选项和设置#
概述#
Pandas有一个选项API配置和定制与以下相关的全局行为 DataFrame 显示、数据行为等。
选项具有完整的“点式”、不区分大小写的名称(例如 display.max_rows )。您可以直接作为顶层的属性获取/设置选项 options 属性：
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
该接口由5个相关函数组成，可直接从 pandas 命名空间：
get_option()/set_option()-获取/设置单个选项的值。reset_option()-将一个或多个选项重置为其缺省值。describe_option()-打印一个或多个选项的说明。option_context()-使用一组选项执行代码块，这些选项在执行后恢复为以前的设置。
备注
开发人员可以查看 pandas/core/config_init.py 了解更多信息。
上面的所有函数都接受regexp模式 (re.search Style)作为参数，以匹配明确的子字符串：
In [5]: pd.get_option("display.chop_threshold")
In [6]: pd.set_option("display.chop_threshold", 2)
In [7]: pd.get_option("display.chop_threshold")
Out[7]: 2
In [8]: pd.set_option("chop", 4)
In [9]: pd.get_option("display.chop_threshold")
Out[9]: 4
以下将是 不工作 因为它匹配多个选项名称，例如 display.max_colwidth ， display.max_rows ， display.max_columns ：
In [10]: pd.get_option("max")
---------------------------------------------------------------------------
OptionError Traceback (most recent call last)
Input In [10], in <cell line: 1>()
----> 1 pd.get_option("max")
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/_config/config.py:256, in CallableDynamicDoc.__call__(self, *args, **kwds)
255 def __call__(self, *args, **kwds):
--> 256 return self.__func__(*args, **kwds)
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/_config/config.py:128, in _get_option(pat, silent)
127 def _get_option(pat: str, silent: bool = False):
--> 128 key = _get_single_key(pat, silent)
130 # walk the nested dict
131 root, k = _get_root(key)
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/_config/config.py:116, in _get_single_key(pat, silent)
114 raise OptionError(f"No such keys(s): {repr(pat)}")
115 if len(keys) > 1:
--> 116 raise OptionError("Pattern matched multiple keys")
117 key = keys[0]
119 if not silent:
OptionError: Pattern matched multiple keys
警告
如果在将来的版本中添加具有类似名称的新选项，则使用这种形式的速记可能会导致代码崩溃。
可用选项#
您可以使用以下命令获取可用选项及其说明的列表 describe_option() 。在没有参数的情况下调用时 describe_option() 将打印出所有可用选项的说明。
In [11]: pd.describe_option()
compute.use_bottleneck : bool
Use the bottleneck library to accelerate if it is installed,
the default is True
Valid values: False,True
[default: True] [currently: True]
compute.use_numba : bool
Use the numba engine option for select operations if it is installed,
the default is False
Valid values: False,True
[default: False] [currently: False]
compute.use_numexpr : bool
Use the numexpr library to accelerate computation if it is installed,
the default is True
Valid values: False,True
[default: True] [currently: True]
display.chop_threshold : float or 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.
[default: None] [currently: None]
display.colheader_justify : 'left'/'right'
Controls the justification of column headers. used by DataFrameFormatter.
[default: right] [currently: right]
display.column_space No description available.
[default: 12] [currently: 12]
display.date_dayfirst : boolean
When True, prints and parses dates with the day first, eg 20/01/2005
[default: False] [currently: False]
display.date_yearfirst : boolean
When True, prints and parses dates with the year first, eg 2005/01/20
[default: False] [currently: False]
display.encoding : str/unicode
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.
[default: utf-8] [currently: utf8]
display.expand_frame_repr : boolean
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`.
[default: True] [currently: True]
display.float_format : callable
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 formats.format.EngFormatter for an example.
[default: None] [currently: None]
display.html.border : int
A ``border=value`` attribute is inserted in the ``<table>`` tag
for the DataFrame HTML repr.
[default: 1] [currently: 1]
display.html.table_schema : boolean
Whether to publish a Table Schema representation for frontends
that support it.
(default: False)
[default: False] [currently: False]
display.html.use_mathjax : boolean
When True, Jupyter notebook will process table contents using MathJax,
rendering mathematical expressions enclosed by the dollar symbol.
