How to Apply a Function to Multiple Columns in a Pandas DataFrame?

Applying Functions to Multiple Columns of a Pandas Dataframe

Suppose we have a dataset in a Pandas dataframe with multiple columns, and we want to apply a custom function to two of those columns. This can be a common task in data manipulation and analysis. Here's a step-by-step guide to achieve this:

1. Define the Function:

Define a custom function that takes two inputs, representing the values from the two columns. This function should perform the desired operation on these inputs.

2. Apply the Function Using Lambda:

Pandas provides a lambda function that allows us to apply a function to each row of a dataframe. We can leverage this to apply our custom function to the selected columns.

The syntax for applying a function to multiple columns using lambda is:

df['new_column_name'] = df.apply(lambda x: your_function(x.column_1, x.column_2), axis=1)

Where:

• new_column_name is the name of the new column that will store the results of the function.
• your_function is the user-defined function that takes two inputs and returns the desired output.
• x represents each row of the dataframe, and x.column_1 and x.column_2 access the values from the specified columns.
• axis=1 indicates that the function is applied to each row, not each column.

3. Example:

Consider the following example dataframe:

df = pd.DataFrame({'ID':['1','2','3'], 'col_1': [0,2,3], 'col_2':[1,4,5]})

Suppose we want to create a new column called 'col_3' that contains a sublist of the original list mylist based on values in col_1 and col_2. We can define a function get_sublist as follows:

def get_sublist(sta, end):
    return ['a', 'b', 'c', 'd', 'e', 'f'][sta:end+1]

Now, we can apply this function using lambda as:

df['col_3'] = df.apply(lambda x: get_sublist(x.col_1, x.col_2), axis=1)

This creates a new column 'col_3' in the dataframe with the desired sublists.

4. Alternatives:

Using lambda is a concise and versatile approach for applying functions to multiple dataframe columns. However, if you prefer a more explicit way, you can also use the apply() method with a custom function that takes a Series as input. This approach involves defining a function that takes a single input representing a row and then manipulates that specific row as needed.

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