オプションと設定#
概要#
pandasは、DataFrameの表示、データの動作など、グローバルな動作を構成およびカスタマイズするためのオプションAPIを提供しています。
オプションは、大文字と小文字を区別しない完全な「ドットスタイル」の名前を持ちます（例：display.max_rows）。最上位のoptions属性の属性として、オプションを直接取得/設定できます。
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は、pandas名前空間から直接使用できる5つの関連関数で構成されています。
get_option()/set_option()- 単一のオプションの値を取得/設定します。reset_option()- 1つ以上のオプションをデフォルト値にリセットします。describe_option()- 1つ以上のオプションの説明を出力します。option_context()- オプションのセットを使用してコードブロックを実行し、実行後に以前の設定に戻します。
注記
開発者は、詳細についてはpandas/core/config_init.pyを参照してください。
上記のすべての関数は、曖昧でない部分文字列に一致する引数として正規表現パターン（re.searchスタイル）を受け入れます。
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
次の例は、display.max_colwidth、display.max_rows、display.max_columnsなど、複数のオプション名に一致するため、**機能しません**。
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
この簡略表記を使用すると、将来のバージョンで同様の名前の新しいオプションが追加された場合、コードが動作しなくなる可能性があります。
利用可能なオプション#
describe_option()を使用して、利用可能なオプションとその説明の一覧を取得できます。引数なしで呼び出された場合、describe_option()は、利用可能なすべてのオプションの説明を出力します。
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]
オプションの取得と設定#
上記のように、get_option()とset_option()はpandas名前空間から使用できます。オプションを変更するには、set_option('option regex', new_value)を呼び出します。
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
注記
オプション'mode.sim_interactive'は、主にデバッグ目的で使用されます。
reset_option()を使用して、設定をデフォルト値に戻すことができます。
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
正規表現を使用して、複数のオプションを一度にリセットすることもできます。
In [20]: pd.reset_option("^display")
option_context()コンテキストマネージャーは最上位APIを介して公開されており、指定されたオプション値を使用してコードを実行できます。withブロックを終了すると、オプション値は自動的に復元されます。
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
Python/IPython環境での起動オプションの設定#
Python/IPython環境の起動スクリプトを使用してpandasをインポートし、オプションを設定すると、pandasの作業効率が向上します。これを行うには、目的のプロファイルの起動ディレクトリに.pyまたは.ipyスクリプトを作成します。起動フォルダーがデフォルトのIPythonプロファイルにある例を次に示します。
$IPYTHONDIR/profile_default/startup
詳細については、IPythonドキュメントを参照してください。pandasの起動スクリプトの例を以下に示します。
import pandas as pd
pd.set_option("display.max_rows", 999)
pd.set_option("display.precision", 5)
頻繁に使用されるオプション#
以下は、より頻繁に使用される表示オプションを示しています。
display.max_rowsとdisplay.max_columnsは、フレームが綺麗に表示される際に表示される最大行数と列数を設定します。切り詰められた行は省略記号で置き換えられます。
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")
display.max_rowsを超えると、display.min_rowsオプションによって、切り詰められたreprに表示される行数が決定されます。
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を使用すると、DataFrameの表現をページ全体に広げ、すべての列にわたって折り返すことができます。
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は、max_columnsまたはmax_rowsを超えるDataFrameを切り詰められたフレームまたはサマリーとして表示します。
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は、列の最大幅を設定します。この長さ以上のセルは、省略記号で切り詰められます。
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は、info()を呼び出すときに表示される列数のしきい値を設定します。
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()は通常、各列のnullカウントを表示します。大規模なDataFrameの場合、これは非常に遅くなる可能性があります。max_info_rowsとmax_info_colsは、このnullチェックを指定された行と列に制限します。info()のキーワード引数show_counts=Trueはこれを上書きします。
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は、小数点以下の桁数で出力表示精度を設定します。
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は、SeriesまたはDataFrameを表示するときのゼロへの丸めしきい値を設定します。この設定は、数値が保存される精度を変更しません。
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 はヘッダーの揃え方を制御します。オプションは'right'と'left'です。
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")
数値の書式設定#
pandasでは、コンソールに表示される数値の形式も設定できます。このオプションはset_options APIでは設定できません。
set_eng_float_format 関数を使用して、pandasオブジェクトの浮動小数点数の書式を変更し、特定の形式を作成します。
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の書式設定#
警告
このオプションを有効にすると、DataFrameとSeriesの表示パフォーマンスに影響します（約2倍遅くなります）。実際に必要な場合のみ使用してください。
東アジアの一部の国では、幅がラテン文字2文字分に相当するUnicode文字を使用しています。DataFrameまたはSeriesにこれらの文字が含まれている場合、デフォルトの出力モードでは適切に揃えられない可能性があります。
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
display.unicode.east_asian_widthを有効にすると、pandasは各文字の「East Asian Width」プロパティをチェックできます。このオプションをTrueに設定することで、これらの文字を適切に揃えることができます。ただし、標準のlen関数よりもレンダリング時間が長くなります。
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
さらに、幅が「ambiguous」（曖昧）なUnicode文字は、ターミナルの設定やエンコーディングによっては、幅が1文字または2文字になります。display.unicode.ambiguous_as_wideオプションを使用して、この曖昧さを処理できます。
デフォルトでは、「曖昧」な文字の幅（下の例にある「¡」（反転感嘆符）など）は1とみなされます。
In [97]: df = pd.DataFrame({"a": ["xxx", "¡¡"], "b": ["yyy", "¡¡"]})
In [98]: df
Out[98]:
a b
0 xxx yyy
1 ¡¡ ¡¡
display.unicode.ambiguous_as_wideを有効にすると、pandasはこれらの文字の幅を2と解釈します。（このオプションは、display.unicode.east_asian_widthが有効になっている場合のみ有効です。）
ただし、ターミナルに対してこのオプションを誤って設定すると、これらの文字が正しく揃えられなくなります。
In [99]: pd.set_option("display.unicode.ambiguous_as_wide", True)
In [100]: df
Out[100]:
a b
0 xxx yyy
1 ¡¡ ¡¡
テーブルスキーマの表示#
DataFrameとSeriesは、デフォルトでTable Schema表現を公開します。これは、display.html.table_schemaオプションでグローバルに有効にできます。
In [101]: pd.set_option("display.html.table_schema", True)
'display.max_rows'のみがシリアル化され、公開されます。