(default: True)
[default: True] [currently: True]
display.large_repr : 'truncate'/'info'
For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
show a truncated table (the default from 0.13), or switch to the view from
df.info() (the behaviour in earlier versions of pandas).
[default: truncate] [currently: truncate]
display.latex.escape : bool
This specifies if the to_latex method of a Dataframe uses escapes special
characters.
Valid values: False,True
[default: True] [currently: True]
display.latex.longtable :bool
This specifies if the to_latex method of a Dataframe uses the longtable
format.
Valid values: False,True
[default: False] [currently: False]
display.latex.multicolumn : bool
This specifies if the to_latex method of a Dataframe uses multicolumns
to pretty-print MultiIndex columns.
Valid values: False,True
[default: True] [currently: True]
display.latex.multicolumn_format : bool
This specifies if the to_latex method of a Dataframe uses multicolumns
to pretty-print MultiIndex columns.
Valid values: False,True
[default: l] [currently: l]
display.latex.multirow : bool
This specifies if the to_latex method of a Dataframe uses multirows
to pretty-print MultiIndex rows.
Valid values: False,True
[default: False] [currently: False]
display.latex.repr : boolean
Whether to produce a latex DataFrame representation for jupyter
environments that support it.
(default: False)
[default: False] [currently: False]
display.max_categories : int
This sets the maximum number of categories pandas should output when
printing out a `Categorical` or a Series of dtype "category".
[default: 8] [currently: 8]
display.max_columns : int
If max_cols is exceeded, switch to truncate view. Depending on
`large_repr`, objects are either centrally truncated or printed as
a summary view. 'None' value means unlimited.
In case python/IPython is running in a terminal and `large_repr`
equals 'truncate' this can be set to 0 and pandas will auto-detect
the width of the terminal and print a truncated object which fits
the screen width. The IPython notebook, IPython qtconsole, or IDLE
do not run in a terminal and hence it is not possible to do
correct auto-detection.
[default: 0] [currently: 0]
display.max_colwidth : int or None
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. A 'None' value means unlimited.
[default: 50] [currently: 50]
display.max_dir_items : int
The number of items that will be added to `dir(...)`. 'None' value means
unlimited. Because dir is cached, changing this option will not immediately
affect already existing dataframes until a column is deleted or added.
This is for instance used to suggest columns from a dataframe to tab
completion.
[default: 100] [currently: 100]
display.max_info_columns : int
max_info_columns is used in DataFrame.info method to decide if
per column information will be printed.
[default: 100] [currently: 100]
display.max_info_rows : int or None
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 than
specified.
[default: 1690785] [currently: 1690785]
display.max_rows : int
If max_rows is exceeded, switch to truncate view. Depending on
`large_repr`, objects are either centrally truncated or printed as
a summary view. 'None' value means unlimited.
In case python/IPython is running in a terminal and `large_repr`
equals 'truncate' this can be set to 0 and pandas will auto-detect
the height of the terminal and print a truncated object which fits
the screen height. The IPython notebook, IPython qtconsole, or
IDLE do not run in a terminal and hence it is not possible to do
correct auto-detection.
[default: 60] [currently: 60]
display.max_seq_items : int or None
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.
[default: 100] [currently: 100]
display.memory_usage : bool, string or None
This specifies if the memory usage of a DataFrame should be displayed when
df.info() is called. Valid values True,False,'deep'
[default: True] [currently: True]
display.min_rows : int
The numbers of rows to show in a truncated view (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`.
[default: 10] [currently: 10]
display.multi_sparse : boolean
"sparsify" MultiIndex display (don't display repeated
elements in outer levels within groups)
[default: True] [currently: True]
display.notebook_repr_html : boolean
When True, IPython notebook will use html representation for
pandas objects (if it is available).
[default: True] [currently: True]
display.pprint_nest_depth : int
Controls the number of nested levels to process when pretty-printing
[default: 3] [currently: 3]
display.precision : int
Floating point output precision in terms of number of places after the
decimal, for regular formatting as well as scientific notation. Similar
to ``precision`` in :meth:`numpy.set_printoptions`.
