Are you tired of manually handling multiple DataFrames and adding new columns one by one? Do you want to learn a more efficient way to process your data? Look no further! In this article, we’ll explore how to loop over enumerated DataFrames and add new columns using Python and the popular pandas library. Buckle up, and let’s dive into the world of data manipulation!

When working with large datasets, it’s common to have multiple DataFrames that require similar processing. Enumerated DataFrames allow you to iterate over these frames while keeping track of their indices, making it easier to perform operations on each frame individually. In this article, we’ll focus on adding a new column to each DataFrame, but the concepts apply to various other tasks, such as data cleaning, filtering, and aggregation.

• Python 3.x installed on your machine
• The pandas library (you can install it using pip install pandas)
• A basic understanding of Python and pandas

Before we dive into the code, make sure you have pandas installed and imported in your Python environment. Create a new Python file or open an existing one, and add the following line:

import pandas as pd

To demonstrate the concept, let’s create three sample DataFrames:

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})
df3 = pd.DataFrame({'A': [13, 14, 15], 'B': [16, 17, 18]})

dfs = [df1, df2, df3]

We’ve created three DataFrames, df1, df2, and df3, each with two columns, A and B. We’ve also stored them in a list called dfs.

Now, let’s loop over the DataFrames using the enumerate function, which returns both the index and value of each item in the list:

for i, df in enumerate(dfs):
    print(f"DataFrame {i+1}:")
    print(df)
    print()

This code will output each DataFrame with its corresponding index (starting from 1). We’ve used the f-string formatting to create a readable output.

Now that we can loop over the DataFrames, let’s add a new column to each one. We’ll create a new column called C with values calculated based on the existing columns A and B.

for i, df in enumerate(dfs):
    df['C'] = df['A'] * df['B']
    print(f"DataFrame {i+1} with new column 'C':")
    print(df)
    print()

In this example, we’re multiplying the values in columns A and B to create the new column C. The resulting DataFrames will have the new column added.

Let’s consider a more practical scenario. Suppose we have three DataFrames containing sales data for different regions:

df1 = pd.DataFrame({'Region': ['North', 'North', 'North'], 
                    'Product': ['A', 'B', 'C'], 
                    'Sales': [100, 200, 300]})

df2 = pd.DataFrame({'Region': ['South', 'South', 'South'], 
                    'Product': ['A', 'B', 'C'], 
                    'Sales': [400, 500, 600]})

df3 = pd.DataFrame({'Region': ['East', 'East', 'East'], 
                    'Product': ['A', 'B', 'C'], 
                    'Sales': [700, 800, 900]})

dfs = [df1, df2, df3]

We want to add a new column called Total_Sales that calculates the total sales for each region. We can use the same approach:

for i, df in enumerate(dfs):
    df['Total_Sales'] = df['Sales'].sum()
    print(f"DataFrame {i+1} with new column 'Total_Sales':")
    print(df)
    print()

The resulting DataFrames will have the new column Total_Sales added, containing the total sales for each region.

In this article, we’ve demonstrated how to loop over enumerated DataFrames and add new columns using Python and pandas. By using the enumerate function, we can iterate over multiple DataFrames while keeping track of their indices, making it easier to perform operations on each frame individually. This technique can be applied to various data manipulation tasks, such as data cleaning, filtering, and aggregation.

Remember to practice and experiment with different scenarios to master the art of looping over enumerated DataFrames and adding new columns. Happy coding!

Keyword	Description
Loop over enumerated DataFrames	Iterate over multiple DataFrames while keeping track of their indices
Adding new columns	Create new columns in each DataFrame based on existing data
pandas	Python library for data manipulation and analysis
enumerate	Function that returns both the index and value of each item in a list

By following the instructions in this article, you should now be able to loop over enumerated DataFrames and add new columns with confidence. Remember to share your thoughts and questions in the comments section below!

Frequently Asked Question

Get ready to loop and conquer! Here are some frequently asked questions about looping over enumerated DataFrames and adding new columns.