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How to Pretty Print an Entire Pandas Series or DataFrame?

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  • Last Updated : 14 Sep, 2022
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In this article, we are going to see how to Pretty Print the entire pandas Series / Dataframe.  There are various pretty print options are available for use with this method. Here we will discuss 3 ways to Pretty Print the entire Pandas Dataframe:

Creating DataFrame to Pretty-print an entire Pandas DataFrame

Python3




import pandas as pd
 
# Create a dataframe
df = pd.DataFrame({
  'Product_id': ['ABC', 'DEF', 'GHI', 'JKL',
                 'MNO', 'PQR', 'STU', 'VWX'],
   
  'Stall_no': [37, 38, 9, 50, 7, 23, 33, 4],
  'Grade': [1, 0, 0, 2, 0, 1, 3, 0],
   
  'Category': ['Fashion', 'Education', 'Technology',
               'Fashion', 'Education', 'Technology',
               'Fashion', 'Education'],
   
  'Demand': [10, 12, 14, 15, 13, 20, 10, 15],
  'charges1': [376, 397, 250, 144, 211, 633, 263, 104],
  'charges2': [11, 12, 9, 13, 4, 6, 13, 15],
  'Max_Price': [4713, 10352, 7309, 20814, 9261,
                6104, 5257, 5921],
   
  'Selling_price': [4185.9477, 9271.490256, 6785.701362,
                    13028.91782, 906.553935, 5631.247872,
                    3874.264992, 4820.943]})
display(df)

Output:

 

Some Important terms to use in pretty print options are discussed below:

  • display.max_columns: The maximum number of columns pandas should print. If None is provided as an argument all columns are printed.
  • display.max_rows: The maximum number of rows pandas should print. If None is provided as an argument all rows are printed.
  • display.colheader_justify: Controls the alignment of column headers
  • display.precision: Floating point output precision in terms of a number of places after the decimal, for regular formatting as well as scientific notation.
  • display.width: Width of the display in characters. If set to None, pandas will correctly auto-detect the width.

Reduce the Size of a Pandas Dataframe using pd.set_options() 

We will use some options of the set_options() method on the above df to see all rows, all columns, all columns in one row with center-aligned column headers, and rounding the number of places after the decimal for each floating value to 2.

Python3




pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 2)
display(df)

Output:

Once set through pd.set_options() method, the same settings are used with all the next Dataframe printing commands. 

 

Reduce the Size of a Pandas Dataframe using pd.option_context()

The pd.set_option() method provides permanent setting for displaying dataframe. pd.option_context() temporarily sets the options in with statement context. Following code prints the above df with 4 rows, all columns, all columns in one row with left-aligned column headers, and rounding the number of places after the decimal for each floating value.

Python3




with pd.option_context('display.max_rows', 5,
                       'display.max_columns', None,
                       'display.width', 1000,
                       'display.precision', 3,
                       'display.colheader_justify', 'left'):
    display(df)

Output:

 

Reduce the Size of a Pandas Dataframe using options.display

Following code prints the above df with 4 rows, 4 columns, all columns in one row with left-aligned column headers, and not rounding the number of places after the decimal for each floating value.

Python3




import pandas as pd
import numpy as np
 
def display_options():
     
    display = pd.options.display
    display.max_columns = 5
    display.max_rows = 4
    display.max_colwidth = 222
    display.width = None
    return None
 
display_options()
display(df)

Output:

 


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