[default: 6] [currently: 6]
display.show_dimensions : boolean or '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)
[default: truncate] [currently: truncate]
display.unicode.ambiguous_as_wide : boolean
Whether to use the Unicode East Asian Width to calculate the display text
width.
Enabling this may affect to the performance (default: False)
[default: False] [currently: False]
display.unicode.east_asian_width : boolean
Whether to use the Unicode East Asian Width to calculate the display text
width.
Enabling this may affect to the performance (default: False)
[default: False] [currently: False]
display.width : int
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.
[default: 80] [currently: 80]
io.excel.ods.reader : string
The default Excel reader engine for 'ods' files. Available options:
auto, odf.
[default: auto] [currently: auto]
io.excel.ods.writer : string
The default Excel writer engine for 'ods' files. Available options:
auto, odf.
[default: auto] [currently: auto]
io.excel.xls.reader : string
The default Excel reader engine for 'xls' files. Available options:
auto, xlrd.
[default: auto] [currently: auto]
io.excel.xls.writer : string
The default Excel writer engine for 'xls' files. Available options:
auto, xlwt.
[default: auto] [currently: auto]
(Deprecated, use `` instead.)
io.excel.xlsb.reader : string
The default Excel reader engine for 'xlsb' files. Available options:
auto, pyxlsb.
[default: auto] [currently: auto]
io.excel.xlsm.reader : string
The default Excel reader engine for 'xlsm' files. Available options:
auto, xlrd, openpyxl.
[default: auto] [currently: auto]
io.excel.xlsm.writer : string
The default Excel writer engine for 'xlsm' files. Available options:
auto, openpyxl.
[default: auto] [currently: auto]
io.excel.xlsx.reader : string
The default Excel reader engine for 'xlsx' files. Available options:
auto, xlrd, openpyxl.
[default: auto] [currently: auto]
io.excel.xlsx.writer : string
The default Excel writer engine for 'xlsx' files. Available options:
auto, openpyxl, xlsxwriter.
[default: auto] [currently: auto]
io.hdf.default_format : format
default format writing format, if None, then
put will default to 'fixed' and append will default to 'table'
[default: None] [currently: None]
io.hdf.dropna_table : boolean
drop ALL nan rows when appending to a table
[default: False] [currently: False]
io.parquet.engine : string
The default parquet reader/writer engine. Available options:
'auto', 'pyarrow', 'fastparquet', the default is 'auto'
[default: auto] [currently: auto]
io.sql.engine : string
The default sql reader/writer engine. Available options:
'auto', 'sqlalchemy', the default is 'auto'
[default: auto] [currently: auto]
mode.chained_assignment : string
Raise an exception, warn, or no action if trying to use chained assignment,
The default is warn
[default: warn] [currently: warn]
mode.data_manager : string
Internal data manager type; can be "block" or "array". Defaults to "block",
unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs
to be set before pandas is imported).
[default: block] [currently: block]
mode.sim_interactive : boolean
Whether to simulate interactive mode for purposes of testing
[default: False] [currently: False]
mode.string_storage : string
The default storage for StringDtype.
[default: python] [currently: python]
mode.use_inf_as_na : boolean
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).
[default: False] [currently: False]
mode.use_inf_as_null : boolean
use_inf_as_null had been deprecated and will be removed in a future
version. Use `use_inf_as_na` instead.
[default: False] [currently: False]
(Deprecated, use `mode.use_inf_as_na` instead.)
plotting.backend : str
The plotting backend to use. The default value is "matplotlib", the
backend provided with pandas. Other backends can be specified by
providing the name of the module that implements the backend.
[default: matplotlib] [currently: matplotlib]
plotting.matplotlib.register_converters : bool or 'auto'.
Whether to register converters with matplotlib's units registry for
dates, times, datetimes, and Periods. Toggling to False will remove
the converters, restoring any converters that pandas overwrote.
[default: auto] [currently: auto]
styler.format.decimal : str
The character representation for the decimal separator for floats and complex.
