标签归档:column-width

如何扩展输出显示以查看pandas DataFrame的更多列?

问题:如何扩展输出显示以查看pandas DataFrame的更多列?

有没有办法在交互式或脚本执行模式下扩大输出的显示?

具体来说,我describe()在pandas上使用该功能DataFrame。当DataFrame5列(标签)宽时,我得到了所需的描述性统计信息。但是,如果DataFrame具有更多列,则统计信息将被抑制,并返回如下所示的内容:

>> Index: 8 entries, count to max  
>> Data columns:  
>> x1          8  non-null values  
>> x2          8  non-null values  
>> x3          8  non-null values  
>> x4          8  non-null values  
>> x5          8  non-null values  
>> x6          8  non-null values  
>> x7          8  non-null values  

无论是6列还是7列,都会给出“ 8”值。“ 8”是什么意思?

我已经尝试过将IDLE窗口拖动更大,并增加“ Configure IDLE”宽度选项,但无济于事。

我使用熊猫的目的describe()是避免使用诸如Stata之类的第二个程序来进行基本的数据操作和调查。

Is there a way to widen the display of output in either interactive or script-execution mode?

Specifically, I am using the describe() function on a pandas DataFrame. When the DataFrame is 5 columns (labels) wide, I get the descriptive statistics that I want. However, if the DataFrame has any more columns, the statistics are suppressed and something like this is returned:

>> Index: 8 entries, count to max  
>> Data columns:  
>> x1          8  non-null values  
>> x2          8  non-null values  
>> x3          8  non-null values  
>> x4          8  non-null values  
>> x5          8  non-null values  
>> x6          8  non-null values  
>> x7          8  non-null values  

The “8” value is given whether there are 6 or 7 columns. What does the “8” refer to?

I have already tried dragging the IDLE window larger, as well as increasing the “Configure IDLE” width options, to no avail.

My purpose in using pandas and describe() is to avoid using a second program like Stata to do basic data manipulation and investigation.


回答 0

更新:熊猫0.23.4起

这不是必须的,如果设置,pandas会自动检测终端窗口的大小pd.options.display.width = 0。(有关较旧的版本,请参阅底部。)

pandas.set_printoptions(...)不推荐使用。而是使用pandas.set_option(optname, val)或等效地pd.options.<opt.hierarchical.name> = val。喜欢:

import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

这是帮助set_option

set_option(pat,value)-设置指定选项的值

可用选项:
显示。[chop_threshold,colheader_justify,column_space,date_dayfirst,
         date_yearfirst,编码,expand_frame_repr,float_format,高度,
         line_width,max_columns,max_colwidth,max_info_columns,max_info_rows,
         max_rows,max_seq_items,mpl_style,multi_sparse,notebook_repr_html,
         pprint_nest_depth,精度,宽度]
模式。[sim_interactive,use_inf_as_null]

参量
----------
pat-str / regexp,应与单个选项匹配。

注意:为方便起见,支持部分匹配,但除非您使用
完整的选项名称(egxyzoption_name),将来您的代码可能会中断
版本,如果引入了具有相似名称的新选项。

value-期权的新价值。

退货
-------
没有

加薪
------
如果没有这样的选项,则为KeyError

display.chop_threshold:[默认:无] [当前:无]
:浮动或无
        如果设置为浮点值,则所有浮点值均小于给定阈值
        将由repr和朋友显示为正好为0。
display.colheader_justify:[默认:正确] [当前:正确]
: '左右'
        控制列标题的对正。由DataFrameFormatter使用。
display.column_space:[默认值:12] [当前:12]无可用描述。

display.date_dayfirst:[默认:False] [当前:False]
:布尔值
        如果为True,则打印和解析日期的日期为第一天,例如20/01/2005
display.date_yearfirst:[默认:False] [当前:False]
:布尔值
        如果为True,则打印和解析日期以年份为第一,例如2005/01/20
display.encoding:[默认:UTF-8] [当前:UTF-8]
:str / unicode
        默认为检测到的控制台编码。
        指定用于to_string返回的字符串的编码,
        这些通常是要在控制台上显示的字符串。
display.expand_frame_repr:[默认:True] [当前:True]
:布尔值
        是否为宽数据框打印完整的数据框代表
        跨多行,`max_columns`仍然受到尊重,但是输出将
        如果宽度超过“ display.width”,则跨多个“页面”进行环绕。
display.float_format:[默认:无] [当前:无]
:可调用
        可调用对象应接受浮点数并返回
        具有所需数字格式的字符串。这用
        在某些地方,例如SeriesFormatter。
        有关示例,请参见core.format.EngFormatter。
display.height:[默认值:60] [当前:1000]
:int
        不推荐使用。
        (已弃用,请改用display.height。)

