有没有办法在交互式或脚本执行模式下扩大输出显示？
具体来说，我在Pandas dataframe上使用describe（）函数。当dataframe是5列（标签）宽时，我得到了我想要的描述性统计数据。但是，如果dataframe有更多列，则会抑制统计信息并返回类似的内容：
>> 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
无论是否有6列或7列，都给出“8”值。 “8”是指什么？
我已经尝试将IDLE窗口拖大，以及增加“配置IDLE”宽度选项，但无济于事。
我使用Pandas和describe（）的目的是避免使用像STATA这样的第二个程序来进行基本的数据操作和调查。
Python / IDLE 2.7.3 熊猫0.8.1 Notepad ++ 6.1.4（UNICODE） Windows Vista SP2
更新：Pandas 0.23.4起
这不是必需的，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(...)已被弃用。相反，使用pandas.set_option(optname, val)，或等效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)
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)
编辑：旧版本信息，其中大部分已被弃用。
作为@bmu mentioned，pandas会自动检测（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。你提到调整IDLE窗口的大小，没有效果。如果你做print df.describe().to_string()它是否适合IDLE窗口？
终端大小由pandas.util.terminal.get_terminal_size()（不推荐使用和删除）确定，这将返回包含显示的(width, height)的元组。输出是否与IDLE窗口的大小相匹配？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，如果行数，列数不超过给定限制，pandas.set_printoptions(max_rows=200, max_columns=10)将永远不会切换到摘要视图。
似乎以上所有答案都解决了这个问题。还有一点：你可以使用（自动完成）而不是pd.set_option('option_name')
pd.options.display.width = None
见Pandas doc: Options and Settings:
选项具有完整的“点状样式”，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options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 In [4]: pd.options.display.max_rows Out[4]: 999
[...]
对于max_...参数：
在
max_rows方法中使用max_columns和__repr__()来决定是否使用to_string()或info()将对象渲染为字符串。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，并在所有列不垂直的情况下交换为较小的格式。 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'价值意味着无限。 [重点不在原文]
为width param：
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笔记本，IPython qtconsole或IDLE不在终端中运行，因此无法正确检测宽度。
我在数据规模很大时使用了这些设置。
# 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)
你可以参考documentationhere
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
如果您不想弄乱显示选项，并且只想查看这一列的特定列表而不扩展您查看的每个数据帧，您可以尝试：
df.columns.values
你也可以尝试循环：
for col in df.columns:
print(col)
试试这个：
pd.set_option('display.expand_frame_repr', False)
从文档：
display.expand_frame_repr：boolean
无论是跨多行打印出宽数据帧的完整DataFrame repr，max_columns仍然受到尊重，但如果宽度超过display.width，则输出将在多个“页面”上进行环绕。 [默认值：True] [当前：True]
见：http://pandas.pydata.org/pandas-docs/stable/generated/pandas.set_option.html
如果要临时设置选项以显示一个大型DataFrame，可以使用option_context：
with pd.option_context('display.max_rows', -1, 'display.max_columns', 5):
print df
退出with块时，会自动恢复选项值。
使用以下方法设置列最大宽
pd.set_option('max_colwidth', 800)
此特定语句将每列的最大宽度设置为800px。
只使用这三条线为我工作：
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
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，这并不适用于所有情况，因为pandas会检测到您的控制台宽度，如果输出适合控制台，它将只使用to_string（请参阅set_printoptions的文档字符串）。在这种情况下，您可以显式调用to_string作为BrenBarn的回答。
更新
使用0.10版本的方式打印宽数据帧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，用于设置pandas选项的API已更改：
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
您可以将输出显示设置为与当前终端宽度匹配：
pd.set_option('display.width', pd.util.terminal.get_terminal_size()[0])
您可以使用print df.describe().to_string()强制它显示整个表格。 （对于任何DataFrame，你可以像这样使用to_string() .describe的结果只是一个DataFrame本身。）
8是DataFrame中包含“description”的行数（因为describe计算8个统计数据，min，max，mean等）。
根据docs for v0.18.0的说法，如果你在一个终端（即不是iPython笔记本，qtconsole或IDLE）上运行，那么Pandas可以自动检测你的屏幕宽度并随时调整显示的列数：
pd.set_option('display.large_repr', 'truncate')
pd.set_option('display.max_columns', 0)