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3. Pandas – Data Manipulation & Analysis

Pandas is the most powerful and widely used Python library for data wrangling, cleaning, exploration, and analysis. It is built on top of NumPy and provides high-level data structures (Series and DataFrame) that make working with tabular/structured data feel like using Excel or SQL — but much more powerful.

Install Pandas (if not using Anaconda)

Bash

pip install pandas

Standard import (always use this)

Python

import pandas as pd

3.1 Series and DataFrame – Core Data Structures

Series A one-dimensional labeled array (like a column in Excel or a vector with labels).

Python

# Create Series from list s1 = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd']) print(s1) # a 10 # b 20 # c 30 # d 40 # dtype: int64 # Access by label print(s1['b']) # 20 # From dictionary s2 = pd.Series({'Math': 85, 'Science': 92, 'English': 78}) print(s2['Science']) # 92

DataFrame A two-dimensional labeled data structure (like a spreadsheet or SQL table) — the heart of Pandas.

Python

# Create DataFrame from dictionary data = { 'Name': ['Anshuman', 'Priya', 'Rahul', 'Sneha'], 'Age': [25, 23, 24, 22], 'City': ['Ranchi', 'Delhi', 'Patna', 'Kolkata'], 'Marks': [92, 88, 85, 90] } df = pd.DataFrame(data) print(df) # Name Age City Marks # 0 Anshuman 25 Ranchi 92 # 1 Priya 23 Delhi 88 # 2 Rahul 24 Patna 85 # 3 Sneha 22 Kolkata 90 # Basic inspection print(df.head(2)) # first 2 rows print(df.info()) # data types, non-null count print(df.describe()) # summary statistics print(df.shape) # (rows, columns) → (4, 4)

Quick access

Python

df['Name'] # Series – one column df[['Name', 'Marks']] # DataFrame – multiple columns df.iloc[0] # first row (position-based) df.loc[0, 'Name'] # label-based access

3.2 Data Loading (CSV, Excel, JSON, SQL)

Pandas makes reading data from almost any source effortless.

CSV

Python

df = pd.read_csv("sales_data.csv") # Options: skiprows=2, usecols=['date','sales'], dtype={'sales':float}

Excel

Python

df = pd.read_excel("report.xlsx", sheet_name="Sales", skiprows=1) # Need: pip install openpyxl or xlrd

JSON

Python

df = pd.read_json("data.json") # or pd.json_normalize() for nested JSON

SQL (with database connection)

Python

import sqlalchemy as sa engine = sa.create_engine("sqlite:///mydb.db") df = pd.read_sql("SELECT * FROM customers", engine) # or pd.read_sql_query(query, engine)

Quick save

Python

df.to_csv("cleaned_data.csv", index=False) df.to_excel("report.xlsx", index=False) df.to_json("data.json", orient="records")

3.3 Data Cleaning, Filtering & Transformation

Real data is messy — Pandas excels at cleaning it.

Basic cleaning

Python

df = df.drop_duplicates() # remove duplicate rows df = df.dropna(subset=['age', 'city']) # drop rows with missing values df['age'] = df['age'].fillna(df['age'].median()) # fill missing with median df['salary'] = df['salary'].astype(float) # change data type df['date'] = pd.to_datetime(df['date']) # convert to datetime

Filtering

Python

high_salary = df[df['salary'] > 80000] young_delhi = df[(df['age'] < 30) & (df['city'] == 'Delhi')] top_10 = df.nlargest(10, 'marks')

Transformations

Python

df['tax'] = df['salary'] * 0.18 # new column df['full_name'] = df['first'] + " " + df['last'] df['salary_category'] = pd.cut(df['salary'], bins=[0, 50000, 100000, np.inf], labels=['Low', 'Medium', 'High'])

3.4 Grouping, Aggregation & Pivot Tables

Groupby – most powerful feature

Python

# Group by city and calculate mean salary df.groupby('city')['salary'].mean() # Multiple aggregations df.groupby('city').agg({ 'salary': ['mean', 'max', 'count'], 'age': 'median' })

Pivot Tables

Python

pd.pivot_table(df, values='salary', index='city', columns='department', aggfunc='mean', fill_value=0)

Crosstab

Python

pd.crosstab(df['city'], df['gender'], margins=True)

3.5 Handling Missing Values & Outliers

Missing values

Python

# Check missing df.isnull().sum() # Fill missing df['age'].fillna(df['age'].median(), inplace=True) df['city'].fillna('Unknown', inplace=True) # Drop missing df.dropna(subset=['salary'], inplace=True)

Detect & handle outliers

Python

# Using IQR method Q1 = df['salary'].quantile(0.25) Q3 = df['salary'].quantile(0.75) IQR = Q3 - Q1 lower = Q1 - 1.5 IQR upper = Q3 + 1.5 IQR # Remove outliers df_clean = df[(df['salary'] >= lower) & (df['salary'] <= upper)] # Or cap them df['salary'] = df['salary'].clip(lower=lower, upper=upper)

Mini Summary Project – Quick EDA on Sample Dataset

Python

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Load sample (or your own CSV) df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv") # Quick look print(df.head()) print(df.info()) print(df.describe()) # Missing values print(df.isnull().sum()) # Group analysis print(df.groupby('day')['total_bill'].mean()) # Visualization sns.boxplot(x='day', y='total_bill', data=df) plt.title("Total Bill by Day") plt.show()

This completes the full Pandas – Data Manipulation & Analysis section — the most important tool for real data science work in Python!

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