display.line_width:[默认值:80] [当前:1000]
:int
        不推荐使用。
        (已弃用,请改用display.width。)

display.max_columns:[默认:20] [当前:500]
:int
        在__repr __()方法中使用max_rows和max_columns来确定是否
        to_string()或info()用于将对象呈现为字符串。如果
        python / IPython在终端中运行,可以将其设置为0和pandas
        将正确地自动检测终端的宽度并交换为较小的宽度
        格式,以防所有列都不能垂直放置。IPython笔记本,
        IPython qtconsole或IDLE不在终端中运行,因此它不是
        可以进行正确的自动检测。
        “无”值意味着无限。
display.max_colwidth:[默认:50] [当前:50]
:int
        列的最大宽度(以字符为单位)
        大熊猫数据结构。当列溢出时,会出现一个“ ...”
        占位符嵌入在输出中。
display.max_info_columns:[默认:100] [当前:100]
:int
        在DataFrame.info方法中使用max_info_columns来确定是否
        每列信息将被打印。
display.max_info_rows:[默认:1690785] [当前:1690785]
:int或无
        max_info_rows是一帧将要进行的最大行数
        重新进入控制台时,对其列执行null检查。
        默认值为1,000,000行。因此,如果DataFrame具有更多
        1,000,000行将不会对
        列,因此表示将花费更少的时间
        在互动会话中显示。值None表示总是
        重复时执行空检查。
display.max_rows:[默认:60] [当前:500]
:int
        设置打印时熊猫应输出的最大行数
        各种输出。例如,此值确定是否repr()
        数据框完全打印出来或只是摘要表示。
        “无”值意味着无限。
display.max_seq_items:[默认:无] [当前:无]
:int或无

        漂亮地打印长序列时,不超过`max_seq_items`
        将被打印。如果省略项目,将用加法表示
        “ ...”到结果字符串。

        如果设置为“无”,则要打印的项目数不受限制。
display.mpl_style:[默认:无] [当前:无]
:布尔

        将此设置为“默认”将修改matplotlib使用的rcParams
        默认情况下,为绘图提供更令人愉悦的视觉样式。
        将此设置为None / False会将值恢复为其初始值。
display.multi_sparse:[默认:True] [当前:True]
:布尔值
        “ sparsify” MultiIndex显示(不重复显示
        组内外层的元素)
display.notebook_repr_html:[默认:True] [当前:True]
:布尔值
        如果为True,则IPython Notebook将使用html表示形式
        熊猫对象(如果有)。
display.pprint_nest_depth:[默认值:3] [当前:3]
:int
        控制漂亮打印时要处理的嵌套层数
display.precision:[默认:7] [当前:7]
:int
        浮点输出精度(有效位数)。这是
        只是一个建议
display.width:[默认值:80] [当前:1000]
:int
        显示的宽度(以字符为单位)。如果python / IPython运行在
        可以将其设置为“无”的终端,熊猫会正确自动检测
        宽度。
        请注意,IPython笔记本,IPython qtconsole或IDLE不会在
        终端,因此无法正确检测宽度。
mode.sim_interactive:[默认:False] [当前:False]
:布尔值
        是否为了测试目的而模拟交互模式
mode.use_inf_as_null:[默认:False] [当前:False]
:布尔值
        True表示将None,NaN,INF,-INF视为null(旧方法),
        False表示None和NaN为空,但INF,-INF不为空
        (新方法)。
呼叫def:pd.set_option(self,* args,** kwds)

编辑:较旧的版本信息,其中许多已被弃用。

如@bmu 所述,pandas自动检测(默认情况下)显示区域的大小,当对象代表不适合显示时,将使用摘要视图。您提到了调整“ IDLE”窗口的大小,但没有任何效果。如果可以print df.describe().to_string(),它是否适合于“ IDLE”窗口?

