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How to Show All Column Names on a Large Pandas DataFrame

In this blog, you may have come across scenarios as a data scientist or software engineer where handling a sizable pandas dataframe with numerous columns becomes necessary. Managing such a dataframe can pose challenges, particularly in instances where the column names are challenging to observe, especially when the dataframe’s width prevents the display of all columns.
By Saturn Cloud | | Miscellaneous | Updated:

As a data scientist or software engineer, you have probably encountered a situation where you need to work with a large pandas dataframe with many columns. When working with such a dataframe, it can be difficult to view all the column names, especially if the columns are not displayed due to the width of the dataframe.

In this blog post, we will explore how to show all column names on a large pandas dataframe using simple and efficient methods.

Table of Contents

  1. What is Pandas?
  2. The Challenge
  3. Method 1: Using the pd.options.display.max_columns Parameter
  4. Method 2: Using the DataFrame.columns Attribute
  5. Method 3: Using the DataFrame.info() Method
  6. Conclusion

What is Pandas?

Pandas is a popular data analysis library in Python that provides data structures and tools for data manipulation and analysis. It is widely used in data science and machine learning projects.

One of the core data structures in Pandas is the DataFrame, which is a two-dimensional table-like data structure with rows and columns. A DataFrame is a powerful tool that allows data scientists to work with large datasets.

The Challenge of Displaying All Column Names on a Large Pandas DataFrame

When working with a large pandas dataframe, it can be challenging to view all column names if the columns are not displayed due to the width of the dataframe. By default, the pandas dataframe shows only a subset of the columns, and the rest are truncated.

This can be frustrating, especially when you need to work with a large dataset with many columns. Fortunately, there are several ways to show all column names on a large pandas dataframe.

Let’s say we have a random DataFrame that has 1000 rows and 50 columns. When you display it, the output will be truncated as shown below:

# Create a large DataFrame with 1000 rows and 50 columns
df = pd.DataFrame(np.random.rand(1000, 50), columns=[f'col_{i}' for i in range(50)])
print(df)

Output:

        col_0     col_1     col_2     col_3     col_4  ...    col_45  \
0    0.964616  0.236512  0.714823  0.451715  0.161998  ...  0.759156   
1    0.160201  0.593552  0.410760  0.949468  0.687854  ...  0.165778   
2    0.085469  0.677406  0.996336  0.156234  0.038921  ...  0.702069   
3    0.404221  0.524581  0.541092  0.525515  0.541101  ...  0.712994   
4    0.765372  0.294593  0.518726  0.368666  0.102627  ...  0.794924   
..        ...       ...       ...       ...       ...  ...       ...   
995  0.318816  0.607031  0.961981  0.449906  0.605990  ...  0.413964   
996  0.408033  0.521799  0.816650  0.950018  0.154797  ...  0.386669   
997  0.229494  0.265283  0.581489  0.521771  0.044155  ...  0.906805   
998  0.916877  0.229309  0.485294  0.711102  0.299946  ...  0.611196   
999  0.947845  0.463052  0.695537  0.852888  0.826098  ...  0.858327   

       col_46    col_47    col_48    col_49  
0    0.236325  0.093809  0.615526  0.078678  
1    0.761265  0.657471  0.933473  0.139356  
2    0.016272  0.031140  0.657813  0.434276  
3    0.268237  0.308290  0.945084  0.224147  
4    0.369746  0.427940  0.356022  0.505767  
..        ...       ...       ...       ...  
995  0.935928  0.686979  0.349809  0.795637  
996  0.466581  0.812713  0.375606  0.675781  
997  0.407140  0.464321  0.876408  0.989232  
998  0.353962  0.656394  0.675275  0.821299  
999  0.688065  0.678142  0.496741  0.225537  

[1000 rows x 50 columns]

Method 1: Using the pd.options.display.max_columns Parameter

The simplest way to display all column names on a large pandas dataframe is to use the pd.options.display.max_columns parameter. This parameter controls the maximum number of columns that Pandas displays.