[default: .] [currently: .]
styler.format.escape : str, optional
Whether to escape certain characters according to the given context; html or latex.
[default: None] [currently: None]
styler.format.formatter : str, callable, dict, optional
A formatter object to be used as default within ``Styler.format``.
[default: None] [currently: None]
styler.format.na_rep : str, optional
The string representation for values identified as missing.
[default: None] [currently: None]
styler.format.precision : int
The precision for floats and complex numbers.
[default: 6] [currently: 6]
styler.format.thousands : str, optional
The character representation for thousands separator for floats, int and complex.
[default: None] [currently: None]
styler.html.mathjax : bool
If False will render special CSS classes to table attributes that indicate Mathjax
will not be used in Jupyter Notebook.
[default: True] [currently: True]
styler.latex.environment : str
The environment to replace ``\begin{table}``. If "longtable" is used results
in a specific longtable environment format.
[default: None] [currently: None]
styler.latex.hrules : bool
Whether to add horizontal rules on top and bottom and below the headers.
[default: False] [currently: False]
styler.latex.multicol_align : {"r", "c", "l", "naive-l", "naive-r"}
The specifier for horizontal alignment of sparsified LaTeX multicolumns. Pipe
decorators can also be added to non-naive values to draw vertical
rules, e.g. "\|r" will draw a rule on the left side of right aligned merged cells.
[default: r] [currently: r]
styler.latex.multirow_align : {"c", "t", "b"}
The specifier for vertical alignment of sparsified LaTeX multirows.
[default: c] [currently: c]
styler.render.encoding : str
The encoding used for output HTML and LaTeX files.
[default: utf-8] [currently: utf-8]
styler.render.max_columns : int, optional
The maximum number of columns that will be rendered. May still be reduced to
satsify ``max_elements``, which takes precedence.
[default: None] [currently: None]
styler.render.max_elements : int
The maximum number of data-cell (<td>) elements that will be rendered before
trimming will occur over columns, rows or both if needed.
[default: 262144] [currently: 262144]
styler.render.max_rows : int, optional
The maximum number of rows that will be rendered. May still be reduced to
satsify ``max_elements``, which takes precedence.
[default: None] [currently: None]
styler.render.repr : str
Determine which output to use in Jupyter Notebook in {"html", "latex"}.
[default: html] [currently: html]
styler.sparse.columns : bool
Whether to sparsify the display of hierarchical columns. Setting to False will
display each explicit level element in a hierarchical key for each column.
[default: True] [currently: True]
styler.sparse.index : bool
Whether to sparsify the display of a hierarchical index. Setting to False will
display each explicit level element in a hierarchical key for each row.
[default: True] [currently: True]
获取和设置选项#
如上所述， get_option() 和 set_option() 都可以从Pandas命名空间中获得。要更改选项，请调用 set_option('option regex', new_value) 。
In [12]: pd.get_option("mode.sim_interactive")
Out[12]: False
In [13]: pd.set_option("mode.sim_interactive", True)
In [14]: pd.get_option("mode.sim_interactive")
Out[14]: True
备注
该选项 'mode.sim_interactive' 主要用于调试目的。
您可以使用 reset_option() 恢复为设置的缺省值的步骤
In [15]: pd.get_option("display.max_rows")
Out[15]: 60
In [16]: pd.set_option("display.max_rows", 999)
In [17]: pd.get_option("display.max_rows")
Out[17]: 999
In [18]: pd.reset_option("display.max_rows")
In [19]: pd.get_option("display.max_rows")
Out[19]: 60
还可以一次重置多个选项(使用正则表达式)：
In [20]: pd.reset_option("^display")
option_context() 上下文管理器通过顶级API公开，允许您使用给定的选项值执行代码。当您退出时，选项值将自动恢复 with 挡路：
In [21]: 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 [22]: print(pd.get_option("display.max_rows"))
