Unit 5 · Advanced Topics
Introduction to Pandas
Pandas is a powerful open-source data manipulation and analysis library for Python, providing two primary data structures — Series and DataFrame — that make working with structured data fast, intuitive, and expressive.
Table of Contents
University Definition
Pandas (Panel Data) is an open-source Python library built on top of NumPy that provides high-performance, easy-to-use data structures and data analysis tools. Its two main structures are Series (1-dimensional) and DataFrame (2-dimensional tabular data).
Installation & Import
# Install (in terminal) pip install pandas # Import import pandas as pd import numpy as np
1. Series
A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floats, etc.). Think of it as a single column of a spreadsheet.
import pandas as pd # Creating Series from list s = pd.Series([10, 20, 30, 40]) print(s) # Output: # 0 10 # 1 20 # 2 30 # 3 40 # dtype: int64 # Creating Series with custom index s2 = pd.Series([85, 90, 78], index=["Math", "Science", "English"]) print(s2) # Output: # Math 85 # Science 90 # English 78 # dtype: int64 # Access by label print(s2["Math"]) # 85 print(s2.mean()) # 84.33
2. DataFrame
A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. Think of it as a spreadsheet, SQL table, or a dictionary of Series objects.
Creating DataFrame from Dictionary
import pandas as pd data = { "Name": ["Amit", "Priya", "Rahul", "Sneha"], "Age": [22, 21, 23, 22], "Marks": [85, 92, 78, 95], "Grade": ["A", "A+", "B+", "A+"] } df = pd.DataFrame(data) print(df) # Output: # Name Age Marks Grade # 0 Amit 22 85 A # 1 Priya 21 92 A+ # 2 Rahul 23 78 B+ # 3 Sneha 22 95 A+
Creating DataFrame from Lists
# From list of lists rows = [ ["Amit", 22, 85], ["Priya", 21, 92], ["Rahul", 23, 78] ] df2 = pd.DataFrame(rows, columns=["Name", "Age", "Marks"]) print(df2)
Creating DataFrame from NumPy Array
import numpy as np arr = np.random.randint(50, 100, size=(3, 3)) df3 = pd.DataFrame(arr, columns=["Math", "Science", "English"], index=["A", "B", "C"]) print(df3)
3. Reading Data from CSV
# Read CSV file df = pd.read_csv("students.csv") # Read first few rows print(df.head()) # Read specific number of rows print(df.head(10)) # Save DataFrame to CSV df.to_csv("output.csv", index=False)
4. Inspecting Data
df = pd.read_csv("students.csv") print(df.head()) # First 5 rows print(df.tail()) # Last 5 rows print(df.shape) # (rows, columns) print(df.columns) # Column names print(df.dtypes) # Data types of each column print(df.info()) # Summary: non-null counts, types print(df.describe()) # Statistics: count, mean, std, min, max
5. Selecting Data
data = {
"Name": ["Amit", "Priya", "Rahul"],
"Age": [22, 21, 23],
"Marks": [85, 92, 78]
}
df = pd.DataFrame(data, index=["r1", "r2", "r3"])
# Select column by name
print(df["Name"])
# Select multiple columns
print(df[["Name", "Marks"]])
# iloc — select by integer position
print(df.iloc[0]) # First row
print(df.iloc[0:2]) # First two rows
print(df.iloc[0, 1]) # Row 0, Col 1 → 22
# loc — select by label
print(df.loc["r1"]) # Row with label "r1"
print(df.loc["r1":"r2", "Name":"Marks"]) # Rows r1-r2, cols Name-Marks
| Selector | Based On | Example |
|---|---|---|
| df["col"] | Column name | df["Name"] |
| df.iloc[] | Integer position | df.iloc[0, 1] |
| df.loc[] | Label / index | df.loc["r1", "Name"] |
6. Filtering Data
data = {
"Name": ["Amit", "Priya", "Rahul", "Sneha"],
"Marks": [85, 92, 78, 95]
}
df = pd.DataFrame(data)
# Filter rows where marks > 80
result = df[df["Marks"] > 80]
print(result)
# Output:
# Name Marks
# 0 Amit 85
# 1 Priya 92
# 3 Sneha 95
# Multiple conditions
result2 = df[(df["Marks"] > 80) & (df["Marks"] < 95)]
print(result2)
# Sorting
sorted_df = df.sort_values("Marks", ascending=False)
print(sorted_df)
# Basic statistics
print(df["Marks"].mean()) # 87.5
print(df["Marks"].max()) # 95
print(df["Marks"].min()) # 78
Practical Example: Student Records Analysis
import pandas as pd # Create student DataFrame students = pd.DataFrame({ "Name": ["Amit", "Priya", "Rahul", "Sneha", "Vikram"], "Math": [85, 92, 78, 95, 88], "Science": [80, 88, 72, 91, 85], "English": [75, 90, 85, 88, 80] }) # Add Total column students["Total"] = students["Math"] + students["Science"] + students["English"] # Add Average column students["Average"] = students["Total"] / 3 # Print full table print(students) # Topper topper = students[students["Average"] == students["Average"].max()] print(f"\nTopper:\n{topper}") # Class average print(f"\nClass Average: {students['Average'].mean():.2f}")
University Exam Tip
University exams often ask: "What is DataFrame?", "Difference between iloc and loc", or "Write a program to read CSV and display statistics". Always include code with output. Mention head(), info(), describe(), and filtering with conditions.
Key Points
Series = 1D labeled array; DataFrame = 2D labeled table.
pd.read_csv() reads CSV files; df.to_csv() saves DataFrames.
head(), tail(), info(), describe() are used to inspect data.
iloc[] selects by position; loc[] selects by label.
Filtering uses boolean conditions: df[df["col"] > value].
Pandas is built on top of NumPy and is essential for data analysis.
Practice Questions
- Create a DataFrame of 5 students with Name, Roll No, and Marks. Display the topper.
- Read a CSV file and use
describe()to show statistics. - Difference between
ilocandlocwith examples. - Filter all students with marks above 80 and sort them in descending order.
- Write a program to add a new column "Grade" based on marks (>= 90: A+, >= 80: A, else: B).
Summary
Pandas provides essential tools for data manipulation in Python. Series and DataFrame are the core structures. Master reading CSV, inspecting data, selecting with iloc/loc, filtering, sorting, and basic statistics for exams and real-world data analysis.