终端大小由pandas.util.terminal.get_terminal_size()(已弃用和移除)确定,这将返回一个包含(width, height)显示内容的元组。输出是否与您的IDLE窗口的大小匹配?可能存在问题(在emacs中运行终端之前有一个问题)。

请注意,可以绕过自动检测,pandas.set_printoptions(max_rows=200, max_columns=10)如果行数,列数不超过给定的限制,则永远不会切换到摘要视图。


“ max_colwidth”选项有助于查看每列的截断形式。

Update: Pandas 0.23.4 onwards

This is not necessary, pandas autodetects the size of your terminal window if you set pd.options.display.width = 0. (For older versions see at bottom.)

pandas.set_printoptions(...) is deprecated. Instead, use pandas.set_option(optname, val), or equivalently pd.options.<opt.hierarchical.name> = val. Like:

import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

Here is the help for set_option:

set_option(pat,value) - Sets the value of the specified option

Available options:
display.[chop_threshold, colheader_justify, column_space, date_dayfirst,
         date_yearfirst, encoding, expand_frame_repr, float_format, height,
         line_width, max_columns, max_colwidth, max_info_columns, max_info_rows,
         max_rows, max_seq_items, mpl_style, multi_sparse, notebook_repr_html,
         pprint_nest_depth, precision, width]
mode.[sim_interactive, use_inf_as_null]

Parameters
----------
pat - str/regexp which should match a single option.

Note: partial matches are supported for convenience, but unless you use the
full option name (e.g. x.y.z.option_name), your code may break in future
versions if new options with similar names are introduced.

value - new value of option.

Returns
-------
None

Raises
------
KeyError if no such option exists

display.chop_threshold: [default: None] [currently: None]
: 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.
display.colheader_justify: [default: right] [currently: right]
: 'left'/'right'
        Controls the justification of column headers. used by DataFrameFormatter.
display.column_space: [default: 12] [currently: 12]No description available.

display.date_dayfirst: [default: False] [currently: False]
: boolean
        When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst: [default: False] [currently: False]
: boolean
        When True, prints and parses dates with the year first, eg 2005/01/20
display.encoding: [default: UTF-8] [currently: UTF-8]
: 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.
display.expand_frame_repr: [default: True] [currently: True]
: 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 it's width exceeds `display.width`.
display.float_format: [default: None] [currently: None]
: 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 core.format.EngFormatter for an example.
display.height: [default: 60] [currently: 1000]
: int
        Deprecated.
        (Deprecated, use `display.height` instead.)

display.line_width: [default: 80] [currently: 1000]
: int
        Deprecated.
        (Deprecated, use `display.width` instead.)

display.max_columns: [default: 20] [currently: 500]
: int
        max_rows and max_columns are used in __repr__() methods to decide if
        to_string() or info() is used to render an object to a string.  In case
        python/IPython is running in a terminal this can be set to 0 and pandas
        will correctly auto-detect the width the terminal and swap to a smaller
        format in case all columns would not fit vertically. The IPython notebook,
        IPython qtconsole, or IDLE do not run in a terminal and hence it is not
        possible to do correct auto-detection.
        'None' value means unlimited.
display.max_colwidth: [default: 50] [currently: 50]
: int
        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.
display.max_info_columns: [default: 100] [currently: 100]
: int
        max_info_columns is used in DataFrame.info method to decide if
        per column information will be printed.
display.max_info_rows: [default: 1690785] [currently: 1690785]
: int or None
        max_info_rows is the maximum number of rows for which a frame will
        perform a null check on its columns when repr'ing To a console.
        The default is 1,000,000 rows. So, if a DataFrame has more
        1,000,000 rows there will be no null check performed on the
        columns and thus the representation will take much less time to
        display in an interactive session. A value of None means always
        perform a null check when repr'ing.
display.max_rows: [default: 60] [currently: 500]
: int
        This sets the maximum number of rows pandas should output when printing
        out various output. For example, this value determines whether the repr()
        for a dataframe prints out fully or just a summary repr.
        'None' value means unlimited.
display.max_seq_items: [default: None] [currently: None]
: int or None

        when pretty-printing a long sequence, no more then `max_seq_items`
        will be printed. If items are ommitted, they will be denoted by the addition
        of "..." to the resulting string.

