Combining Lists into a DataFrame in Python

List comprehensions in Python are a concise and efficient way to manipulate lists. Combined with the pandas library, they can be a powerful tool for data analysis. This blog post will show you how to classify numbers by size and parity using list comprehensions, and then how to combine these lists into a DataFrame using pandas.

Classifying Numbers by Size and Parity

First, we'll classify the numbers from our original list based on whether they're small (less than 10), large (greater than or equal to 10), even, or odd:

old_list = [2, 9, 10, 15]
new_list = ['Small & Even' if i < 10 and i % 2 == 0 else 'Small & Odd' if i < 10 else 'Large & Even' if i % 2 == 0 else 'Large & Odd' for i in old_list]
print(new_list)  # Outputs: ['Small & Even', 'Small & Odd', 'Large & Even', 'Large & Odd']

Combining Lists into a DataFrame

Next, we'll combine the original list and the new list into a DataFrame using the pandas library. If you haven't already, you'll need to install pandas with pip:

pip install pandas

Then, you can create a DataFrame by calling the pandas DataFrame constructor and passing in a dictionary, where the keys are the column names and the values are the lists:

import pandas as pd

df = pd.DataFrame({'Original Number': old_list, 'Classification': new_list})
print(df)

This code will output:

   Original Number Classification
0                2   Small & Even
1                9     Small & Odd
2               10   Large & Even
3               15    Large & Odd

We now have a pandas DataFrame where the first column is the original numbers and the second column is their classification by size and parity. This is a simple example, but the same approach can be used to create complex DataFrames from multiple lists of data.