To set the pd.options.display.max_columns parameter to show all columns, you can use the following code:

import pandas as pd
pd.options.display.max_columns = None
print(df)

This code sets the pd.options.display.max_columns parameter to None, which means that Pandas will display all columns.

Output:

        col_0     col_1     col_2     col_3     col_4     col_5     col_6  \
0    0.153717  0.482612  0.831060  0.077882  0.289472  0.467470  0.965212   
1    0.753398  0.592403  0.256186  0.385437  0.974246  0.311077  0.298807   
2    0.494298  0.667306  0.099722  0.329681  0.334417  0.686789  0.743527   
3    0.706999  0.923867  0.341464  0.815451  0.190278  0.354622  0.781376   
4    0.161814  0.847634  0.162079  0.116882  0.234656  0.774938  0.567430   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.343012  0.916642  0.206359  0.487503  0.666251  0.530213  0.673208   
996  0.386813  0.794457  0.689360  0.815614  0.956183  0.521029  0.675205   
997  0.061997  0.998701  0.795018  0.955766  0.087892  0.752429  0.078475   
998  0.586326  0.689030  0.133247  0.785062  0.929016  0.127573  0.860095   
999  0.152820  0.722834  0.388162  0.174324  0.070073  0.261502  0.555507   

        col_7     col_8     col_9    col_10    col_11    col_12    col_13  \
0    0.064808  0.276789  0.977565  0.540970  0.581865  0.846636  0.446658   
1    0.902976  0.551107  0.496794  0.282331  0.689882  0.109611  0.716832   
2    0.774651  0.322931  0.263034  0.690746  0.818504  0.526758  0.528841   
3    0.037405  0.818513  0.689578  0.102399  0.795942  0.021479  0.770433   
4    0.180333  0.119362  0.261067  0.067885  0.444463  0.668978  0.547032   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.867552  0.204308  0.157197  0.610779  0.058301  0.548629  0.105525   
996  0.378360  0.795320  0.560457  0.454346  0.059251  0.940875  0.471719   
997  0.606062  0.094666  0.286443  0.274784  0.552347  0.534680  0.028932   
998  0.095685  0.608516  0.466998  0.136260  0.543215  0.530210  0.201358   
999  0.672376  0.288557  0.844070  0.219242  0.594211  0.774128  0.758904   

       col_14    col_15    col_16    col_17    col_18    col_19    col_20  \
0    0.157853  0.858069  0.691384  0.097937  0.529731  0.335217  0.516953   
1    0.796044  0.483558  0.129574  0.311156  0.011691  0.989641  0.034566   
2    0.385424  0.301513  0.535031  0.960461  0.491448  0.317463  0.919824   
3    0.618826  0.395679  0.004772  0.667339  0.200393  0.922259  0.758442   
4    0.948167  0.797001  0.313806  0.195133  0.800427  0.063825  0.287324   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.336403  0.160305  0.902384  0.333265  0.031580  0.030954  0.722896   
996  0.444248  0.720805  0.668845  0.920229  0.892215  0.866361  0.763199   
997  0.649125  0.644163  0.309692  0.097460  0.993488  0.885684  0.193746   
998  0.872590  0.123234  0.500231  0.658634  0.752794  0.594370  0.554094   
999  0.343469  0.606472  0.724897  0.150611  0.747608  0.049472  0.924962   

       col_21    col_22    col_23    col_24    col_25    col_26    col_27  \
0    0.869054  0.115983  0.781765  0.456945  0.998403  0.546874  0.128295   
1    0.262757  0.124925  0.263503  0.417877  0.804679  0.355874  0.357750   
2    0.486958  0.763892  0.594275  0.786688  0.064658  0.293554  0.691706   
3    0.236898  0.022522  0.186116  0.192795  0.414632  0.256097  0.840114   
4    0.812371  0.394920  0.376737  0.397080  0.976586  0.868973  0.041196   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.541643  0.505638  0.474656  0.642070  0.654075  0.372817  0.823473   
996  0.584527  0.268562  0.694579  0.544153  0.399260  0.260127  0.440994   
997  0.360960  0.006208  0.429259  0.802490  0.405759  0.393156  0.155887   
998  0.891960  0.974717  0.519034  0.354690  0.642086  0.106520  0.358574   
999  0.676138  0.422191  0.531630  0.053351  0.436653  0.850496  0.955167   