        If set to None, the number of items to be printed is unlimited.
display.mpl_style: [default: None] [currently: None]
: bool

        Setting this to 'default' will modify the rcParams used by matplotlib
        to give plots a more pleasing visual style by default.
        Setting this to None/False restores the values to their initial value.
display.multi_sparse: [default: True] [currently: True]
: boolean
        "sparsify" MultiIndex display (don't display repeated
        elements in outer levels within groups)
display.notebook_repr_html: [default: True] [currently: True]
: boolean
        When True, IPython notebook will use html representation for
        pandas objects (if it is available).
display.pprint_nest_depth: [default: 3] [currently: 3]
: int
        Controls the number of nested levels to process when pretty-printing
display.precision: [default: 7] [currently: 7]
: int
        Floating point output precision (number of significant digits). This is
        only a suggestion
display.width: [default: 80] [currently: 1000]
: 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.
mode.sim_interactive: [default: False] [currently: False]
: boolean
        Whether to simulate interactive mode for purposes of testing
mode.use_inf_as_null: [default: False] [currently: False]
: boolean
        True means treat None, NaN, INF, -INF as null (old way),
        False means None and NaN are null, but INF, -INF are not null
        (new way).
Call def:   pd.set_option(self, *args, **kwds)

EDIT: older version information, much of this has been deprecated.

As @bmu mentioned, pandas auto detects (by default) the size of the display area, a summary view will be used when an object repr does not fit on the display. You mentioned resizing the IDLE window, to no effect. If you do print df.describe().to_string() does it fit on the IDLE window?

The terminal size is determined by pandas.util.terminal.get_terminal_size() (deprecated and removed), this returns a tuple containing the (width, height) of the display. Does the output match the size of your IDLE window? There might be an issue (there was one before when running a terminal in emacs).

Note that it is possible to bypass the autodetect, pandas.set_printoptions(max_rows=200, max_columns=10) will never switch to summary view if number of rows, columns does not exceed the given limits.


The ‘max_colwidth’ option helps in seeing untruncated form of each column.


回答 1

尝试这个:

pd.set_option('display.expand_frame_repr', False)

从文档中:

display.expand_frame_repr:布尔值

是否跨多行打印宽数据帧的完整DataFrame repr,仍会考虑max_columns,但是如果宽度超过display.width,则输出将在多个“页面”中回绕。[默认:真] [当前:真]

请参阅:http : //pandas.pydata.org/pandas-docs/stable/generated/pandas.set_option.html

Try this:

pd.set_option('display.expand_frame_repr', False)

From the documentation:

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 it’s width exceeds display.width. [default: True] [currently: True]

See: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.set_option.html


回答 2

如果要临时设置选项以显示一个大的DataFrame,则可以使用option_context

with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print (df)

退出with块时,选项值将自动恢复。

If you want to set options temporarily to display one large DataFrame, you can use option_context:

with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print (df)

Option values are restored automatically when you exit the with block.


回答 3

仅使用以下3行对我有用:

pd.set_option('display.max_columns', None)  
pd.set_option('display.expand_frame_repr', False)
pd.set_option('max_colwidth', -1)

Anaconda / Python 3.6.5 /熊猫:0.23.0 / Visual Studio Code 1.26

Only using these 3 lines worked for me:

pd.set_option('display.max_columns', None)  
pd.set_option('display.expand_frame_repr', False)
pd.set_option('max_colwidth', -1)

Anaconda / Python 3.6.5 / pandas: 0.23.0 / Visual Studio Code 1.26


回答 4

使用以下方法设置列的最大宽度:

pd.set_option('max_colwidth', 800)

该特定语句将每列的最大宽度设置为800px。

Set column max width using:

pd.set_option('max_colwidth', 800)

This particular statement sets max width to 800px, per column.


回答 5

您可以使用print df.describe().to_string()它来强制显示整个表格。(您可以to_string()对任何DataFrame像这样使用。结果describe只是一个DataFrame本身。)

8是保存“描述”的DataFrame中的行数(因为describe计算8个统计信息,最小值,最大值,平均值等)。

You can use print df.describe().to_string() to force it to show the whole table. (You can use to_string() like this for any DataFrame. The result of describe is just a DataFrame itself.)

The 8 is the number of rows in the DataFrame holding the “description” (because describe computes 8 statistics, min, max, mean, etc.).


