选项和设置#
概述#
pandas 有一个选项 API，用于配置和自定义与 DataFrame 显示、数据行为等相关的全局行为。
选项有一个完整的“点式”、不区分大小写的名称（例如 display.max_rows）。你可以直接将选项作为顶级 options 属性的属性进行获取/设置：
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
API 由 5 个相关函数组成，可以直接从 pandas 命名空间中获取：
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默认值。describe_option()- 打印一个或多个选项的描述。option_context()- 使用一组选项执行代码块，这些选项在执行后恢复到之前的设置。
备注
开发者可以查看 pandas/core/config_init.py 获取更多信息。
上述所有函数都接受一个正则表达式模式（re.search 风格）作为参数，以匹配一个明确的子字符串：
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 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)
Cell In[10], line 1
----> 1 pd.get_option("max")
File /home/pandas/pandas/_config/config.py:176, in get_option(pat)
136 def get_option(pat: str) -> Any:
137 """
138 Retrieve the value of the specified option.
139
(...)
174 4
175 """
--> 176 key = _get_single_key(pat)
178 # walk the nested dict
179 root, k = _get_root(key)
File /home/pandas/pandas/_config/config.py:126, in _get_single_key(pat)
124 raise OptionError(f"No such keys(s): {pat!r}")
125 if len(keys) > 1:
--> 126 raise OptionError("Pattern matched multiple keys")
127 key = keys[0]
129 _warn_if_deprecated(key)
OptionError: Pattern matched multiple keys
警告
使用这种简写形式可能会导致您的代码在将来版本中添加类似名称的新选项时中断。
可用选项#
你可以通过 describe_option() 获取可用选项及其描述的列表。当不带参数调用 describe_option() 时，它会打印出所有可用选项的描述。
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 than 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.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, or switch to the view from
df.info() (the behaviour in earlier versions of pandas).
[default: truncate] [currently: truncate]
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 or None 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 and defaults to 20.
[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
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]
future.infer_string Whether to infer sequence of str objects as pyarrow string dtype, which will be the default in pandas 3.0 (at which point this option will be deprecated).
[default: False] [currently: False]
future.no_silent_downcasting Whether to opt-in to the future behavior which will *not* silently downcast results from Series and DataFrame `where`, `mask`, and `clip` methods. Silent downcasting will be removed in pandas 3.0 (at which point this option will be deprecated).
[default: False] [currently: False]
io.excel.ods.reader : string
The default Excel reader engine for 'ods' files. Available options:
auto, odf, calamine.
[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, calamine.
[default: auto] [currently: auto]
io.excel.xlsb.reader : string
The default Excel reader engine for 'xlsb' files. Available options:
auto, pyxlsb, calamine.
[default: auto] [currently: auto]
io.excel.xlsm.reader : string
The default Excel reader engine for 'xlsm' files. Available options:
auto, xlrd, openpyxl, calamine.
[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, calamine.
[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.copy_on_write : bool
Use new copy-view behaviour using Copy-on-Write. Defaults to False,
unless overridden by the 'PANDAS_COPY_ON_WRITE' environment variable
(if set to "1" for True, needs to be set before pandas is imported).
[default: False] [currently: False]
mode.performance_warnings : boolean
Whether to show or hide PerformanceWarnings.
[default: True] [currently: True]
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: auto] [currently: auto]
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
satisfy ``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
satisfy ``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() 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
选项 'mode.sim_interactive' 主要用于调试目的。
你可以使用 reset_option() 来恢复设置的默认值
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
也可以一次性重置多个选项（使用正则表达式）：
In [20]: pd.reset_option("^display")
option_context() 上下文管理器已通过顶层 API 暴露，允许您使用给定的选项值执行代码。选项值在退出 with 块时会自动恢复：
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 Python/IPython 环境的启动脚本来导入 pandas 并设置选项可以使使用 pandas 更加高效。为此，在所需配置文件的启动目录中创建一个 .py 或 .ipy 脚本。启动文件夹在默认 IPython 配置文件中的示例可以在以下位置找到：
$IPYTHONDIR/profile_default/startup
更多信息可以在 IPython 文档 中找到。下面显示了一个用于 pandas 的启动脚本示例：
import pandas as pd
pd.set_option("display.max_rows", 999)
pd.set_option("display.precision", 5)
常用选项#
以下是演示更常用的显示选项。
display.max_rows 和 display.max_columns 设置了一个帧在漂亮打印时显示的最大行数和列数。被截断的行用省略号替换。
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")
一旦超过 display.max_rows，display.min_rows 选项决定了在截断的 repr 中显示多少行。
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 DataFrame 的表示跨页显示，所有列都换行。
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 显示一个超过 max_columns 或 max_rows 的 DataFrame 作为截断的帧或摘要。
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.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。长度为此值或更长的单元格将以省略号截断。
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 设置了一个阈值，用于在调用 info() 时显示的列数。
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.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.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() 通常会显示每一列的空值计数。对于一个大的 DataFrame，这可能会非常慢。max_info_rows 和 max_info_cols 分别限制这个空值检查到指定的行和列。info() 关键字参数 show_counts=True 将覆盖这个设置。
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.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.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 设置输出显示的精度，以小数位数表示。
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 在显示 Series 或 DataFrame 时设置舍入阈值为零。此设置不会改变数字存储的精度。
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 控制标题的对齐方式。选项有 'right' 和 '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")
数字格式化#
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 API 设置的。
使用 set_eng_float_format 函数来改变 pandas 对象的浮点数格式，以生成特定的格式。
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 格式化#
警告
启用此选项将影响 DataFrame 和 Series 的打印性能（大约慢 2 倍）。仅在实际需要时使用。
一些东亚国家使用宽度对应两个拉丁字符的Unicode字符。如果一个DataFrame或Series包含这些字符，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
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 True，这些字符可以正确对齐。然而，这将导致比标准 len 函数更长的渲染时间。
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，宽度为“模糊”的 Unicode 字符在不同的终端设置或编码下可以是 1 或 2 个字符宽。选项 display.unicode.ambiguous_as_wide 可以用来处理这种模糊性。
默认情况下，一个“模糊”字符的宽度，例如下面例子中的“¡”（倒置的感叹号），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 ¡¡ ¡¡
启用 display.unicode.ambiguous_as_wide 使 pandas 将这些字符的宽度解释为 2。（请注意，此选项仅在启用 display.unicode.east_asian_width 时有效。）
然而，为您的终端错误地设置此选项将导致这些字符对齐不正确：
In [99]: pd.set_option("display.unicode.ambiguous_as_wide", True)
In [100]: df
Out[100]:
a b
0 xxx yyy
1 ¡¡ ¡¡
表格模式显示#
DataFrame 和 Series 默认会发布一个 Table Schema 表示。这可以通过 display.html.table_schema 选项全局启用：
In [101]: pd.set_option("display.html.table_schema", True)
只有 'display.max_rows' 被序列化并发布。