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This tutorial demonstrates methods to check if a column in a Pandas Dataframe exists in Python. We will use the INand NOT INoperators that can be used to perform this operation in Python.

Check if a column exists in Pandas using INthe operator

Dataframe is an arrangement that holds two-dimensional data and its corresponding labels. We can dataframe.columnfind the column labels using the column attribute.

To ensure that the column exists, we use INthe expression. However, before we begin, we need to form a dummy DataFrame in Pandas to use the above technique.

Here, we have created a DataFrame of student performance with column names Name, , Promotedand Marks.

import pandas as pd
import numpy as np

# Creating dataframe
df = pd.DataFrame()
# Adding columns to the dataframe
df["Name"] = ["John", "Doe", "Bill"]
df["Promoted"] = [True, False, True]
df["Marks"] = [82, 38, 63]
# Getting the dataframe as an output
print(df)

The code gives the following output.

   Name  Promoted  Marks
0  John      True     82
1   Doe     False     38
2  Bill      True     63

Once the DataFrame is prepared, we can check whether the DataFrame contains items or is empty by writing the code given below. For this, we can use two methods.

We can use the function present in Pandas df.emptyor we can len(df.index)check the length of the DataFrame using .

We have used Pandas attributes in the following examples df.empty.

if df.empty:
    print("DataFrame is empty!")
else:
    print("Not empty!")

Since we have inserted the data into the columns, the output must be Not empty!.

Not empty!

Now, let’s move on to INchecking if a column in a Pandas DataFrame exists using the check_if method. See the code below to see this function in action.

if "Promoted" in df:
    print("Yes, it does exist.")
else:
    print("No, it does not exist.")

The code gives the following output.

Yes, it does exist.

For greater clarity, this can also be written as if 'Promoted' in df.columns:, instead of just df.

Check if a column exists in Pandas using NOT INthe operator

Let's see how we can use NOT INthe property to perform the same operation. It operates in the opposite way, and because of the negation added to the property, the output is inverted.

This is a sample working of the property given below NOT IN.

if "Promoted" not in df.columns:
    print("Yes, it does not exist.")
else:
    print("No, it does exist.")

The code gives the following output.

No, it does exist.

We have seen how to do this for a single column in a DataFrame. Pandas also enables users to examine multiple columns in a DataFrame.

This helps in completing the task quickly and helps in sorting multiple columns at the same time.

Below is the code snippet to inspect multiple columns in a Pandas DataFrame.

if set(["Name", "Promoted"]).issubset(df.columns):
    print("Yes, all of them exist.")
else:
    print("No")

The code gives the following output.

Yes, all of them exist.

set([])It can also be constructed using curly braces.

if not {"Name", "Promoted"}.issubset(df.columns):
    print("Yes")
else:
    print("No")

The output will be:

No

These are the possible methods to check one or more columns in the data. Also, we can perform these functions on ready-made data instead of dummy data.

We just need read_csvto import the CSV file using the Python Pandas module through the import method. If you are using Google Colab, import google.colabthe filesmodule from to upload the data file from your personal system at runtime.