How To Expand The Output To See More Columns Of Pandas Dataframe – Definitive Guide

A real-world dataset might have a huge number of columns.

You can expand the output to see more columns of a pandas dataframe using the pd.set_option(‘display.max_columns’, 50) option.

Basic Example

pd.set_option('display.max_columns', n)

df

• display.max_columns – Property to set
• n – denotes the number of columns you want to display. Use 50 if you want to display 50 columns

This tutorial teaches you how to expand the output to see more columns or see all columns of a pandas dataframe.

To select specific columns from the dataframe, read How To Select Columns From Pandas Dataframe

Sample Dataframe

First, create a dataframe with 2 rows and 50 columns values and fill it with random values using np.random.random().

import numpy as np

import pandas as pd

df = pd.DataFrame(np.random.random(100).reshape(2, 50))

df

When you print the dataframe, only 20 columns are printed by default.

Dataframe Will Look Like

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49
0	0.417062	0.219623	0.921322	0.503798	0.982479	0.892796	0.248203	0.353815	0.726481	0.100264	0.510168	0.415028	0.947420	0.720434	0.924738	0.400641	0.304465	0.619013	0.310163	0.636735	0.151370	0.519218	0.287380	0.563320	0.214689	0.652611	0.046745	0.006324	0.068200	0.513099	0.020755	0.455717	0.697704	0.179328	0.032257	0.816988	0.196204	0.937047	0.505805	0.418193	0.80158	0.645451	0.827207	0.940999	0.715637	0.635426	0.160438	0.984641	0.950591	0.832376
1	0.437460	0.715467	0.365535	0.413917	0.390729	0.668190	0.933138	0.272375	0.669757	0.263731	0.554711	0.614270	0.620697	0.237893	0.510138	0.903928	0.807898	0.179799	0.374178	0.528347	0.302234	0.517696	0.211037	0.113548	0.968004	0.661091	0.723043	0.469664	0.516262	0.850608	0.069848	0.439313	0.090727	0.786028	0.422759	0.814950	0.254307	0.810177	0.256475	0.780853	0.52584	0.372847	0.531025	0.101307	0.324935	0.582926	0.781617	0.496048	0.162659	0.134785

Now let us see how to use the options to display more columns.

Using Pandas set_Option Method

Pandas allow you to set values for different options using the set_option() method.

• The option used to display more columns is display.max_columns.

The setting will remain the same for the complete session and reset when the kernel is restarted.

Code

The following code demonstrates how to display 50 columns.

pd.set_option('display.max_columns', 50)

df

All the 50 columns in the Pandas dataframe are printed.

Dataframe Will Look Like

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49
0	0.417062	0.219623	0.921322	0.503798	0.982479	0.892796	0.248203	0.353815	0.726481	0.100264	0.510168	0.415028	0.947420	0.720434	0.924738	0.400641	0.304465	0.619013	0.310163	0.636735	0.151370	0.519218	0.287380	0.563320	0.214689	0.652611	0.046745	0.006324	0.068200	0.513099	0.020755	0.455717	0.697704	0.179328	0.032257	0.816988	0.196204	0.937047	0.505805	0.418193	0.80158	0.645451	0.827207	0.940999	0.715637	0.635426	0.160438	0.984641	0.950591	0.832376
1	0.437460	0.715467	0.365535	0.413917	0.390729	0.668190	0.933138	0.272375	0.669757	0.263731	0.554711	0.614270	0.620697	0.237893	0.510138	0.903928	0.807898	0.179799	0.374178	0.528347	0.302234	0.517696	0.211037	0.113548	0.968004	0.661091	0.723043	0.469664	0.516262	0.850608	0.069848	0.439313	0.090727	0.786028	0.422759	0.814950	0.254307	0.810177	0.256475	0.780853	0.52584	0.372847	0.531025	0.101307	0.324935	0.582926	0.781617	0.496048	0.162659	0.134785

Using pd.options.display.max_columns Attribute

Pandas allow you to directly set values for different options using the options attribute.

• Use the display.max_columns option to display a specific number of columns.
• It is similar to the set_options() method.

The setting will remain the same for the complete session and reset when the kernel is restarted.

Code

The following code demonstrates how to display 50 columns.

pd.options.display.max_columns = 35

df

All the 50 columns of the dataframe are printed.

Dataframe Will Look Like

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	…	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49
0	0.417062	0.219623	0.921322	0.503798	0.982479	0.892796	0.248203	0.353815	0.726481	0.100264	0.510168	0.415028	0.947420	0.720434	0.924738	0.400641	0.304465	…	0.179328	0.032257	0.816988	0.196204	0.937047	0.505805	0.418193	0.80158	0.645451	0.827207	0.940999	0.715637	0.635426	0.160438	0.984641	0.950591	0.832376
1	0.437460	0.715467	0.365535	0.413917	0.390729	0.668190	0.933138	0.272375	0.669757	0.263731	0.554711	0.614270	0.620697	0.237893	0.510138	0.903928	0.807898	…	0.786028	0.422759	0.814950	0.254307	0.810177	0.256475	0.780853	0.52584	0.372847	0.531025	0.101307	0.324935	0.582926	0.781617	0.496048	0.162659	0.134785

2 rows × 50 columns

Using pd.option_context To Set Option Temporarily

Unlike the other options, the option_context() method allows you to set the option to a specific with statement context.

