10 Essential Pandas Hacks Every Python Data Analyst Should Know

As a data analyst, I spend a significant portion of my time wrangling data and performing various data manipulations. Pandas, the Python library for data manipulation and analysis, has been my trusty companion throughout my data analysis journey. Over the years, I’ve discovered several Pandas hacks that have made my life much easier and my code more efficient.

In this article, I’ll share ten essential Pandas hacks that every Python data analyst should know.

1. Rename Columns

import pandas as pd

# Create a sample DataFrame
data = {'old_name_1': [1, 2, 3],
        'old_name_2': [4, 5, 6]}
df = pd.DataFrame(data)
# Rename columns
df.rename(columns={'old_name_1': 'new_name_1', 'old_name_2': 'new_name_2'}, inplace=True)

Sometimes, you’ll work with datasets where column names aren’t descriptive. You can easily rename columns using the rename method.

2. Filter Rows by Condition

# Filter rows where a condition is met
filtered_df = df[df['column_name'] > 3]

Filtering rows based on a condition is a common operation. It allows you to select only the rows that meet specific criteria.

3. Handling Missing Data