回答 6

您可以使用调整熊猫打印选项set_printoptions

In [3]: df.describe()
Out[3]: 
<class 'pandas.core.frame.DataFrame'>
Index: 8 entries, count to max
Data columns:
x1    8  non-null values
x2    8  non-null values
x3    8  non-null values
x4    8  non-null values
x5    8  non-null values
x6    8  non-null values
x7    8  non-null values
dtypes: float64(7)

In [4]: pd.set_printoptions(precision=2)

In [5]: df.describe()
Out[5]: 
            x1       x2       x3       x4       x5       x6       x7
count      8.0      8.0      8.0      8.0      8.0      8.0      8.0
mean   69024.5  69025.5  69026.5  69027.5  69028.5  69029.5  69030.5
std       17.1     17.1     17.1     17.1     17.1     17.1     17.1
min    69000.0  69001.0  69002.0  69003.0  69004.0  69005.0  69006.0
25%    69012.2  69013.2  69014.2  69015.2  69016.2  69017.2  69018.2
50%    69024.5  69025.5  69026.5  69027.5  69028.5  69029.5  69030.5
75%    69036.8  69037.8  69038.8  69039.8  69040.8  69041.8  69042.8
max    69049.0  69050.0  69051.0  69052.0  69053.0  69054.0  69055.0

但是,这并不是在所有情况下都可行,因为熊猫会检测到您的控制台宽度,并且仅to_string在输出适合控制台时才使用(请参阅的文档字符串set_printoptions)。在这种情况下,你可以显式调用to_string由作为回答BrenBarn

更新资料

对于0.10版,更改了宽数据帧的打印方式:

In [3]: df.describe()
Out[3]: 
                 x1            x2            x3            x4            x5  \
count      8.000000      8.000000      8.000000      8.000000      8.000000   
mean   59832.361578  27356.711336  49317.281222  51214.837838  51254.839690   
std    22600.723536  26867.192716  28071.737509  21012.422793  33831.515761   
min    31906.695474   1648.359160     56.378115  16278.322271     43.745574   
25%    45264.625201  12799.540572  41429.628749  40374.273582  29789.643875   
50%    56340.214856  18666.456293  51995.661512  54894.562656  47667.684422   
75%    75587.003417  31375.610322  61069.190523  67811.893435  76014.884048   
max    98136.474782  84544.484627  91743.983895  75154.587156  99012.695717   

                 x6            x7  
count      8.000000      8.000000  
mean   41863.000717  33950.235126  
std    38709.468281  29075.745673  
min     3590.990740   1833.464154  
25%    15145.759625   6879.523949  
50%    22139.243042  33706.029946  
75%    72038.983496  51449.893980  
max    98601.190488  83309.051963  

进一步更改了用于设置熊猫选项的API:

In [4]: pd.set_option('display.precision', 2)

In [5]: df.describe()
Out[5]: 
            x1       x2       x3       x4       x5       x6       x7
count      8.0      8.0      8.0      8.0      8.0      8.0      8.0
mean   59832.4  27356.7  49317.3  51214.8  51254.8  41863.0  33950.2
std    22600.7  26867.2  28071.7  21012.4  33831.5  38709.5  29075.7
min    31906.7   1648.4     56.4  16278.3     43.7   3591.0   1833.5
25%    45264.6  12799.5  41429.6  40374.3  29789.6  15145.8   6879.5
50%    56340.2  18666.5  51995.7  54894.6  47667.7  22139.2  33706.0
75%    75587.0  31375.6  61069.2  67811.9  76014.9  72039.0  51449.9
max    98136.5  84544.5  91744.0  75154.6  99012.7  98601.2  83309.1

You can adjust pandas print options with set_printoptions.

