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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.

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    Here is the user guide 'options and settings' from pandas with examples you are looking for. Commented May 14, 2021 at 23:18

23 Answers 23

Reset to default
1512

(For Pandas versions before 0.23.4, see at bottom.)

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)

Pandas will try to autodetect the size of your terminal window if you set pd.options.display.width = 0.

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.

TruncatedColumnDisplay

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3 Comments

Equivalent to the answer import pandas as pd pd.options.display.width = 1000 pd.options.display.max_rows = 500 pd.options.display.max_columns = 500
Pandas did not auto-detect the size of my window with pd.options.display.width = 0, but pd.options.display.width = 1000 worked.
I wonder if pd.options.display.width = 0 is an typo in this answer. The quoted documentation only describes setting display.max_columns to zero in order to enable auto-detection of window width. However, the latter doesn't work for me.
288

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.

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275

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.

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191

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.

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<ipython-input-40-af12a43ad140>:3: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width. pd.set_option('max_colwidth', -1)
91

Set the column maximum width using:

pd.set_option('max_colwidth', 800)

This particular statement sets the maximum width to 800 pixels per column.

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Scrolling down in descending vote order, this is the first answer that worked for me to get pandas to not truncate the plaintext output of DataFrames. (pandas 0.22, iTerm2 3.0.13, OS X 10.12).
This is the only one that worked for me for Pandas 0.23.2.
How come you didn't have to specify it as display.max_colwidth? That is how it is listed in the documentation. I agree that only max_colwidth works and is shorter to write, but I was surprised.
35

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.

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This was only one that worked using Powershell / Windows / Pandas 1.5.1 / Python 3.10.1
max_seq_items is what you need for things like print(df.columns) - other answers miss this
35

The below line is enough to display all columns from a dataframe.

pd.set_option('display.max_columns', None)
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Welcome to SO! When you post a new answer to a question and there are some more answers, try to show the Pros. There is still one answer pd.set_option('display.max_columns', 0) Which ones are the benefits of yours?
30

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.).

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29

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
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25

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

pd.set_option('display.width', pd.util.terminal.get_terminal_size()[0])
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this no longer works
21

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.

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15

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)
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This worked for me, thank you! I'm using Pandas 0.22.0 (latest as of Feb 8 2018) using the built-in Terminal app on OS X 10.11.6
What if you are running in a notebook?
13

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.

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12 
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
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9

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.

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5

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

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Why are you manually calling display()? The last value of a cell gets displayed by default, unless you turned that off.
@wjandrea so that it can be run in the middle of a cell?
4

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
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3

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)
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2

You can also try in a loop:

for col in df.columns: print(col)
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An explanation would be in order. E.g., how does that answer the question "Is there a way to widen the display of output in either interactive or script-execution mode?"
2 
pd.options.display.max_columns = 100

You can specify the numbers of columns as per your requirement in max_columns.

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1

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)) )
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0

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())
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0

If you want to see all rows of a particular column, you just need to select the dataframe with the column name and then use the tolist method.

pd['column_name'].tolist()

This will convert pandas series to a python list.

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