60
In [23]: print(pd.get_option("display.max_columns"))
0
在Python/IPython环境中设置启动选项#
使用用于Python/IPython环境的启动脚本来导入Pandas并设置选项，可以更高效地处理Pandas。为此，请创建一个 .py 或 .ipy 所需配置文件的启动目录中的脚本。可以在以下位置找到启动文件夹位于默认IPython配置文件中的示例：
$IPYTHONDIR/profile_default/startup
有关更多信息，请参阅 IPython documentation 。Pandas的启动脚本示例如下所示：
import pandas as pd
pd.set_option("display.max_rows", 999)
pd.set_option("display.precision", 5)
常用选项#
以下是更常用的显示选项的演示。
display.max_rows 和 display.max_columns 设置框架打印精美时显示的最大行数和列数。截断的行将被省略号替换。
In [24]: df = pd.DataFrame(np.random.randn(7, 2))
In [25]: pd.set_option("display.max_rows", 7)
In [26]: df
Out[26]:
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 [27]: pd.set_option("display.max_rows", 5)
In [28]: df
Out[28]:
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]
In [29]: pd.reset_option("display.max_rows")
一旦 display.max_rows 被超过，则 display.min_rows 选项确定在截断的REPR中显示的行数。
In [30]: pd.set_option("display.max_rows", 8)
In [31]: pd.set_option("display.min_rows", 4)
# below max_rows -> all rows shown
In [32]: df = pd.DataFrame(np.random.randn(7, 2))
In [33]: df
Out[33]:
0 1
0 -1.039575 0.271860
1 -0.424972 0.567020
2 0.276232 -1.087401
3 -0.673690 0.113648
4 -1.478427 0.524988
5 0.404705 0.577046
6 -1.715002 -1.039268
# above max_rows -> only min_rows (4) rows shown
In [34]: df = pd.DataFrame(np.random.randn(9, 2))
In [35]: df
Out[35]:
0 1
0 -0.370647 -1.157892
1 -1.344312 0.844885
.. ... ...
7 0.276662 -0.472035
8 -0.013960 -0.362543
[9 rows x 2 columns]
In [36]: pd.reset_option("display.max_rows")
In [37]: pd.reset_option("display.min_rows")
display.expand_frame_repr 允许表示一个 DataFrame 跨页伸展，包裹所有的栏。
In [38]: df = pd.DataFrame(np.random.randn(5, 10))
In [39]: pd.set_option("expand_frame_repr", True)
In [40]: df
Out[40]:
0 1 2 3 4 5 6 7 8 9
0 -0.006154 -0.923061 0.895717 0.805244 -1.206412 2.565646 1.431256 1.340309 -1.170299 -0.226169
1 0.410835 0.813850 0.132003 -0.827317 -0.076467 -1.187678 1.130127 -1.436737 -1.413681 1.607920
2 1.024180 0.569605 0.875906 -2.211372 0.974466 -2.006747 -0.410001 -0.078638 0.545952 -1.219217
3 -1.226825 0.769804 -1.281247 -0.727707 -0.121306 -0.097883 0.695775 0.341734 0.959726 -1.110336
4 -0.619976 0.149748 -0.732339 0.687738 0.176444 0.403310 -0.154951 0.301624 -2.179861 -1.369849
In [41]: pd.set_option("expand_frame_repr", False)
In [42]: df
Out[42]:
0 1 2 3 4 5 6 7 8 9
0 -0.006154 -0.923061 0.895717 0.805244 -1.206412 2.565646 1.431256 1.340309 -1.170299 -0.226169
1 0.410835 0.813850 0.132003 -0.827317 -0.076467 -1.187678 1.130127 -1.436737 -1.413681 1.607920
2 1.024180 0.569605 0.875906 -2.211372 0.974466 -2.006747 -0.410001 -0.078638 0.545952 -1.219217
3 -1.226825 0.769804 -1.281247 -0.727707 -0.121306 -0.097883 0.695775 0.341734 0.959726 -1.110336
4 -0.619976 0.149748 -0.732339 0.687738 0.176444 0.403310 -0.154951 0.301624 -2.179861 -1.369849
In [43]: pd.reset_option("expand_frame_repr")
display.large_repr 显示为 DataFrame 超过了 max_columns 或 max_rows 作为截断的框架或摘要。
In [44]: df = pd.DataFrame(np.random.randn(10, 10))
In [45]: pd.set_option("display.max_rows", 5)
In [46]: pd.set_option("large_repr", "truncate")
In [47]: df
Out[47]:
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]
In [48]: pd.set_option("large_repr", "info")
In [49]: df
Out[49]:
<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
In [50]: pd.reset_option("large_repr")
In [51]: pd.reset_option("display.max_rows")
display.max_colwidth 设置列的最大宽度。此长度或更长的单元格将用省略号截断。
In [52]: df = pd.DataFrame(
....: np.array(
....: [
....: ["foo", "bar", "bim", "uncomfortably long string"],
....: ["horse", "cow", "banana", "apple"],
....: ]
....: )
....: )
....:
In [53]: pd.set_option("max_colwidth", 40)
In [54]: df
Out[54]:
0 1 2 3
0 foo bar bim uncomfortably long string
1 horse cow banana apple
In [55]: pd.set_option("max_colwidth", 6)
In [56]: df
Out[56]:
0 1 2 3
0 foo bar bim un...
