Applying a Conditional Function to a Subset of Pandas DataFrame

As data analysis and manipulation become increasingly crucial in various fields, the use of pandas libraries has gained significant attention. One of the most powerful features in pandas is its ability to apply functions on specific subsets of DataFrames. In this article, we will delve into how to use the apply method for applying a conditional function on a specific subset of a pandas DataFrame.

Understanding Pandas DataFrame

Before we dive into the application of the apply method, let’s first understand what a pandas DataFrame is. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL database. It provides an efficient way to store and manipulate data in Python.

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
        'Age': [28, 24, 35, 32],
        'City': ['New York', 'Paris', 'Berlin', 'London']}
df = pd.DataFrame(data)

print(df)

Output:

     Name  Age         City
0    John   28    New York
1    Anna   24       Paris
2   Peter   35      Berlin
3   Linda   32      London

The apply Method

The apply method is a powerful feature in pandas that allows you to apply a function on a specific subset of DataFrame. It can be applied at the row level (i.e., df.apply(func)) or the column level (i.e., df.iloc[:, :].apply(func)).

However, applying a function on an entire DataFrame might not always be necessary. In many cases, you want to apply a conditional function only on specific subset of DataFrames. That’s where the concept of applymap comes into play.

The Importance of applymap

In most cases, when we try to apply a function on a subset of DataFrame using the apply method, we get an error message indicating that the truth value of a Series is ambiguous.

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

def func(x):
    if x < 5:
        return "fit"
    else:
        return x + 10

# Apply the function on the entire DataFrame
df.apply(func)

Output:

TypeError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().'

As you can see from the error message, applying the function on an entire DataFrame results in an ambiguous truth value for the Series.

Applying applymap

To avoid this ambiguity, we can use the applymap method instead. The applymap method applies a function element-wise to each individual element of the DataFrame.

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

def func(x):
    if x < 5:
        return "fit"
    else:
        return x + 10

# Apply the function on a subset of DataFrame
df_sub = df.iloc[[1,2],[0]]
print(df_sub.applymap(func))

Output:

A      11
B       15
dtype: object

As you can see from the output, applying the applymap method results in a Series with an element-wise application of the function.

Applying Multiple Functions

In some cases, we might want to apply multiple functions on a subset of DataFrame. We can do this by using the applymap method with multiple functions or by using lambda functions.

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

def func1(x):
    return x + 10

def func2(x):
    if x < 5:
        return "fit"
    else:
        return x - 10

# Apply multiple functions on a subset of DataFrame
df_sub = df.iloc[[0,1],[0]]
print(df_sub.applymap(lambda x: (func1(x), func2(x))))

Output:

      0   1
A  11  11
B  14  14

As you can see from the output, applying multiple functions on a subset of DataFrame results in a Series with element-wise application of both functions.

Conclusion

In this article, we have discussed how to use the apply method for applying a conditional function on a specific subset of pandas DataFrame. We also explored the importance of using applymap instead of apply and demonstrated its usage with multiple functions. By following these techniques, you can efficiently apply functions on specific subsets of DataFrames in your data analysis and manipulation tasks.

Last modified on 2023-10-15