如何扩展输出显示以查看 Pandas DataFrame 的更多列？

更新：熊猫 0.23.4 起

这不是必需的。如果您设置 pd.options.display.width = 0，Pandas 会自动检测终端窗口的大小。 （对于旧版本，请参阅底部。）

pandas.set_printoptions(...) 已弃用。而是使用 pandas.set_option(optname, val) 或等效的 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 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, 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。其中大部分已被弃用。

正如@bmu mentioned，Pandas 自动检测（默认）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，但没有效果。如果您执行 print df.describe().to_string()，它是否适合 IDLE 窗口？

终端大小由 pandas.util.terminal.get_terminal_size() 确定（已弃用和删除），这将返回一个包含显示的 (width, height) 的元组。输出是否与 IDLE 窗口的大小匹配？可能存在问题（之前在 Emacs 中运行终端时存在问题）。

请注意，可以绕过自动检测，如果行数、列数未超过给定限制，pandas.set_printoptions(max_rows=200, max_columns=10) 将永远不会切换到摘要视图。

'max_colwidth' 选项有助于查看每列的未截断形式。

https://i.stack.imgur.com/J412l.png

---

尝试这个：

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

从文档中：

display.expand_frame_repr : boolean 是否为跨多行的宽 DataFrame 打印完整的 DataFrame repr，仍然尊重 max_columns，但如果宽度超过 display.width，输出将跨越多个“页面”。 [默认：真] [当前：真]

请参阅：pandas.set_option。

---

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

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

当您退出 with 块时，选项值会自动恢复。

---

只有使用这三行对我有用：

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

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

---

使用以下方法设置列最大宽度：

pd.set_option('max_colwidth', 800)

此特定语句将最大宽度设置为每列 800 像素。

---

您可以使用 print df.describe().to_string() 强制它显示整个表格。 （您可以像这样对任何 DataFrame 使用 to_string()。describe 的结果只是一个 DataFrame 本身。）

8 是 DataFrame 中包含“描述”的行数（因为 describe 计算 8 个统计信息，最小值、最大值、平均值等）。

---

您可以使用 set_printoptions 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

但是，这并非在所有情况下都有效，因为 Pandas 会检测您的控制台宽度，并且只有在输出适合控制台时才会使用 to_string（请参阅 set_printoptions 的文档字符串）。在这种情况下，您可以按照 BrenBarn 的回答显式调用 to_string。

更新

使用 0.10 版打印宽数据帧的方式 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

此外，设置 Pandas 选项的 API 发生了变化：

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

---

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)

您可以参考文档 here。

---

您可以设置输出显示以匹配您当前的终端宽度：

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

---

下面的行足以显示数据框中的所有列。

pd.set_option('display.max_columns', None)

---

根据 documentation for v0.18.0，如果您在终端中运行（即，不是 IPython 笔记本、qtconsole 或 IDLE），那么让 Pandas 自动检测您的屏幕宽度并动态适应如何它显示了许多列：

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

---

似乎所有先前的答案都解决了这个问题。还有一点：您可以使用 (auto-complete-able) 代替 pd.set_option('option_name')：

pd.options.display.width = None

请参阅Pandas documentation: Options and settings：

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/设置选项作为顶级选项属性的属性： In [1]: import pandas as pd In [2]: pd.options.display.max_rows Out[2]: 15 In [3]: pd .options.display.max_rows = 999 输入 [4]：pd.options.display.max_rows 输出[4]：999

[...]

对于 max_... 参数：

max_rows 和 max_columns 用于 __repr__() 方法来决定是否使用 to_string() 或 info() 将对象呈现为字符串。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更小的格式，以防所有列垂直不适合。 IPython notebook、IPython qtconsole 或 IDLE 不在终端中运行，因此无法进行正确的自动检测。 “无”值意味着无限。 【重点不在原文】

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 并且 pandas 将正确地自动检测宽度。请注意，IPython notebook、IPython qtconsole 或 IDLE 不在终端中运行，因此无法正确检测宽度。

---

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

---

您可以简单地执行以下步骤，

您可以更改 Pandas max_columns 功能的选项，如下所示： import pandas as pd pd.options.display.max_columns = 10 （这允许显示 10 列，您可以根据需要进行更改。）

像这样，您可以更改需要显示的行数，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。）

请参阅 the documentation 更改 Pandas 的不同选项/设置。

---

您可以使用此自定义函数来显示 Pandas Dataframe 的内容。

def display_all(df):     # For any Dataframe df
   with pd.option_context('display.max_rows',1000): # Change number of rows accordingly
      with pd.option_context('display.max_columns',1000): # Change number of columns accordingly
          display(df)

display_all(df.head()) # Pass this function to your dataframe and voilà!

您不必将 pd.set_option 用于整个笔记本，只需用于单个单元格。

---

如果您不想弄乱您的显示选项并且只想查看这个特定的列列表而不展开您查看的每个数据框，您可以尝试：

df.columns.values

---

您也可以循环尝试：

for col in df.columns: 
    print(col) 

---

pd.options.display.max_columns = 100

您可以根据您的要求在 max_columns 中指定列数。

---

下面将在打印 NumPy 数组时增加宽度。

它在 Jupyter Notebook 中给出了很好的结果。

import numpy as np
np.set_printoptions(linewidth=160)

---

这些答案都不适合我。其中一些确实会打印所有列，但看起来很草率。与所有信息一样，但格式不正确。我在 Neovim 内使用终端，所以我怀疑这就是原因。

这个迷你函数完全符合我的需要，只需在两个位置更改 df_data 即可为您的数据框名称（col_range 设置为 pandas 自然显示的内容，对我来说是 5，但对您来说可能更大或更小）。

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())

---

严格来说，这不是答案，但请记住，我们可以df.describe().transpose()甚至df.head(n).transpose()或df.tail(n).transpose()。

我还发现在结构化标题时更容易将标题作为一列阅读：

header1_xxx,

header2_xxx,

header3_xxx,

我认为终端和应用程序更自然地处理垂直滚动，如果在转置后这是必要的。

标题通常大于它们的值，将它们全部放在一列（索引）中可以最大限度地减少它们对总表宽度的影响。

最后也可以合并其他 df 描述，这是一个可能的想法：

def df_overview(df: pd.DataFrame, max_colwidth=25, head=3, tail=3):
    return(
        df.describe([0.5]).transpose()
        .merge(df.dtypes.rename('dtypes'), left_index=True, right_index=True)
        .merge(df.head(head).transpose(), left_index=True, right_index=True)
        .merge(df.tail(tail).transpose(), left_index=True, right_index=True)
        .to_string(max_colwidth=max_colwidth, float_format=lambda x: "{:.4G}".format(x))
    )

---