Pandas DataFrames

Introduction

Pandas is a powerful data manipulation library built on NumPy, providing DataFrame and Series structures.

Creating DataFrames

import pandas as pd

# From dictionary
data = {
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "score": [85, 92, 78]
}
df = pd.DataFrame(data)

# From CSV
df = pd.read_csv("data.csv")

# From dictionary of lists
df = pd.DataFrame({
    "product": ["A", "B", "C"],
    "sales": [100, 200, 150]
}, index=["Jan", "Feb", "Mar"])

Viewing Data

df.head()        # First 5 rows
df.tail()        # Last 5 rows
df.info()        # Data types and nulls
df.describe()    # Statistical summary
df.shape          # (rows, columns)
df.columns        # Column names
df.index          # Row indices

Selecting Data

# Columns
df["name"]
df[["name", "age"]]

# Rows
df.loc[0]              # By index label
df.iloc[0]             # By integer position
df.loc[0:2]            # Slice by label
df.iloc[0:2]           # Slice by position

Practice Problems

1. Create DataFrame from real dataset
2. Filter rows based on conditions
3. Select multiple columns
4. Handle missing values
5. Group and aggregate data