In [3]: df.describe()
Out[3]: 
<class 'pandas.core.frame.DataFrame'>
Index: 8 entries, count to max
Data columns:
x1    8  non-null values
x2    8  non-null values
x3    8  non-null values
x4    8  non-null values
x5    8  non-null values
x6    8  non-null values
x7    8  non-null values
dtypes: float64(7)

In [4]: pd.set_printoptions(precision=2)

In [5]: df.describe()
Out[5]: 
            x1       x2       x3       x4       x5       x6       x7
count      8.0      8.0      8.0      8.0      8.0      8.0      8.0
mean   69024.5  69025.5  69026.5  69027.5  69028.5  69029.5  69030.5
std       17.1     17.1     17.1     17.1     17.1     17.1     17.1
min    69000.0  69001.0  69002.0  69003.0  69004.0  69005.0  69006.0
25%    69012.2  69013.2  69014.2  69015.2  69016.2  69017.2  69018.2
50%    69024.5  69025.5  69026.5  69027.5  69028.5  69029.5  69030.5
75%    69036.8  69037.8  69038.8  69039.8  69040.8  69041.8  69042.8
max    69049.0  69050.0  69051.0  69052.0  69053.0  69054.0  69055.0

However this will not work in all cases as pandas detects your console width and it will only use to_string if the output fits in the console (see the docstring of set_printoptions). In this case you can explicitly call to_string as answered by BrenBarn.

Update

With version 0.10 the way wide dataframes are printed changed:

In [3]: df.describe()
Out[3]: 
                 x1            x2            x3            x4            x5  \
count      8.000000      8.000000      8.000000      8.000000      8.000000   
mean   59832.361578  27356.711336  49317.281222  51214.837838  51254.839690   
std    22600.723536  26867.192716  28071.737509  21012.422793  33831.515761   
min    31906.695474   1648.359160     56.378115  16278.322271     43.745574   
25%    45264.625201  12799.540572  41429.628749  40374.273582  29789.643875   
50%    56340.214856  18666.456293  51995.661512  54894.562656  47667.684422   
75%    75587.003417  31375.610322  61069.190523  67811.893435  76014.884048   
max    98136.474782  84544.484627  91743.983895  75154.587156  99012.695717   

                 x6            x7  
count      8.000000      8.000000  
mean   41863.000717  33950.235126  
std    38709.468281  29075.745673  
min     3590.990740   1833.464154  
25%    15145.759625   6879.523949  
50%    22139.243042  33706.029946  
75%    72038.983496  51449.893980  
max    98601.190488  83309.051963  

Further more the API for setting pandas options changed:

In [4]: pd.set_option('display.precision', 2)

In [5]: df.describe()
Out[5]: 
            x1       x2       x3       x4       x5       x6       x7
count      8.0      8.0      8.0      8.0      8.0      8.0      8.0
mean   59832.4  27356.7  49317.3  51214.8  51254.8  41863.0  33950.2
std    22600.7  26867.2  28071.7  21012.4  33831.5  38709.5  29075.7
min    31906.7   1648.4     56.4  16278.3     43.7   3591.0   1833.5
25%    45264.6  12799.5  41429.6  40374.3  29789.6  15145.8   6879.5
50%    56340.2  18666.5  51995.7  54894.6  47667.7  22139.2  33706.0
75%    75587.0  31375.6  61069.2  67811.9  76014.9  72039.0  51449.9
max    98136.5  84544.5  91744.0  75154.6  99012.7  98601.2  83309.1

回答 7

您可以设置输出显示以匹配您当前的端子宽度:

pd.set_option('display.width', pd.util.terminal.get_terminal_size()[0])

You can set the output display to match your current terminal width:

pd.set_option('display.width', pd.util.terminal.get_terminal_size()[0])

回答 8

根据v0.18.0文档,如果您在终端机(即非iPython笔记本电脑,qtconsole或IDLE)上运行,则熊猫自动检测屏幕宽度并即时调整屏幕宽度是2线它显示的列:

pd.set_option('display.large_repr', 'truncate')
pd.set_option('display.max_columns', 0)

According to the docs for v0.18.0, if you’re running on a terminal (ie not iPython notebook, qtconsole or IDLE), it’s a 2-liner to have Pandas auto-detect your screen width and adapt on the fly with how many columns it shows:

pd.set_option('display.large_repr', 'truncate')
pd.set_option('display.max_columns', 0)

回答 9

似乎以上所有答案都可以解决问题。还有一点:pd.set_option('option_name')您可以使用(自动完成功能)代替

pd.options.display.width = None

请参阅熊猫文档:选项和设置:

选项具有完整的“点分样式”,不区分大小写的名称(例如 display.max_rows)。您可以直接将选项作为顶级属性的属性来获取/设置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

[…]

