Python Mastery – From Basics to Brilliance / Python Libraries for Real-World Use

Pandas – DataFrames & Analysis

🐼 Pandas

Pandas is a popular Python library used for working with, analyzing, and manipulating data.

It is especially useful for working with tables of data, similar to Excel spreadsheets.

📦 Installing Pandas

Pandas is an external library, so install it using pip:

pip install pandas

Then import it:

import pandas as pd

📊 DataFrame

The most important Pandas object is a DataFrame.

A DataFrame is a 2-dimensional table with rows and columns.

import pandas as pd
data = {
    "Name": ["Tisha", "Rahul", "Anu"],
    "Age": [23, 25, 21]
}
df = pd.DataFrame(data)
print(df)
    Name  Age
0  Tisha   23
1  Rahul   25
2    Anu   21

📋 Series

A Series is a single column of data.

A Series is a 1-dimensional table with rows and columns.

ages = pd.Series([23, 25, 21])

print(ages)
0    23
1    25
2    21

🔍 Common Pandas Operations

df.head()        # First few rows
df.tail()        # Last few rows
df.info()        # Information about data
df.describe()    # Statistics
df["Age"]        # Select a column
df.dropna()      # Remove missing values

📁 Reading a CSV File

Pandas makes it easy to work with files:

df = pd.read_csv("students.csv")
print(df)

You can also save data:

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

⭐ Remember

Pandas = Python library for working with structured/tabular data.