Pandas Function Application: From Data to Insights in No Time

The apply function is a built-in Pandas function that allows you to apply a function to each element of a Pandas Series or DataFrame. It is a powerful tool for data manipulation and can be used to perform a variety of operations on data.

Here is an example of how to use the apply function to each element of a Pandas Series:

import pandas as pd

# Create a Pandas Series
s = pd.Series([1, 2, 3, 4, 5])

# Define a function to be applied to each element
def double(x):
    return x * 2

# Apply the function to each element of the Series
s_doubled = s.apply(double)

# Print the result
print(s_doubled)

The output of this code will be a new Pandas Series with each element doubled:

0    0
1    4
2    2
3    8
4    4
dtype: int64

You can also use the apply function to each row or column of a Pandas DataFrame. For example:

import pandas as pd

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

# Define a function to be applied to each row
def add_columns(row):
    return row['A'] + row['B']

# Apply the function to each row of the DataFrame
df['Sum'] = df.apply(add_columns, axis=1)

# Print the result
print(df)

The output of this code will be a new Pandas DataFrame with a new column Sum that contains the sum of the values in columns A and B for each row:

   A  B  Sum
0  1  4    5
1  2  5    7
2  3  6    9

The apply function is a useful tool for applying custom functions to data in Pandas, and can be used to perform a wide variety of operations on data.