Introduction to Pandas

Duration: 5 min

This module introduces you to the Pandas library, a powerful and flexible tool for data manipulation and analysis. You will learn how to create and manipulate DataFrames, perform exploratory data analysis (EDA), clean data, and visualize it effectively. Understanding Pandas is crucial for any data scientist as it forms the backbone of data handling in Python.

Creating and Understanding DataFrames

Pandas DataFrames are two-dimensional, size-mutable, and heterogeneous tabular data structures with labeled axes (rows and columns). They are the most commonly used Pandas objects. DataFrames can contain any type of data, including integers, floats, and strings, and are incredibly versatile for data manipulation and analysis.

import pandas as pd

# Creating a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# Displaying the DataFrame
print(df)

Try it in Google Colab:

     Name  Age
0   Alice   25
1     Bob   30
2  Charlie   35

Basic DataFrame Operations

Once you have a DataFrame, you can perform various operations such as selecting columns, filtering rows, and aggregating data. These operations are essential for data cleaning and preparation, which are critical steps in any data science workflow.

import pandas as pd

# Creating a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'Score': [88, 92, 85]}
df = pd.DataFrame(data)

# Selecting a column
print(df['Name'])

# Filtering rows
filtered_df = df[df['Age'] > 28]
print(filtered_df)

# Aggregating data
average_score = df['Score'].mean()
print('Average Score:', average_score)

💡 Tip: Always check for missing values in your DataFrame before performing any operations. Use df.isnull().sum() to get a summary of missing values in each column.

Key Concepts

Check Your Understanding