Introduction to Pandas

Pandas is a powerful Python library for data analysis and manipulation. It provides data structures such as DataFrame and Series that make it easy to clean, transform, and analyze data.

In this lesson, you’ll learn the basics of Pandas and how to use it for data analysis.

Installing Pandas

To install Pandas, use the following command in your terminal:

pip install pandas

Once installed, you can import Pandas in your Python scripts:

# Importing pandas
import pandas as pd

print("Pandas imported successfully")

What Is a DataFrame?

A DataFrame is a two-dimensional, tabular data structure in Pandas. It is similar to a spreadsheet or a SQL table.

You can create a DataFrame from a dictionary, list, or external file:

# Creating a DataFrame from a dictionary
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "London", "Paris"]
}
df = pd.DataFrame(data)
print(df)

Reading Data from Files

Pandas makes it easy to read data from external files, such as CSV, Excel, and JSON files:

# Reading data from a CSV file
df = pd.read_csv("data.csv")
print(df.head())  # Displays the first 5 rows

Inspecting a DataFrame

You can inspect the structure and content of a DataFrame using methods like head(), info(), and describe():

# Inspecting a DataFrame
df = pd.read_csv("data.csv")
print(df.info())  # Displays information about the DataFrame
print(df.describe())  # Provides statistical summaries

Filtering and Selecting Data

Pandas allows you to filter rows and select columns easily using conditions and indexing:

# Filtering and selecting data
filtered_df = df[df['Age'] > 30]  # Select rows where Age > 30
print(filtered_df[['Name', 'City']])  # Select specific columns

Data Cleaning

Pandas provides methods for cleaning data, such as handling missing values, renaming columns, and dropping duplicates:

# Handling missing values
df.fillna(0, inplace=True)  # Replace NaN values with 0

# Renaming columns
df.rename(columns={"Name": "Full Name"}, inplace=True)

# Dropping duplicates
df.drop_duplicates(inplace=True)
print(df)

Performing Aggregations

Pandas allows you to perform aggregations, such as summing, averaging, and grouping data:

# Grouping and aggregating data
grouped = df.groupby('City')['Age'].mean()
print(grouped)  # Displays average age for each city

Common Mistakes with Pandas

Here are some mistakes to avoid:

• Not handling missing values properly.
• Using inefficient loops instead of vectorized operations.
• Forgetting to specify inplace=True for operations that modify data.

What Did We Learn?

In this lesson, you learned:

• How to install and import Pandas.
• The structure of DataFrames and how to create them.
• How to read, clean, filter, and aggregate data using Pandas.

Great job! Let’s move to the next topic.