1 horse cow ba... apple
In [57]: pd.reset_option("max_colwidth")
display.max_info_columns 调用时显示的列数设置阈值 info() 。
In [58]: df = pd.DataFrame(np.random.randn(10, 10))
In [59]: pd.set_option("max_info_columns", 11)
In [60]: df.info()
<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
In [61]: pd.set_option("max_info_columns", 5)
In [62]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Columns: 10 entries, 0 to 9
dtypes: float64(10)
memory usage: 928.0 bytes
In [63]: pd.reset_option("max_info_columns")
display.max_info_rows: info() 通常会显示每列的空计数。对于一个大的 DataFrame ，这可能会相当慢。 max_info_rows 和 max_info_cols 将此NULL检查分别限制到指定的行和列。这个 info() 关键字参数 null_counts=True 将会推翻这一点。
In [64]: df = pd.DataFrame(np.random.choice([0, 1, np.nan], size=(10, 10)))
In [65]: df
Out[65]:
0 1 2 3 4 5 6 7 8 9
0 0.0 NaN 1.0 NaN NaN 0.0 NaN 0.0 NaN 1.0
1 1.0 NaN 1.0 1.0 1.0 1.0 NaN 0.0 0.0 NaN
2 0.0 NaN 1.0 0.0 0.0 NaN NaN NaN NaN 0.0
3 NaN NaN NaN 0.0 1.0 1.0 NaN 1.0 NaN 1.0
4 0.0 NaN NaN NaN 0.0 NaN NaN NaN 1.0 0.0
5 0.0 1.0 1.0 1.0 1.0 0.0 NaN NaN 1.0 0.0
6 1.0 1.0 1.0 NaN 1.0 NaN 1.0 0.0 NaN NaN
7 0.0 0.0 1.0 0.0 1.0 0.0 1.0 1.0 0.0 NaN
8 NaN NaN NaN 0.0 NaN NaN NaN NaN 1.0 NaN
9 0.0 NaN 0.0 NaN NaN 0.0 NaN 1.0 1.0 0.0
In [66]: pd.set_option("max_info_rows", 11)
In [67]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 0 8 non-null float64
1 1 3 non-null float64
2 2 7 non-null float64
3 3 6 non-null float64
4 4 7 non-null float64
5 5 6 non-null float64
6 6 2 non-null float64
7 7 6 non-null float64
8 8 6 non-null float64
9 9 6 non-null float64
dtypes: float64(10)
memory usage: 928.0 bytes
In [68]: pd.set_option("max_info_rows", 5)
In [69]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
# Column Dtype
--- ------ -----
0 0 float64
1 1 float64
2 2 float64
3 3 float64
4 4 float64
5 5 float64
6 6 float64
7 7 float64
8 8 float64
9 9 float64
dtypes: float64(10)
memory usage: 928.0 bytes
In [70]: pd.reset_option("max_info_rows")
display.precision 以小数位数为单位设置输出显示精度。
In [71]: df = pd.DataFrame(np.random.randn(5, 5))
In [72]: pd.set_option("display.precision", 7)
In [73]: df
Out[73]:
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
In [74]: pd.set_option("display.precision", 4)
In [75]: df
Out[75]:
0 1 2 3 4
0 -1.1506 -0.7983 -0.5577 0.3814 1.3371
1 -1.5311 1.3315 -0.5713 -0.0267 -1.0857
2 -1.1147 -0.0582 -0.4868 1.6851 0.1126
3 -1.4953 0.8984 -0.1482 -1.5961 0.1597
4 0.2621 0.0362 0.1847 -0.2551 -0.2710
