When working with data in Python, it is often necessary to manipulate columns in a list. There are several ways to achieve this, but in this article, we will explore three different approaches to changing a list of columns to different data types in a more pythonic or pandorable way.

Approach 1: Using a for loop

One way to change a list of columns to different data types is by using a for loop. This approach allows us to iterate over each column in the list and apply the desired data type conversion.

# Sample code
columns = ['column1', 'column2', 'column3']
data_types = [int, float, str]

for i in range(len(columns)):
    df[columns[i]] = df[columns[i]].astype(data_types[i])

In this code snippet, we have a list of columns and a corresponding list of data types. We iterate over each column using the range function and access the column in the DataFrame using the index. We then use the astype method to convert the column to the desired data type.

Approach 2: Using list comprehension

List comprehension is a concise way to create lists in Python. It can also be used to change a list of columns to different data types.

# Sample code
columns = ['column1', 'column2', 'column3']
data_types = [int, float, str]

df = pd.DataFrame({column: df[column].astype(data_type) for column, data_type in zip(columns, data_types)})

In this code snippet, we use list comprehension to create a new DataFrame with the desired data types for each column. We iterate over the columns and data types simultaneously using the zip function and create a dictionary comprehension. The keys of the dictionary are the column names, and the values are the columns with the desired data types.

Approach 3: Using the pandas apply method

The pandas apply method allows us to apply a function to each element of a DataFrame. We can use this method to change a list of columns to different data types.

# Sample code
columns = ['column1', 'column2', 'column3']
data_types = [int, float, str]

df[columns] = df[columns].apply(lambda x: x.astype(data_types))

In this code snippet, we use the apply method to apply the astype function to each column in the list. We use a lambda function to pass the astype function with the desired data type as an argument.

After exploring these three different approaches, it is clear that the second approach, using list comprehension, is the most pythonic and pandorable way to change a list of columns to different data types. It is concise, readable, and takes advantage of the built-in functionality of list comprehension in Python.