选项和设置# 概述＃ pandas 有一个选项 API 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）。您可以直接获取/设置选项作为顶级属性的属性options： 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 API 由 5 个相关函数组成，可直接从pandas 命名空间获取： 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先前的设置。 笔记 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正则表达式模式（re.search样式）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 以下内容将不起作用，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 ~/work/pandas/pandas/pandas/_config/config.py:274, in CallableDynamicDoc.__call__(self, *args, **kwds) 273 def __call__(self, *args, **kwds) -> T: --> 274 return self.__func__(*args, **kwds) File ~/work/pandas/pandas/pandas/_config/config.py:146, in _get_option(pat, silent) 145 def _get_option(pat: str, silent: bool = False) -> Any: --> 146 key = _get_single_key(pat, silent) 148 # walk the nested dict 149 root, k = _get_root(key) File ~/work/pandas/pandas/pandas/_config/config.py:134, in _get_single_key(pat, silent) 132 raise OptionError(f"No such keys(s): {repr(pat)}") 133 if len(keys) > 1: --> 134 raise OptionError("Pattern matched multiple keys") 135 key = keys[0] 137 if not silent: OptionError: Pattern matched multiple keys 警告 如果在未来版本中添加具有相似名称的新选项，使用这种形式的速记可能会导致您的代码中断。 可用选项＃ 您可以使用 获取可用选项及其描述的列表describe_option()。当不带参数调用时，describe_option()将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.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] (Deprecated, use `` instead.) 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. This option is ignored if ``future.infer_string`` is set to True. [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). This option is deprecated in pandas 2.1.0 and will be removed in 3.0. [default: False] [currently: False] (Deprecated, use `` 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 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 命名空间获得。要更改选项，请致电 。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 笔记 该选项'mode.sim_interactive'主要用于调试目的。 您可以用来reset_option()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 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()上下文管理器已通过顶级 API 公开，允许您使用给定的选项值执行代码。当您退出块时，选项值会自动恢复with： 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 环境的启动脚本导入 pandas 并设置选项可以使 pandas 的使用更加高效。为此，请在所需配置文件的启动目录中创建一个.py或脚本。.ipy启动文件夹位于默认 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。 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# 以下是较常用的显示选项的演示。 display.max_rows并display.max_columns设置当框架被漂亮打印时显示的最大行数和列数。截断的行由省略号代替。 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超过，display.min_rows选项将确定在截断的 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 a 的表示形式 DataFrame跨页延伸，覆盖所有列。 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 ... 7 8 9 0 -0.006154 -0.923061 0.895717 ... 1.340309 -1.170299 -0.226169 1 0.410835 0.813850 0.132003 ... -1.436737 -1.413681 1.607920 2 1.024180 0.569605 0.875906 ... -0.078638 0.545952 -1.219217 3 -1.226825 0.769804 -1.281247 ... 0.341734 0.959726 -1.110336 4 -0.619976 0.149748 -0.732339 ... 0.301624 -2.179861 -1.369849 [5 rows x 10 columns] 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DataFrame超出 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 ... 7 8 9 0 -0.954208 1.462696 -1.743161 ... 0.995761 2.396780 0.014871 1 3.357427 -0.317441 -1.236269 ... 0.380396 0.084844 0.432390 .. ... ... ... ... ... ... ... 8 -0.303421 -0.858447 0.306996 ... 0.476720 0.473424 -0.242861 9 -0.014805 -0.284319 0.650776 ... 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被省略号截断。 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设置调用时显示的列数的阈值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。对于大型 来说DataFrame，这可能会相当慢。max_info_rows并将max_info_cols 此空检查分别限制为指定的行和列。关键字info() 参数show_counts=True将覆盖它。 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设置以小数位数表示的输出显示精度。 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_thresholdSeries显示或时将舍入阈值设置为零 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# pandas 还允许您设置数字在控制台中的显示方式。该选项不是通过 API 设置的set_options。 使用该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 用于round()专门控制个体的舍入DataFrame 统一码格式# 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/地区使用宽度相当于两个拉丁字符的 Unicode 字符。如果 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 可以正确对齐这些字符True。但是，这将导致渲染时间比标准len函数更长。 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可用于处理歧义。 默认情况下，“不明确”字符的宽度（例如下例中的“¡”（倒转感叹号））被视为 1。 In [97]: df = pd.DataFrame({"a": ["xxx", "¡¡"], "b": ["yyy", "¡¡"]}) In [98]: df Out[98]: a b 0 xxx yyy 1 ¡¡ ¡¡ 启用后， 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.ambiguous_as_wide时才有效。）display.unicode.east_asian_width 但是，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 ¡¡ ¡¡ 表模式显示# DataFrame并将Series默认发布表架构表示。可以使用以下 display.html.table_schema选项全局启用此功能： In [101]: pd.set_option("display.html.table_schema", True) 仅'display.max_rows'连载并出版。