5 ways to apply an IF condition in Pandas DataFrame – Data to Fish (2024)

In this guide, you’ll see 5 different ways to apply an IF condition in Pandas DataFrame.

Specifically, you’ll see how to apply an IF condition for:

1. Set of numbers
2. Set of numbers andlambda
3. Strings
4. Strings and lambda
5. OR condition

Applying an IF condition in Pandas DataFrame

Let’s now review the following 5 cases:

(1) IF condition – Set of numbers

Suppose that you created a DataFrame in Python that has 10 numbers (from 1 to 10). You then want to apply the following IF conditions:

• If the number is equal or lower than 4, then assign the value of ‘True’
• Otherwise, if the number is greater than 4, then assign the value of ‘False’

This is the general structure that you may use to create the IF condition:

df.loc[df['column name'] condition, 'new column name'] = 'value if condition is met'

For our example, the Python code would look like this:

import pandas as pddata = {'set_of_numbers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}df = pd.DataFrame(data)df.loc[df['set_of_numbers'] <= 4, 'equal_or_lower_than_4?'] = 'True' df.loc[df['set_of_numbers'] > 4, 'equal_or_lower_than_4?'] = 'False' print(df)

Here is the result that you’ll get in Python:

 set_of_numbers equal_or_lower_than_4?0 1 True1 2 True2 3 True3 4 True4 5 False5 6 False6 7 False7 8 False8 9 False9 10 False

(2) IF condition – set of numbers andlambda

You’ll now see how to get the same results as in case 1 by using lambda, where the conditions are:

• If the number is equal or lower than 4, then assign the value of ‘True’
• Otherwise, if the number is greater than 4, then assign the value of ‘False’

Here is the generic structure that you may apply in Python:

df['new column name'] = df['column name'].apply(lambda x: 'value if condition is met' if x condition else 'value if condition is not met')

And for our example:

import pandas as pddata = {'set_of_numbers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}df = pd.DataFrame(data)df['equal_or_lower_than_4?'] = df['set_of_numbers'].apply(lambda x: 'True' if x <= 4 else 'False')print(df)

This is the result that you’ll get, which matches with case 1:

 set_of_numbers equal_or_lower_than_4?0 1 True1 2 True2 3 True3 4 True4 5 False5 6 False6 7 False7 8 False8 9 False9 10 False

(3) IF condition – strings

Now, let’s create a DataFrame that contains only strings/text with 4names: Jon, Bill, Maria and Emma.

The conditions are:

• If the name is equal to ‘Bill,’ then assign the value of ‘Match’
• Otherwise, if the name is not‘Bill,’ then assign the value of ‘Mismatch’

import pandas as pddata = {'first_name': ['Jon', 'Bill', 'Maria', 'Emma']}df = pd.DataFrame(data)df.loc[df['first_name'] == 'Bill', 'name_match'] = 'Match' df.loc[df['first_name'] != 'Bill', 'name_match'] = 'Mismatch' print(df)

Once you run the above Python code, you’ll see:

 first_name name_match0 Jon Mismatch1 Bill Match2 Maria Mismatch3 Emma Mismatch

(4) IF condition – strings and lambda

You’ll get the same results as in case 3 by using lambda:

import pandas as pddata = {'first_name': ['Jon', 'Bill', 'Maria', 'Emma']}df = pd.DataFrame(data)df['name_match'] = df['first_name'].apply(lambda x: 'Match' if x == 'Bill' else 'Mismatch')print(df)

And here is the output from Python:

 first_name name_match0 Jon Mismatch1 Bill Match2 Maria Mismatch3 Emma Mismatch

(5) IF condition with OR

Now let’s apply these conditions:

• If the name is‘Bill’or ‘Emma,’ then assign the value of ‘Match’
• Otherwise, if the name is neither ‘Bill’ nor ‘Emma,’ then assign the value of ‘Mismatch’

import pandas as pddata = {'first_name': ['Jon', 'Bill', 'Maria', 'Emma']}df = pd.DataFrame(data)df.loc[(df['first_name'] == 'Bill') | (df['first_name'] == 'Emma'), 'name_match'] = 'Match' df.loc[(df['first_name'] != 'Bill') & (df['first_name'] != 'Emma'), 'name_match'] = 'Mismatch' print(df)

Run the Python code, and you’ll get the following result:

 first_name name_match0 Jon Mismatch1 Bill Match2 Maria Mismatch3 Emma Match

Applying an IF condition under an existing DataFrame column

So far you have seen how to apply an IF condition by creating a new column.

Alternatively, you may store the results under an existing DataFrame column.

For example, let’s say that you created a DataFrame that has 12 numbers, where the last two numbers are zeros:

‘set_of_numbers’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0]

You may then apply the following IF conditions, and then store the results under the existing ‘set_of_numbers’ column:

• If the number is equal to 0, then change the value to 999
• If the number is equal to 5, then change the value to 555

import pandas as pddata = {'set_of_numbers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0]}df = pd.DataFrame(data)print(df)df.loc[df['set_of_numbers'] == 0, 'set_of_numbers'] = 999df.loc[df['set_of_numbers'] == 5, 'set_of_numbers'] = 555print(df)

Here are the before and after results, where the ‘5’ became ‘555’ and the 0’s became ‘999’ under the existing ‘set_of_numbers’ column:

BEFORE:

 set_of_numbers0 11 22 33 44 55 66 77 88 99 1010 011 0

AFTER:

 set_of_numbers0 11 22 33 44 5555 66 77 88 99 1010 99911 999

On another instance, you may have a DataFrame that contains NaN values. You can then apply an IF condition to replace those values with zeros, as in the example below:

import pandas as pdimport numpy as npdata = {'set_of_numbers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, np.nan, np.nan]}df = pd.DataFrame(data)print(df)df.loc[df['set_of_numbers'].isnull(), 'set_of_numbers'] = 0print(df)

Before you’ll see the NaN values, and after you’ll see the zero values:

BEFORE:

 set_of_numbers0 1.01 2.02 3.03 4.04 5.05 6.06 7.07 8.08 9.09 10.010 NaN11 NaN

AFTER:

 set_of_numbers0 1.01 2.02 3.03 4.04 5.05 6.06 7.07 8.08 9.09 10.010 0.011 0.0

Conclusion

You just saw how to apply an IF condition in Pandas DataFrame. There are indeed multiple ways to apply such a condition in Python. You can achieve the same results by using either lambda, or just by sticking with Pandas.

At the end, it boils down to working with the method that is best suited to your needs.

Finally, you may want to check the following external source for additional information about Pandas DataFrame.