How to Apply the If-Else Condition in a Pandas DataFrame

1. Use DataFrame.loc[] to Apply the if-else Condition in a Pandas DataFrame in Python
2. Use DataFrame.apply() to Apply the if-else Condition in a Pandas DataFrame in Python
3. Use NumPy.select() to Apply the if-else Condition in a Pandas DataFrame in Python
4. Use lambda With apply() to Apply the if-else Condition in a Pandas DataFrame in Python

Pandas is an open-source data analysis library in Python. It provides many built-in methods to perform operations on numerical data.

In some cases, we want to apply the if-else conditions on a Pandas dataframe to filter the records or perform computations according to some conditions. Python provides many ways to use if-else on a Pandas dataframe.

Use DataFrame.loc[] to Apply the if-else Condition in a Pandas DataFrame in Python

loc[] is a property of the Pandas data frame used to select or filter a group of rows or columns. In the following example, we will employ this property to filter the records that meet a given condition.

Here, we have a Pandas data frame consisting of the students’ data. Using loc[], we can only apply a single condition at a time.

We will filter those students having marks greater than or equal to 60 in the first condition and assign their result as Pass in the new column Result. Similarly, we will set Fail for the rest of the student’s results in another condition.

Example Code:

# Python 3.x
import pandas as pd

df = pd.DataFrame(
    {"Name": ["Robert", "Sam", "Alia", "Jhon", "Smith"], "Marks": [60, 41, 79, 51, 88]}
)
display(df)
df.loc[df["Marks"] >= 60, "Result"] = "Pass"
df.loc[df["Marks"] < 60, "Result"] = "Fail"
display(df)

Output:

Use DataFrame.apply() to Apply the if-else Condition in a Pandas DataFrame in Python

The apply() method uses the data frame’s axis (row or column) to apply a function. We can make our defined function that consists of if-else conditions and apply it to the Pandas dataframe.

Here, we have defined a function assign_Result() and applied it to the Marks column. The function consists of if-else conditions that assign the result based on the Marks and invoke this for every column row.

Example Code:

# Python 3.x
import pandas as pd

df = pd.DataFrame(
    {"Name": ["Robert", "Sam", "Alia", "Jhon", "Smith"], "Marks": [60, 41, 79, 51, 88]}
)
display(df)


def assign_Result(marks):
    if marks >= 60:
        result = "Pass"
    else:
        result = "Fail"
    return result


df["Result"] = df["Marks"].apply(assign_Result)
display(df)

Output:

Use NumPy.select() to Apply the if-else Condition in a Pandas DataFrame in Python

We can define multiple conditions for a column in a list and their corresponding values in another list if the condition is True. The select() method takes the list of conditions and their corresponding list of values as arguments and assigns them to the Result column.

Example Code:

# Python 3.x
import pandas as pd
import numpy as np

df = pd.DataFrame(
    {"Name": ["Robert", "Sam", "Alia", "Jhon", "Smith"], "Marks": [60, 41, 79, 51, 88]}
)
display(df)
conditions = [(df["Marks"] >= 60), (df["Marks"] < 60)]
result = ["Pass", "Fail"]
df["Result"] = np.select(conditions, result)
display(df)

Output:

Use lambda With apply() to Apply the if-else Condition in a Pandas DataFrame in Python

A lambda is a small anonymous function consisting of a single expression. We will use lambda with apply() on the Marks column.

The x contains the marks in the lambda expression. We applied the if-else condition to the x and assigned the result accordingly in the Result column.

Example Code:

# Python 3.x
import pandas as pd
import numpy as np

df = pd.DataFrame(
    {"Name": ["Robert", "Sam", "Alia", "Jhon", "Smith"], "Marks": [60, 41, 79, 51, 88]}
)
display(df)
df["Result"] = df["Marks"].apply(lambda x: "Pass" if x >= 60 else "Fail")
display(df)

Output: