Q:

How to widen output display to see more columns in Pandas DataFrame?

belongs to collection: Python Pandas Programs

mahmoud2506
Save to bookmark
|
2022-05-25
|
  177 
0

In the real world, data is huge so is the dataset. While importing a dataset and converting it into DataFrame, if the number of columns is large, the default printing method does not print the entire columns. It compresses the rows and columns. In this article, we are going to learn how to expand the output display to see all the columns of DataFrame.

We can achieve this task with the help of pandas.DataFrame.set_options() function. The set_options() method allows us to set different properties according to our requirements. display.max_columns() defines the total number of columns to be printed. If None is passed as an argument, all columns would be printed.

To work with MultiIndex in Python Pandas, we need to import the pandas library. Below is the syntax,

import pandas as pd
Python programming,
login and add your own answer to this question

All Answers

mahmoud2506
2022-05-25
need an explanation for this answer? contact us directly to get an explanation for this answer

Let us understand with the help of an example.

#  importing pandas package
import pandas as pd

# Creating an empty dictionary
d = {}

# Creating a large DataFrame of 20 columns
for i in range(1,21):
    d[i]=[i for i in range(0,11)]
df=pd.DataFrame(d)

# Printing default DataFrame
print(df)

Output:

    1   2   3   4   5   6   7   8   9  ...  12  13  14  15  16  17  18  19  20
0    0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   0   0
1    1   1   1   1   1   1   1   1   1 ...   1   1   1   1   1   1   1   1   1
2    2   2   2   2   2   2   2   2   2 ...   2   2   2   2   2   2   2   2   2
3    3   3   3   3   3   3   3   3   3 ...   3   3   3   3   3   3   3   3   3
4    4   4   4   4   4   4   4   4   4 ...   4   4   4   4   4   4   4   4   4
5    5   5   5   5   5   5   5   5   5 ...   5   5   5   5   5   5   5   5   5
6    6   6   6   6   6   6   6   6   6 ...   6   6   6   6   6   6   6   6   6
7    7   7   7   7   7   7   7   7   7 ...   7   7   7   7   7   7   7   7   7
8    8   8   8   8   8   8   8   8   8 ...   8   8   8   8   8   8   8   8   8
9    9   9   9   9   9   9   9   9   9 ...   9   9   9   9   9   9   9   9   9
10  10  10  10  10  10  10  10  10  10 ...  10  10  10  10  10  10  10  10  10

[11 rows x 20 columns]

Here, we have defined 20 columns but not all the columns are printed, to overcome this problem, we will use the set_options('display.max_columns') method.

#  importing pandas package
import pandas as pd

# Creating an empty dictionary
d = {}

# Creating a large DataFrame of 20 columns
for i in range(1,21):
    d[i]=[i for i in range(0,11)]
df=pd.DataFrame(d)

# Printing default DataFrame
print(df)

# Setting value of columns=20
pd.set_option('display.max_columns', 20)

# Printing modified DataFrame
print(df)

Output:

    1   2   3   4   5   6   7   8   9  ...  12  13  14  15  16  17  18  19  20
0    0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   0   0
1    1   1   1   1   1   1   1   1   1 ...   1   1   1   1   1   1   1   1   1
2    2   2   2   2   2   2   2   2   2 ...   2   2   2   2   2   2   2   2   2
3    3   3   3   3   3   3   3   3   3 ...   3   3   3   3   3   3   3   3   3
4    4   4   4   4   4   4   4   4   4 ...   4   4   4   4   4   4   4   4   4
5    5   5   5   5   5   5   5   5   5 ...   5   5   5   5   5   5   5   5   5
6    6   6   6   6   6   6   6   6   6 ...   6   6   6   6   6   6   6   6   6
7    7   7   7   7   7   7   7   7   7 ...   7   7   7   7   7   7   7   7   7
8    8   8   8   8   8   8   8   8   8 ...   8   8   8   8   8   8   8   8   8
9    9   9   9   9   9   9   9   9   9 ...   9   9   9   9   9   9   9   9   9
10  10  10  10  10  10  10  10  10  10 ...  10  10  10  10  10  10  10  10  10

[11 rows x 20 columns]
    1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  18  \
0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
1    1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   
2    2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   
3    3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   
4    4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   
5    5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   
6    6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   
7    7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   
8    8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   
9    9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   
10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10   

    19  20  
0    0   0  
1    1   1  
2    2   2  
3    3   3  
4    4   4  
5    5   5  
6    6   6  
7    7   7  
8    8   8  
9    9   9  
10  10  10

need an explanation for this answer? contact us directly to get an explanation for this answer

total answers (1)

Python Pandas Programs
This question belongs to these collections

Similar questions

need a help?

find thousands of online teachers now
How to Use \'NOT IN\' Filter in Pandas?... >>
<< Difference between loc() and iloc() in Pandas Data...