Expand output to display all columns of Pandas DataFrame

In situations where the pandas DataFrame contains too many columns, the default display settings don’t allow all the columns to be shown in the output. Just a few first and last columns would be displayed and the middle columns would be compressed as “…” sign. In this article, we will discuss multiple approaches on how to expand the output display to see more columns in such situations.

Table of Contents

Introduction

To quickly get started, let’s create a sample DataFrame to experiment. We’ll use the pandas and NumPy library with some random data.

# importing library 
import numpy as np
import pandas as pd

# random DataFrame
df = pd.DataFrame(np.random.random(80).reshape(2, 40))

print (df)

Contents of the created dataframe are,

         0         1         2         3   ...        36        37        38        39
0  0.622675  0.060146  0.604744  0.710405  ...  0.571265  0.567789  0.761263  0.985691
1  0.601135  0.687421  0.537218  0.715122  ...  0.865830  0.680659  0.340409  0.228477

[2 rows x 40 columns]

In the above output, all the columns between 4 to 35 are minimized, and shown as “…”. In order to expand the display, let’s look at different methods.

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Expand display to show all columns using pandas.set_option function

The set_option() functionality in pandas offers all the flexibility to change the default display settings. To change the default number of columns displayed, use the “max_columns” argument from the display.

# change the max_columns value to 40
pd.set_option("display.max_columns", 40)

print (df)

Now after calling the set_option() with 40 as max_columns, if we print any DataFrame then at max it will display 40 columns of the DataFrame. Let’s see a complete example,

# importing library 
import numpy as np
import pandas as pd

# random DataFrame
df = pd.DataFrame(np.random.random(80).reshape(2, 40))

# change the max_columns value to 40
pd.set_option("display.max_columns", 40)

print (df)

Output

         0         1         2         3         4         5         6   \
0  0.099547  0.317654  0.696267  0.500405  0.662395  0.347217  0.919962   
1  0.183956  0.093377  0.914585  0.824104  0.178390  0.714917  0.740702   

         7         8         9         10        11        12        13  \
0  0.792616  0.856906  0.470693  0.828003  0.167716  0.705077  0.900042   
1  0.099741  0.748058  0.281502  0.632023  0.428459  0.123901  0.231481   

         14        15        16        17        18        19        20  \
0  0.753212  0.838928  0.762214  0.256567  0.874984  0.396890  0.600915   
1  0.649959  0.205741  0.174359  0.349932  0.758043  0.092794  0.180718   

         21        22        23        24        25        26        27  \
0  0.238923  0.501682  0.259952  0.007046  0.794067  0.392352  0.296982   
1  0.867870  0.861766  0.538408  0.277828  0.418462  0.528816  0.006187   

         28        29        30        31        32        33        34  \
0  0.242004  0.647488  0.406277  0.239739  0.086527  0.661979  0.703148   
1  0.184497  0.260851  0.599812  0.064739  0.736251  0.805404  0.899075   

         35        36        37        38        39  
0  0.592287  0.350457  0.372377  0.246324  0.342693  
1  0.785853  0.289017  0.504370  0.770860  0.416821

As observed above, the output now shows all the columns from the pandas DataFrame. We can set this value to “None” in case we want to always display all the columns in any DataFrame. Like,

# change the max_columns value to infinite
pd.set_option("display.max_columns", None)

print (df)

It will display all columns of the DataFrame while printing.

Expand display to show all columns using pandas.options() method

Instead of using it as an argument, we can directly change the max_columns value using the options function from Pandas. Let’s again try to expand the columns to 40.

# change the max_columns value to 40
pd.options.display.max_columns = 40

print (df)

Let’s checkout the complete example,

# importing library 
import numpy as np
import pandas as pd

# random DataFrame
df = pd.DataFrame(np.random.random(80).reshape(2, 40))

# change the max_columns value to 40
pd.options.display.max_columns = 40

print (df)

Output

         0         1         2         3         4         5         6   \
0  0.099547  0.317654  0.696267  0.500405  0.662395  0.347217  0.919962   
1  0.183956  0.093377  0.914585  0.824104  0.178390  0.714917  0.740702   

         7         8         9         10        11        12        13  \
0  0.792616  0.856906  0.470693  0.828003  0.167716  0.705077  0.900042   
1  0.099741  0.748058  0.281502  0.632023  0.428459  0.123901  0.231481   

         14        15        16        17        18        19        20  \
0  0.753212  0.838928  0.762214  0.256567  0.874984  0.396890  0.600915   
1  0.649959  0.205741  0.174359  0.349932  0.758043  0.092794  0.180718   

         21        22        23        24        25        26        27  \
0  0.238923  0.501682  0.259952  0.007046  0.794067  0.392352  0.296982   
1  0.867870  0.861766  0.538408  0.277828  0.418462  0.528816  0.006187   

         28        29        30        31        32        33        34  \
0  0.242004  0.647488  0.406277  0.239739  0.086527  0.661979  0.703148   
1  0.184497  0.260851  0.599812  0.064739  0.736251  0.805404  0.899075   

         35        36        37        38        39  
0  0.592287  0.350457  0.372377  0.246324  0.342693  
1  0.785853  0.289017  0.504370  0.770860  0.416821

Both the above methods are quite similar. Although, in cases where we need to set multiple values at once, the first method is cleaner as it allows us to set everything in one-liner code.

pd.set_option("display.max_columns", 40, "display.max_rows", 50)

For example, above we have set both the max_columns and max_rows together. You can also use None instead of any integer value, in that case it will show all rows and columns.

Expand display to show all columns using pandas.option_context() function

Note that both the above methods change these default values at the global level, meaning, post that the changes would be reflected in all the display commands. But in case, where we just want to change these settings for a single run, the option_context() function from pandas comes in handy.

# using option_context 
with pd.option_context('display.max_columns', None):
    print (df)

Inside thw with block, if we print DataFrame, then it will print all columns. Let’s see the complete example,

# importing library 
import numpy as np
import pandas as pd

# random DataFrame
df = pd.DataFrame(np.random.random(80).reshape(2, 40))

# using option_context 
with pd.option_context('display.max_columns', None):
    print (df)

Output

        0         1         2         3        4         5         6   \
0  0.632569  0.563294  0.473282  0.625990  0.88394  0.969119  0.571010   
1  0.790676  0.958089  0.908703  0.553424  0.89150  0.562219  0.407857   

         7         8         9         10        11        12        13  \
0  0.379490  0.428773  0.664126  0.263777  0.518166  0.195164  0.222407   
1  0.206793  0.773425  0.542391  0.892428  0.934983  0.053018  0.735291   

         14        15        16        17        18        19        20  \
0  0.275035  0.282219  0.138600  0.509403  0.121989  0.977795  0.950575   
1  0.794428  0.758629  0.210019  0.625313  0.201312  0.323290  0.281961   

         21        22        23        24        25        26        27  \
0  0.179377  0.663175  0.602607  0.409337  0.170292  0.933000  0.876569   
1  0.819548  0.909461  0.709151  0.575640  0.208872  0.788984  0.119483   

         28        29        30        31        32        33        34  \
0  0.235779  0.736269  0.546753  0.742913  0.338967  0.652793  0.841542   
1  0.666341  0.442340  0.302817  0.594159  0.975746  0.303037  0.093311   

         35        36        37        38        39  
0  0.250550  0.761866  0.140867  0.522595  0.936228  
1  0.140261  0.623021  0.488438  0.880718  0.149635

In case, we print any other DataFrame outside the with block, it will continue using the default settings.

Summary

Great, you made it! In this article, we have discussed multiple ways to the output display to see more columns of a Pandas DataFrame.

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