       col_28    col_29    col_30    col_31    col_32    col_33    col_34  \
0    0.612715  0.289769  0.262615  0.397411  0.973778  0.229357  0.027246   
1    0.836455  0.131338  0.460692  0.874707  0.497715  0.144222  0.431756   
2    0.526487  0.421510  0.772382  0.170965  0.884607  0.877887  0.090390   
3    0.406056  0.871964  0.246394  0.061501  0.548550  0.280167  0.114024   
4    0.167982  0.556702  0.453225  0.321289  0.086918  0.142730  0.509901   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.313636  0.252033  0.352655  0.513922  0.712625  0.676948  0.965147   
996  0.380124  0.677867  0.144330  0.297111  0.402275  0.753710  0.513965   
997  0.732346  0.041922  0.263013  0.637607  0.554263  0.112356  0.069624   
998  0.415643  0.764130  0.018093  0.536630  0.496172  0.085398  0.795797   
999  0.851988  0.241698  0.076521  0.376431  0.204558  0.003570  0.490827   

       col_35    col_36    col_37    col_38    col_39    col_40    col_41  \
0    0.969778  0.890687  0.252725  0.958133  0.955823  0.694491  0.357555   
1    0.211286  0.738304  0.956136  0.332277  0.709432  0.977291  0.338518   
2    0.382878  0.341724  0.505131  0.969127  0.254081  0.420203  0.873372   
3    0.849604  0.587547  0.619855  0.879578  0.504818  0.169264  0.320221   
4    0.368632  0.134104  0.901714  0.921227  0.006675  0.737873  0.873520   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.332006  0.589716  0.286749  0.735156  0.254230  0.766034  0.187530   
996  0.541045  0.007886  0.566133  0.717299  0.310796  0.893926  0.006056   
997  0.986283  0.692556  0.537410  0.162068  0.461754  0.984682  0.936976   
998  0.466028  0.485508  0.285970  0.865334  0.254768  0.717602  0.198167   
999  0.907145  0.787205  0.398355  0.221750  0.154364  0.601802  0.580466   

       col_42    col_43    col_44    col_45    col_46    col_47    col_48  \
0    0.664041  0.422512  0.646385  0.107367  0.442005  0.570606  0.604091   
1    0.298868  0.370593  0.365926  0.911279  0.809941  0.902058  0.939920   
2    0.499322  0.532290  0.278293  0.933268  0.802350  0.422360  0.113506   
3    0.393836  0.680546  0.361885  0.549631  0.259546  0.476253  0.508761   
4    0.470585  0.548421  0.456551  0.032836  0.204966  0.718750  0.309230   
..        ...       ...       ...       ...       ...       ...       ...   
995  0.231127  0.344774  0.217120  0.695439  0.525060  0.617114  0.618750   
996  0.128004  0.369993  0.885262  0.665682  0.589731  0.852448  0.528447   
997  0.640847  0.470862  0.713722  0.246703  0.030507  0.944230  0.972758   
998  0.061010  0.991973  0.567517  0.328101  0.715328  0.974949  0.646360   
999  0.618688  0.601053  0.491485  0.882420  0.383666  0.246607  0.338111   

       col_49  
0    0.610033  
1    0.851141  
2    0.944832  
3    0.836301  
4    0.973903  
..        ...  
995  0.947736  
996  0.366277  
997  0.205147  
998  0.672895  
999  0.459406  

[1000 rows x 50 columns]

Method 2: Using the DataFrame.columns Attribute

Another way to display all column names on a large pandas dataframe is to use the DataFrame.columns attribute. This attribute returns a list of all column names in the dataframe.

