Mastering Pandas in Python: Data Analysis and Manipulation Made Easy

Chapter 2: Introduction to Pandas and Data Structures

🔹 1. Introduction to Pandas

Pandas is one of the most powerful libraries in Python for data analysis and manipulation. It is specifically designed to handle structured data (such as tables, databases, and CSV files), and provides fast, flexible, and expressive data structures for working with time series, data frames, and heterogeneous data.

Pandas is widely used in fields like data science, machine learning, and financial analysis due to its ability to easily load, clean, and manipulate large datasets.

The two core data structures in Pandas are:

• Series (1D data structure)
• DataFrame (2D data structure)

These structures enable data scientists and analysts to manipulate and analyze data with just a few lines of code.

🔹 2. Installing Pandas

To install Pandas, you can use pip (Python's package installer):

pip install pandas

Once installed, you can import Pandas in your Python script or notebook:

import pandas as pd

🔹 3. Understanding Pandas Data Structures

✅ Series

A Series is a one-dimensional labeled array capable of holding data of any type (integers, strings, floats, Python objects, etc.). It is similar to a list or a column in a table.

✅ Example of a Series:

import pandas as pd

# Creating a Series from a list

data = [1, 2, 3, 4]

s = pd.Series(data)

print(s)

Output:

dtype: int64

Here, each item in the list is indexed with an integer value starting from 0.

Accessing elements in a Series:

# Accessing the first element

print(s[0])  # Output: 1

Setting custom indices:

# Create a Series with custom indices

s = pd.Series(data, index=['A', 'B', 'C', 'D'])

print(s)

Output:

dtype: int64

✅ DataFrame

A DataFrame is a two-dimensional data structure that holds tabular data in rows and columns. It can be seen as a collection of Series with a shared index, where each Series represents a column of data.

✅ Example of a DataFrame:

import pandas as pd

# Creating a DataFrame from a dictionary

data = {'Name': ['John', 'Alice', 'Bob'],

        'Age': [28, 24, 35],

        'City': ['New York', 'Los Angeles', 'Chicago']}

df = pd.DataFrame(data)

print(df)

Output:

In this case, the dictionary keys become the column names and the corresponding lists are the column values.

🔹 4. Basic Operations on Series and DataFrames

✅ Accessing Data in DataFrame

You can access individual columns or rows using the column name or row index.

Accessing Columns:

# Accessing a column as a Series

print(df['Name'])

Output:

Name: Name, dtype: object

Accessing Rows:

# Accessing a row by index

print(df.iloc[0])  # Access the first row (index 0)

Output:

Name: 0, dtype: object

You can also use the loc[] method if you want to access rows using labels.

print(df.loc[0])  # Same output as iloc

✅ Filtering Data

You can filter data in a DataFrame based on conditions.

Example: Filtering Rows Based on Age:

# Filter rows where Age is greater than 25

filtered_data = df[df['Age'] > 25]

print(filtered_data)

Output:

✅ Modifying Data

You can easily modify the values of an existing DataFrame.

Example: Changing a Column Value

# Update the 'Age' of Bob to 36

df.loc[df['Name'] == 'Bob', 'Age'] = 36

print(df)

Output:

🔹 5. Importing and Exporting Data with Pandas

Pandas makes it easy to read from and write to various data formats, including CSV, Excel, SQL, and more.

✅ Reading Data

# Read a CSV file into a DataFrame

df = pd.read_csv('data.csv')

✅ Writing Data

# Write DataFrame to a CSV file

df.to_csv('output.csv', index=False)

✅ Reading Excel Files

# Read an Excel file into a DataFrame

df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

🔹 6. Summary Table