对于max_...参数:

max_rowsmax_columns在使用__repr__()的方法,以决定是否to_string()info()用于呈现的对象为字符串。如果python / IPython在终端中运行,则可以将其设置为0,并且pandas将正确地自动检测终端的宽度,并交换为较小的格式,以防所有列都不能垂直放置。IPython笔记本,IPython qtconsole或IDLE不在终端中运行,因此无法进行正确的自动检测。None”的值意味着无限。[重点不是原文]

对于width参数:

显示的宽度(以字符为单位)。如果python / IPython在终端中运行,则可以将其设置为,None而pandas将正确地自动检测宽度。请注意,IPython笔记本,IPython qtconsole或IDLE不在终端中运行,因此无法正确检测宽度。

It seems like all above answers solve the problem. One more point: instead of pd.set_option('option_name'), you can use the (auto-complete-able)

pd.options.display.width = None

See Pandas doc: Options and Settings:

Options have a full “dotted-style”, case-insensitive name (e.g. display.max_rows). You can get/set options directly as attributes of the top-level options attribute:

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

[…]

for the max_... params:

max_rows and max_columns are used in __repr__() methods to decide if to_string() or info() is used to render an object to a string. In case python/IPython is running in a terminal this can be set to 0 and pandas will correctly auto-detect the width the terminal and swap to a smaller format in case all columns would not fit vertically. The IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to do correct auto-detection. None’ value means unlimited. [emphasis not in original]

for the width param:

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.


回答 10

import pandas as pd
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 1000)

SentenceA = "William likes Piano and Piano likes William"
SentenceB = "Sara likes Guitar"
SentenceC = "Mamoosh likes Piano"
SentenceD = "William is a CS Student"
SentenceE = "Sara is kind"
SentenceF = "Mamoosh is kind"


bowA = SentenceA.split(" ")
bowB = SentenceB.split(" ")
bowC = SentenceC.split(" ")
bowD = SentenceD.split(" ")
bowE = SentenceE.split(" ")
bowF = SentenceF.split(" ")

# Creating a set consisted of all words

wordSet = set(bowA).union(set(bowB)).union(set(bowC)).union(set(bowD)).union(set(bowE)).union(set(bowF))
print("Set of all words is: ", wordSet)

# Initiating dictionary with 0 value for all BOWs

wordDictA = dict.fromkeys(wordSet, 0)
wordDictB = dict.fromkeys(wordSet, 0)
wordDictC = dict.fromkeys(wordSet, 0)
wordDictD = dict.fromkeys(wordSet, 0)
wordDictE = dict.fromkeys(wordSet, 0)
wordDictF = dict.fromkeys(wordSet, 0)

for word in bowA:
    wordDictA[word] += 1
for word in bowB:
    wordDictB[word] += 1
for word in bowC:
    wordDictC[word] += 1
for word in bowD:
    wordDictD[word] += 1
for word in bowE:
    wordDictE[word] += 1
for word in bowF:
    wordDictF[word] += 1

# Printing Term frequency

print("SentenceA TF: ", wordDictA)
print("SentenceB TF: ", wordDictB)
print("SentenceC TF: ", wordDictC)
print("SentenceD TF: ", wordDictD)
print("SentenceE TF: ", wordDictE)
print("SentenceF TF: ", wordDictF)

print(pd.DataFrame([wordDictA, wordDictB, wordDictB, wordDictC, wordDictD, wordDictE, wordDictF]))

输出:

   CS  Guitar  Mamoosh  Piano  Sara  Student  William  a  and  is  kind  likes
0   0       0        0      2     0        0        2  0    1   0     0      2
1   0       1        0      0     1        0        0  0    0   0     0      1
2   0       1        0      0     1        0        0  0    0   0     0      1
3   0       0        1      1     0        0        0  0    0   0     0      1
4   1       0        0      0     0        1        1  1    0   1     0      0
5   0       0        0      0     1        0        0  0    0   1     1      0
6   0       0        1      0     0        0        0  0    0   1     1      0
import pandas as pd
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 1000)

SentenceA = "William likes Piano and Piano likes William"
SentenceB = "Sara likes Guitar"
SentenceC = "Mamoosh likes Piano"
SentenceD = "William is a CS Student"
SentenceE = "Sara is kind"
SentenceF = "Mamoosh is kind"


bowA = SentenceA.split(" ")
bowB = SentenceB.split(" ")
bowC = SentenceC.split(" ")
bowD = SentenceD.split(" ")
bowE = SentenceE.split(" ")
bowF = SentenceF.split(" ")

