 -0.0582158 -0.48676811.68514830.1125723 3 -1.49530860.8984347 -0.1482168 -1.59606980.1596530 40.26213580.03621960.1847350 -0.2550694 -0.2710197

零转换的门槛display.chop_threshold 可以设置将Series或者DF中数据展示为0的门槛：

In [75]: df = pd.DataFrame(np.random.randn(6, 6))In [76]: pd.set_option("chop_threshold", 0)In [77]: df Out[77]: 012345 01.28840.2946 -1.16580.8470 -0.68560.6091 1 -0.30400.6256 -0.05930.24971.1039 -1.0875 21.9980 -0.24450.13620.8863 -1.3507 -0.8863 3 -1.01331.9209 -0.3882 -2.31440.66550.4026 40.3996 -1.76600.85040.38810.99230.7441 5 -0.7398 -1.0549 -0.17960.63961.58501.9067In [78]: pd.set_option("chop_threshold", 0.5)In [79]: df Out[79]: 012345 01.28840.0000 -1.16580.8470 -0.68560.6091 10.00000.62560.00000.00001.1039 -1.0875 21.99800.00000.00000.8863 -1.3507 -0.8863 3 -1.01331.92090.0000 -2.31440.66550.0000 40.0000 -1.76600.85040.00000.99230.7441 5 -0.7398 -1.05490.00000.63961.58501.9067

上例中，绝对值< 0.5 的都会被展示为0 。
列头的对齐方向display.colheader_justify 可以修改列头部文字的对齐方向：

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]: ABC 00.10400.10.0 10.17410.50.0 2 -0.43950.40.0 3 -0.74130.80.0 4 -0.07970.40.0 5 -0.92290.30.0In [84]: pd.set_option("colheader_justify", "left")In [85]: df Out[85]: ABC 00.10400.10.0 10.17410.50.0 2 -0.43950.40.0 3 -0.74130.80.0 4 -0.07970.40.0 5 -0.92290.30.0

常见的选项表格：

选项	默认值	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.