Pandas: Series and DataFrames

Pandas is a powerful Python library used for data analysis and manipulation. It provides two core data structures:

Series – One-dimensional (like a column)
DataFrame – Two-dimensional (like an Excel sheet or SQL table)

1. Pandas Series

What is a Series?

A Series is a one-dimensional array-like structure that can hold any data type (integers, strings, floats, etc.). It has:

Data: The actual values
Index: Labels for each value (like row names)
Creating a Series:

import pandas as pd

# From a list
s1 = pd.Series([10, 20, 30, 40])

# From a list with custom index
s2 = pd.Series([10, 20, 30], index=['a', 'b', 'c'])

# From a dictionary
s3 = pd.Series({'x': 100, 'y': 200})

Key Features:

Automatically assigns index
Supports slicing and indexing
Vectorized operations

Accessing Elements:

print(s2['a'])      # Access by label
print(s1[0:2])      # Access by position

2. Pandas DataFrame

What is a DataFrame?

A DataFrame is a two-dimensional labeled data structure with:

Rows (index)
Columns (labels)

Think of it as a table with rows and columns.

Creating a DataFrame:

# From a dictionary of lists
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# From a list of dictionaries
data = [{'a': 10, 'b': 20}, {'a': 30, 'b': 40}]
df2 = pd.DataFrame(data)

Topics to Cover in Series and DataFrames

1. Basic Operations

df.head() – First 5 rows
df.tail() – Last 5 rows
df.shape – Rows and columns
df.info() – Summary of the DataFrame
df.describe() – Descriptive statistics

2. Selecting and Accessing Data

df['ColumnName'] or df.ColumnName
df.iloc[0] – Row by position
df.loc[0] – Row by index label
df.iloc[0, 1] – Specific value
df[['Col1', 'Col2']] – Multiple columns

3. Adding/Removing Columns and Rows

# Adding a new column
df['Salary'] = [50000, 60000]

# Deleting a column
del df['Salary']

# Adding a row
df.loc[2] = ['Charlie', 28]

# Dropping a row
df = df.drop(0)  # Drops row with index 0

4. Filtering Data

# Filter rows where Age > 25
filtered = df[df['Age'] > 25]

5. Handling Missing Data

df.isnull()        # Detect missing values
df.dropna()        # Remove missing rows
df.fillna(0)       # Fill missing values with 0

6. DataFrame Methods

Method	Description
`df.mean()`	Mean of numeric columns
`df.max()`	Max value
`df.min()`	Min value
`df.sort_values(by='Age')`	Sort by a column
`df.rename(columns={'Old': 'New'})`	Rename columns

7. Importing and Exporting Files

# Read CSV
df = pd.read_csv('data.csv')

# Read Excel
df = pd.read_excel('data.xlsx')

# Save to CSV
df.to_csv('output.csv', index=False)

8. Indexing and Slicing

df[0:3]             # Row slicing
df[['Name', 'Age']] # Column selection

9. GroupBy and Aggregation

df.groupby('Department')['Salary'].mean()

10. Merging, Joining, Concatenating

# Concatenation
pd.concat([df1, df2])

# Merging
pd.merge(df1, df2, on='ID')

Practice Questions

Create a Series with names of 5 students and marks.
Create a DataFrame with columns: Name, Age, Grade.
Add a new column "Pass" with boolean values.
Filter students with marks > 80.
Count the number of students in each grade.
Replace missing values with the column mean.
Sort the DataFrame by Age.
Read a CSV file and display top 10 rows.
Drop a column and save to Excel.
Merge two DataFrames on a common column.

Concept	Structure	Example
Series	1D	pd.Series([10, 20])
DataFrame	2D	pd.DataFrame({...})