To use this method, you can simply call the DataFrame.columns attribute on your dataframe, as shown below:

import pandas as pd
df = pd.read_csv('large_dataframe.csv')
print(df.columns)

This code will print all column names in the df dataframe.

Output:

Index(['col_0', 'col_1', 'col_2', 'col_3', 'col_4', 'col_5', 'col_6', 'col_7',
       'col_8', 'col_9', 'col_10', 'col_11', 'col_12', 'col_13', 'col_14',
       'col_15', 'col_16', 'col_17', 'col_18', 'col_19', 'col_20', 'col_21',
       'col_22', 'col_23', 'col_24', 'col_25', 'col_26', 'col_27', 'col_28',
       'col_29', 'col_30', 'col_31', 'col_32', 'col_33', 'col_34', 'col_35',
       'col_36', 'col_37', 'col_38', 'col_39', 'col_40', 'col_41', 'col_42',
       'col_43', 'col_44', 'col_45', 'col_46', 'col_47', 'col_48', 'col_49'],
      dtype='object')

Method 3: Using the DataFrame.info() Method

The DataFrame.info() method is another way to display all column names on a large pandas dataframe. This method provides a summary of the dataframe, including the column names, data types, and memory usage.

To use this method, you can call the DataFrame.info() method on your dataframe, as shown below:

import pandas as pd
df = pd.read_csv('large_dataframe.csv')
df.info()

This code will print a summary of the df dataframe, including all column names.

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 50 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   col_0   1000 non-null   float64
 1   col_1   1000 non-null   float64
 2   col_2   1000 non-null   float64
 3   col_3   1000 non-null   float64
 4   col_4   1000 non-null   float64
 5   col_5   1000 non-null   float64
 6   col_6   1000 non-null   float64
 7   col_7   1000 non-null   float64
 8   col_8   1000 non-null   float64
 9   col_9   1000 non-null   float64
 10  col_10  1000 non-null   float64
 11  col_11  1000 non-null   float64
 12  col_12  1000 non-null   float64
 13  col_13  1000 non-null   float64
 14  col_14  1000 non-null   float64
 15  col_15  1000 non-null   float64
 16  col_16  1000 non-null   float64
 17  col_17  1000 non-null   float64
 18  col_18  1000 non-null   float64
 19  col_19  1000 non-null   float64
 20  col_20  1000 non-null   float64
 21  col_21  1000 non-null   float64
 22  col_22  1000 non-null   float64
 23  col_23  1000 non-null   float64
 24  col_24  1000 non-null   float64
 25  col_25  1000 non-null   float64
 26  col_26  1000 non-null   float64
 27  col_27  1000 non-null   float64
 28  col_28  1000 non-null   float64
 29  col_29  1000 non-null   float64
 30  col_30  1000 non-null   float64
 31  col_31  1000 non-null   float64
 32  col_32  1000 non-null   float64
 33  col_33  1000 non-null   float64
 34  col_34  1000 non-null   float64
 35  col_35  1000 non-null   float64
 36  col_36  1000 non-null   float64
 37  col_37  1000 non-null   float64
 38  col_38  1000 non-null   float64
 39  col_39  1000 non-null   float64
 40  col_40  1000 non-null   float64
 41  col_41  1000 non-null   float64
 42  col_42  1000 non-null   float64
 43  col_43  1000 non-null   float64
 44  col_44  1000 non-null   float64
 45  col_45  1000 non-null   float64
 46  col_46  1000 non-null   float64
 47  col_47  1000 non-null   float64
 48  col_48  1000 non-null   float64
 49  col_49  1000 non-null   float64
dtypes: float64(50)
memory usage: 390.8 KB

Conclusion

In this blog post, we have explored different methods of displaying all column names on a large pandas dataframe. By using the pd.options.display.max_columns parameter, the DataFrame.columns attribute, and the DataFrame.info() method, data scientists and software engineers can easily view all column names in a dataframe without truncation.

When working with large datasets, it is essential to have a good understanding of the tools and techniques available to manipulate and analyze the data. Pandas is a powerful library that provides data scientists with a rich set of tools for working with data.

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