# Creating a set consisted of all words

wordSet = set(bowA).union(set(bowB)).union(set(bowC)).union(set(bowD)).union(set(bowE)).union(set(bowF))
print("Set of all words is: ", wordSet)

# Initiating dictionary with 0 value for all BOWs

wordDictA = dict.fromkeys(wordSet, 0)
wordDictB = dict.fromkeys(wordSet, 0)
wordDictC = dict.fromkeys(wordSet, 0)
wordDictD = dict.fromkeys(wordSet, 0)
wordDictE = dict.fromkeys(wordSet, 0)
wordDictF = dict.fromkeys(wordSet, 0)

for word in bowA:
    wordDictA[word] += 1
for word in bowB:
    wordDictB[word] += 1
for word in bowC:
    wordDictC[word] += 1
for word in bowD:
    wordDictD[word] += 1
for word in bowE:
    wordDictE[word] += 1
for word in bowF:
    wordDictF[word] += 1

# Printing Term frequency

print("SentenceA TF: ", wordDictA)
print("SentenceB TF: ", wordDictB)
print("SentenceC TF: ", wordDictC)
print("SentenceD TF: ", wordDictD)
print("SentenceE TF: ", wordDictE)
print("SentenceF TF: ", wordDictF)

print(pd.DataFrame([wordDictA, wordDictB, wordDictB, wordDictC, wordDictD, wordDictE, wordDictF]))

OutPut:

   CS  Guitar  Mamoosh  Piano  Sara  Student  William  a  and  is  kind  likes
0   0       0        0      2     0        0        2  0    1   0     0      2
1   0       1        0      0     1        0        0  0    0   0     0      1
2   0       1        0      0     1        0        0  0    0   0     0      1
3   0       0        1      1     0        0        0  0    0   0     0      1
4   1       0        0      0     0        1        1  1    0   1     0      0
5   0       0        0      0     1        0        0  0    0   1     1      0
6   0       0        1      0     0        0        0  0    0   1     1      0

回答 11

当数据规模很大时,我使用了这些设置。

# environment settings: 
pd.set_option('display.max_column',None)
pd.set_option('display.max_rows',None)
pd.set_option('display.max_seq_items',None)
pd.set_option('display.max_colwidth', 500)
pd.set_option('expand_frame_repr', True)

您可以在这里参考文档

I used these settings when scale of data is high.

# environment settings: 
pd.set_option('display.max_column',None)
pd.set_option('display.max_rows',None)
pd.set_option('display.max_seq_items',None)
pd.set_option('display.max_colwidth', 500)
pd.set_option('expand_frame_repr', True)

You can refer the documentationhere


回答 12

下一行足以显示数据框中的所有列。 pd.set_option('display.max_columns', None)

The below line is enough to display all columns from dataframe. pd.set_option('display.max_columns', None)


回答 13

如果您不想弄乱显示选项,而只想查看此特定的列列表,而无需扩展您查看的每个数据框,则可以尝试:

df.columns.values

If you don’t want to mess with your display options and you just want to see this one particular list of columns without expanding out every dataframe you view, you could try:

df.columns.values

回答 14

您还可以尝试循环:

for col in df.columns: 
    print(col) 

You can also try in a loop:

for col in df.columns: 
    print(col) 

回答 15

您只需执行以下步骤,

  • 您可以如下更改熊猫max_columns功能的选项

    import pandas as pd
    pd.options.display.max_columns = 10

    (这将显示10列,您可以根据需要进行更改)

  • 这样,您可以更改行数,如下所示(如果您还需要更改最大行数)

    pd.options.display.max_rows = 999

    (这允许一次打印999行)

请参考文档以更改熊猫的不同选项/设置

You can simply do the following steps,

  • You can change the options for pandas max_columns feature as follows

    import pandas as pd
    pd.options.display.max_columns = 10
    

    (this allows 10 columns to display, you can change this as you need)

  • Like that you can change the number of rows as you need to display as follows (if you need to change maximum rows as well)

    pd.options.display.max_rows = 999
    

    (this allows to print 999 rows at a time)

Please kindly refer the doc to change different options/settings for pandas