How do I expand the output display to see more columns of a Pandas DataFrame?
Question:
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 five 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.
Answers:
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.).
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
Furthermore, 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
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, 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)
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.
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: pandas.set_option.
You can set the output display to match your current terminal width:
pd.set_option('display.width', pd.util.terminal.get_terminal_size()[0])
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.
Set the column maximum width using:
pd.set_option('max_colwidth', 800)
This particular statement sets the maximum width to 800 pixels per column.
According to the documentation for v0.18.0, if you’re running in a terminal (i.e., not IPython notebook, qtconsole or IDLE), it’s a two-liner to have Pandas autodetect 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)
It seems like all the previous 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 documentation: 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_... parameters:
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 parameter:
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.
Only using these three 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)
It was for Anaconda, Python 3.6.5, Pandas 0.23.0, and Visual Studio Code 1.26.
I used these settings when the scale of the data was 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 to the documentation here.
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]))
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
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
You can also try in a loop:
for col in df.columns:
print(col)
The below line is enough to display all columns from a dataframe.
pd.set_option('display.max_columns', None)
You can simply do the following steps,
-
You can change the options for the Pandas max_columns feature as follows:
import pandas as pd
pd.options.display.max_columns = 10
(This allows 10 columns to display, and you can change this as you need.)
-
Like that, you can change the number of rows that 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 to the documentation to change different options/settings for Pandas.
pd.options.display.max_columns = 100
You can specify the numbers of columns as per your requirement in max_columns.
You can use this custom function for displaying things for a Pandas Dataframe.
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!
You don’t have to use pd.set_option for the whole notebook just using for a single cell.
None of these answers were working for me. A couple of them would indeed print all the columns, but it would look sloppy. As in all the information was there, but it wasn’t formatted correctly. I’m using a terminal inside of Neovim so I suspect that to be the reason.
This mini function does exactly what I need, just change df_data in the two places it is for your dataframe name (col_range is set to what pandas naturally shows, for me it is 5 but it could be bigger or smaller for you).
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())
The below will increase the width when NumPy arrays are printed.
It gave good results in Jupyter Notebook.
import numpy as np
np.set_printoptions(linewidth=120)
For increasing the print area for pandas objects (eg: Index):
import pandas as pd
pd.set_option('display.width', 120)
It is not the answer strictly speaking, but let’s remember we can df.describe().transpose() or even df.head(n).transpose(), or df.tail(n).transpose().
I also find it easier to read headers as a column when they are structured:
header1_xxx,
header2_xxx,
header3_xxx,
I think terminals and applications handle the vertical scrolling more naturally, if this is necessary after transposing.
Headers are commonly larger than their values, having all of them in one column (index) minimizes their impact on the total table width.
Finally other df descriptions can be merged as well, here is a possible idea:
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))
)
To use context in notebook,
from IPython.display import display
with pd.option_context('display.max_rows', None,
'display.max_columns', None):
display(df)
*based on previous answer
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 five 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.
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.).
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
Furthermore, 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
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, 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)
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.
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: pandas.set_option.
You can set the output display to match your current terminal width:
pd.set_option('display.width', pd.util.terminal.get_terminal_size()[0])
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.
Set the column maximum width using:
pd.set_option('max_colwidth', 800)
This particular statement sets the maximum width to 800 pixels per column.
According to the documentation for v0.18.0, if you’re running in a terminal (i.e., not IPython notebook, qtconsole or IDLE), it’s a two-liner to have Pandas autodetect 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)
It seems like all the previous 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 documentation: 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-leveloptionsattribute: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_... parameters:
max_rowsandmax_columnsare used in__repr__()methods to decide ifto_string()orinfo()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 parameter:
Width of the display in characters. In case Python/IPython is running in a terminal this can be set to
Noneand 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.
Only using these three 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)
It was for Anaconda, Python 3.6.5, Pandas 0.23.0, and Visual Studio Code 1.26.
I used these settings when the scale of the data was 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 to the documentation here.
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]))
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
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
You can also try in a loop:
for col in df.columns:
print(col)
The below line is enough to display all columns from a dataframe.
pd.set_option('display.max_columns', None)
You can simply do the following steps,
-
You can change the options for the Pandas max_columns feature as follows:
import pandas as pd pd.options.display.max_columns = 10(This allows 10 columns to display, and you can change this as you need.)
-
Like that, you can change the number of rows that 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 to the documentation to change different options/settings for Pandas.
pd.options.display.max_columns = 100
You can specify the numbers of columns as per your requirement in max_columns.
You can use this custom function for displaying things for a Pandas Dataframe.
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!
You don’t have to use pd.set_option for the whole notebook just using for a single cell.
None of these answers were working for me. A couple of them would indeed print all the columns, but it would look sloppy. As in all the information was there, but it wasn’t formatted correctly. I’m using a terminal inside of Neovim so I suspect that to be the reason.
This mini function does exactly what I need, just change df_data in the two places it is for your dataframe name (col_range is set to what pandas naturally shows, for me it is 5 but it could be bigger or smaller for you).
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())
The below will increase the width when NumPy arrays are printed.
It gave good results in Jupyter Notebook.
import numpy as np
np.set_printoptions(linewidth=120)
For increasing the print area for pandas objects (eg: Index):
import pandas as pd
pd.set_option('display.width', 120)
It is not the answer strictly speaking, but let’s remember we can df.describe().transpose() or even df.head(n).transpose(), or df.tail(n).transpose().
I also find it easier to read headers as a column when they are structured:
header1_xxx,
header2_xxx,
header3_xxx,
I think terminals and applications handle the vertical scrolling more naturally, if this is necessary after transposing.
Headers are commonly larger than their values, having all of them in one column (index) minimizes their impact on the total table width.
Finally other df descriptions can be merged as well, here is a possible idea:
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))
)
To use context in notebook,
from IPython.display import display
with pd.option_context('display.max_rows', None,
'display.max_columns', None):
display(df)
*based on previous answer