• The options are valid only to that statement context
• The default setting will be used when the control is out of context.

You can use this method when changing the options temporarily.

Code

with pd.option_context('display.max_columns', 20):
    print(df)

The print() statement prints the values of the 20 columns.

Output

         0         1         2         3         4         5         6   \
0  0.061784  0.472239  0.459510  0.605066  0.614997  0.985658  0.282726   
1  0.251828  0.922949  0.184618  0.261911  0.612386  0.024675  0.655262   

         7         8         9   ...        40        41        42        43  \
0  0.783858  0.535543  0.776357  ...  0.375716  0.911047  0.363114  0.659071   
1  0.074593  0.917032  0.028496  ...  0.616139  0.332934  0.678500  0.979174   

         44        45        46        47        48        49  
0  0.042372  0.570251  0.211773  0.451576  0.149772  0.332129  
1  0.843564  0.192778  0.842525  0.298098  0.793355  0.618525  

[2 rows x 50 columns]

Display Specific Number Of Columns

To print a specific number of columns, assign the number using the display.max_columns property.

Code

pd.options.display.max_columns = 6

df

Only the first six columns of the dataframe are printed.

Dataframe Will Look Like

	0	1	2	…	47	48	49
0	0.417062	0.219623	0.921322	…	0.984641	0.950591	0.832376
1	0.437460	0.715467	0.365535	…	0.496048	0.162659	0.134785

2 rows × 50 columns

Display All Columns in Pandas

To display all columns of the pandas dataframe,

• Get the number of columns in the dataframe using the df.shape[1]
• Set it to the display.max_columns property.

df.shape[1] returns the number of columns, whereas the df.shape[0] returns the number of rows.

Code

pd.options.display.max_columns = df.shape[1]

df

All the columns of the dataframe are printed.

Dataframe Will Look Like

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49
0	0.417062	0.219623	0.921322	0.503798	0.982479	0.892796	0.248203	0.353815	0.726481	0.100264	0.510168	0.415028	0.947420	0.720434	0.924738	0.400641	0.304465	0.619013	0.310163	0.636735	0.151370	0.519218	0.287380	0.563320	0.214689	0.652611	0.046745	0.006324	0.068200	0.513099	0.020755	0.455717	0.697704	0.179328	0.032257	0.816988	0.196204	0.937047	0.505805	0.418193	0.80158	0.645451	0.827207	0.940999	0.715637	0.635426	0.160438	0.984641	0.950591	0.832376
1	0.437460	0.715467	0.365535	0.413917	0.390729	0.668190	0.933138	0.272375	0.669757	0.263731	0.554711	0.614270	0.620697	0.237893	0.510138	0.903928	0.807898	0.179799	0.374178	0.528347	0.302234	0.517696	0.211037	0.113548	0.968004	0.661091	0.723043	0.469664	0.516262	0.850608	0.069848	0.439313	0.090727	0.786028	0.422759	0.814950	0.254307	0.810177	0.256475	0.780853	0.52584	0.372847	0.531025	0.101307	0.324935	0.582926	0.781617	0.496048	0.162659	0.134785

Setting Maximum Column Width

You can use the max_colwidth property to set the maximum column width.

The default column width is 50.

When the column width overflows this set value, it is embedded with a “…” placeholder in the output.

Code

pd.set_option('display.max_colwidth', 20)

df

Dataframe Will Look Like

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49
0	0.363724	0.676707	0.721925	0.575342	0.566716	0.961884	0.073550	0.884787	0.317784	0.970323	0.280536	0.503097	0.007111	0.515447	0.105260	0.374613	0.600998	0.440877	0.886563	0.631789	0.428414	0.333583	0.010816	0.619702	0.250974	0.289465	0.246841	0.467938	0.892630	0.540848	0.826629	0.744478	0.854683	0.997073	0.950930	0.613686	0.934962	0.355072	0.050826	0.456950	0.838414	0.583197	0.671389	0.565268	0.263413	0.580325	0.914720	0.430985	0.614488	0.461278
1	0.191289	0.997060	0.481743	0.570579	0.439722	0.417660	0.874434	0.348448	0.804842	0.950400	0.973354	0.818711	0.198811	0.004225	0.256985	0.139528	0.142631	0.150044	0.316873	0.055435	0.288040	0.609995	0.405348	0.362378	0.340979	0.486746	0.614668	0.983170	0.109289	0.223767	0.785090	0.151063	0.791574	0.174479	0.765386	0.640319	0.648512	0.049203	0.847795	0.548358	0.763249	0.213180	0.457687	0.531022	0.474988	0.579902	0.845015	0.234135	0.491087	0.629529

Additional Resources

• How to Pretty Print the dataframe
• How To Select Column Names Starting With a Particular String in Pandas Dataframe