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:
- Use pd.set_options() method
- Use pd.option_context() method
- Use options.display() Method
Creating DataFrame to Pretty-print an entire Pandas DataFrame
Python3
import pandas as pd# Create a dataframedf = 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 pdimport numpy as npdef display_options(): display = pd.options.display display.max_columns = 5 display.max_rows = 4 display.max_colwidth = 222 display.width = None return Nonedisplay_options()display(df) |
Output:
