Pandas DataFrames

Pandas is the most popular Python library for working with tabular data — data that looks like a table with rows and columns (like an Excel spreadsheet).

While NumPy works with plain numbers, Pandas works with labeled data — it gives names to your columns and lets you do powerful data operations.

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1. Installing Pandas

pip install pandas

Note: Pandas is already installed in Google Colab.

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2. What is a DataFrame?

A DataFrame is a 2D table of data with:

• Rows = individual data points (e.g., one student, one sale)
• Columns = features/properties (e.g., name, score, date)

import pandas as pd

# Create a DataFrame from a dictionary
data = {
    "name": ["Sai", "Ravi", "Priya", "Anu"],
    "score": [85, 72, 91, 68],
    "city": ["Hyderabad", "Chennai", "Bangalore", "Mumbai"]
}

df = pd.DataFrame(data)
print(df)

Output:

    name  score       city
0    Sai     85  Hyderabad
1   Ravi     72    Chennai
2  Priya     91  Bangalore
3    Anu     68     Mumbai

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3. Reading Data from CSV Files

In the real world, you do not type data by hand. You load it from CSV files (Comma Separated Values):

import pandas as pd

# Read a CSV file into a DataFrame
df = pd.read_csv("students.csv")

# See the first 5 rows
print(df.head())

# See the last 3 rows
print(df.tail(3))

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4. Exploring Your Data

Before doing anything with data, always explore it first:

import pandas as pd

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

# Shape: How many rows and columns?
print(df.shape)  # Output: (100, 5)  → 100 rows, 5 columns

# Column names
print(df.columns)  # Output: Index(['name', 'score', 'city', ...])

# Data types of each column
print(df.dtypes)

# Quick summary of numeric columns
print(df.describe())

# Info about the whole DataFrame
print(df.info())

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5. Selecting Columns

import pandas as pd

data = {
    "name": ["Sai", "Ravi", "Priya"],
    "score": [85, 72, 91],
    "city": ["Hyderabad", "Chennai", "Bangalore"]
}
df = pd.DataFrame(data)

# Select one column (returns a Series)
print(df["name"])

# Select multiple columns (returns a DataFrame)
print(df[["name", "score"]])

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6. Selecting Rows

# Select by index position
print(df.iloc[0])    # First row
print(df.iloc[0:2])  # First two rows

# Select by condition (filtering)
toppers = df[df["score"] > 80]
print(toppers)

Output:

    name  score       city
0    Sai     85  Hyderabad
2  Priya     91  Bangalore

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7. Adding and Removing Columns

# Add a new column
df["passed"] = df["score"] >= 50
print(df)

# Remove a column
df = df.drop("passed", axis=1)
print(df)

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Summary

• Pandas = Python library for working with tables of data.
• DataFrame = A table with rows and columns (like Excel).
• pd.read_csv() = Load data from a CSV file.
• df.head(), df.shape, df.describe() = Explore your data quickly.
• df["column"] = Select a column. df[condition] = Filter rows.
• Pandas is built on top of NumPy — everything you learned about NumPy helps here.