是否有一种方法可以在交互或脚本执行模式下扩大输出的显示?

具体来说,我在Pandas DataFrame上使用了describe()函数。当DataFrame是五列(标签)宽时,我得到了我想要的描述性统计数据。然而,如果DataFrame有更多的列,统计数据将被抑制,并返回如下内容:

>> 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

无论有6列还是7列,都给出“8”值。“8”指什么?

我已经尝试过将IDLE窗口拖大,以及增加“配置IDLE”宽度选项,但无济于事。


当前回答

你可以设置输出显示来匹配你当前的终端宽度:

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

其他回答

更新:熊猫0.23.4起

这是不必要的。如果设置pd.options.display.width = 0, Pandas会自动检测终端窗口的大小。(旧版本见底部。)

Pandas.set_printoptions(…)已弃用。相反,使用熊猫。set_option(optname, val),或者等效的pd.options.<opt. hierarchy .name> = val。

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

下面是set_option的帮助:

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, e.g., 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)

旧版本信息。这其中的大部分已经被弃用了。

正如@bmu提到的,Pandas自动检测(默认情况下)显示区域的大小,当对象repr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。如果你打印df.describe().to_string()是否适合IDLE窗口?

终端大小由pandas.util.terminal.get_terminal_size()(已弃用并删除)决定,这将返回一个包含显示的(宽度,高度)的元组。输出是否与IDLE窗口的大小匹配?可能会出现问题(以前在Emacs中运行终端时出现过一个问题)。

注意,可以绕过自动检测,熊猫。Set_printoptions (max_rows=200, max_columns=10)将永远不会切换到摘要视图,如果行数,列数没有超过给定的限制。


'max_colwidth'选项有助于查看每列的未截断形式。

这些答案对我都没用。其中一些确实会打印所有列,但看起来会很草率。所有的信息都在那里,但格式不正确。我正在使用Neovim内部的终端,所以我怀疑这是原因。

这个迷你函数确实是我需要的,只是改变df_data在两个地方,它是为你的dataframe名称(col_range被设置为熊猫自然显示,对我来说是5,但它可以更大或更小为你)。

import math
col_range = 5
for _ in range(int(math.ceil(len(df_data.columns)/col_range))):
    idx1 = _*col_range
    idx2 = idx1+col_range
    print(df_data.iloc[:, idx1:idx2].describe())
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 consisting 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

你可以通过set_printoptions来调整Pandas打印选项。

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

然而,这并不会在所有情况下工作,因为Pandas会检测你的控制台宽度,并且它只会在输出适合控制台时使用to_string(参见set_printoptions的文档字符串)。 在这种情况下,你可以显式调用由BrenBarn回答的to_string。

更新

在0.10版本中,数据帧的打印方式发生了变化:

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

此外,设置Pandas选项的API改变了:

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

我只用了这三句话:

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

它适用于Anaconda, Python 3.6.5, Pandas 0.23.0和Visual Studio Code 1.26。