display.chop_threshold 属性时将舍入阈值设置为零。 Series 或 DataFrame 。此设置不会更改存储数字的精度。
In [76]: df = pd.DataFrame(np.random.randn(6, 6))
In [77]: pd.set_option("chop_threshold", 0)
In [78]: df
Out[78]:
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 [79]: pd.set_option("chop_threshold", 0.5)
In [80]: df
Out[80]:
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
In [81]: pd.reset_option("chop_threshold")
display.colheader_justify 控制页眉的对齐方式。这些选项包括 'right' ，以及 'left' 。
In [82]: 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 [83]: pd.set_option("colheader_justify", "right")
In [84]: df
Out[84]:
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 [85]: pd.set_option("colheader_justify", "left")
In [86]: df
Out[86]:
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 [87]: pd.reset_option("colheader_justify")
数字格式设置#
Pandas还允许您设置数字在控制台中的显示方式。此选项不是通过 set_options 原料药。
使用 set_eng_float_format 函数更改Pandas对象的浮点格式以生成特定格式。
In [88]: import numpy as np
In [89]: pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)
In [90]: s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])
In [91]: s / 1.0e3
Out[91]:
a 303.638u
b -721.084u
c -622.696u
d 648.250u
e -1.945m
dtype: float64
In [92]: s / 1.0e6
Out[92]:
a 303.638n
b -721.084n
c -622.696n
d 648.250n
e -1.945u
dtype: float64
Unicode格式设置#
警告
启用此选项将影响DataFrame和系列的打印性能(大约慢2倍)。仅在实际需要时使用。
一些东亚国家/地区使用Unicode字符，其宽度对应于两个拉丁字符。如果DataFrame或Series包含这些字符，则默认输出模式可能无法正确对齐它们。
In [93]: df = pd.DataFrame({"国籍": ["UK", "日本"], "名前": ["Alice", "しのぶ"]})
In [94]: df
Out[94]:
国籍 名前
0 UK Alice
1 日本 しのぶ
正在启用 display.unicode.east_asian_width 允许Pandas检查每个字符的“东亚宽度”属性。通过将此选项设置为，可以正确对齐这些字符 True 。但是，这将导致渲染时间比标准时间更长 len 功能。
In [95]: pd.set_option("display.unicode.east_asian_width", True)
In [96]: df
Out[96]:
国籍 名前
0 UK Alice
1 日本 しのぶ
此外，宽度不明确的Unicode字符可以是1个或2个字符宽，具体取决于终端设置或编码。该选项 display.unicode.ambiguous_as_wide 可以用来处理歧义。
默认情况下，“不明确”字符的宽度，如下例中的“”(倒置感叹号)，被认为是1。
In [97]: df = pd.DataFrame({"a": ["xxx", "¡¡"], "b": ["yyy", "¡¡"]})
In [98]: df
Out[98]:
a b
0 xxx yyy
1 ¡¡ ¡¡
正在启用 display.unicode.ambiguous_as_wide 使Pandas将这些字符的宽度解释为2。(请注意，此选项仅在以下情况下有效 display.unicode.east_asian_width 已启用。)
但是，为您的终端错误设置此选项将导致这些字符错误对齐：
In [99]: pd.set_option("display.unicode.ambiguous_as_wide", True)
In [100]: df
Out[100]:
a b
0 xxx yyy
1 ¡¡ ¡¡
表架构显示#
DataFrame 和 Series 默认情况下将发布表架构表示形式。可以在全局范围内使用 display.html.table_schema 选项：
In [101]: pd.set_option("display.html.table_schema", True)
仅限 'display.max_rows' 都被序列化并发布。