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COURSE TOPIC

Python for Data Analysis with NumPy and Pandas

Use Python, NumPy, and Pandas to inspect, clean, transform, and explore data with repeatable steps.

Part of course Data Analyst Course
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Difficulty Level
Beginner to intermediate
Estimated Learning Time
4-5 hours
OVERVIEW

About Python for Data Analysis with NumPy and Pandas

Python provides a flexible way to prepare and explore data. NumPy supports numeric arrays, while Pandas provides labeled tables for selecting, cleaning, combining, and summarizing records.

LEARNING OUTCOMES

What You Will Learn

  • Load data and inspect Pandas DataFrame structure.
  • Identify missing values, duplicates, and inconsistent data.
  • Filter, transform, and combine data with Pandas.
  • Use NumPy and Pandas for calculations and summaries.
TOPIC GUIDE

Learn Python for Data Analysis with NumPy and Pandas

Build a Python foundation

Variables, lists, dictionaries, conditions, loops, and functions help break analysis into manageable steps. Clear names and small functions make scripts easier to understand and reuse.

Inspect data with Pandas

A DataFrame represents tabular data. Check dimensions, columns, types, and sample rows. Convert dates and numbers to suitable types, then inspect missing values and duplicates.

Clean and transform

Standardize text, filter records, create calculated columns, group data, and combine files. Check each transformation with row counts and totals. NumPy supports numeric calculations and operations on arrays.

Explore and explain

Use summaries and simple charts to investigate patterns. A notebook combines code, output, and notes so others can follow the workflow. Confirm each result answers the original question and record assumptions.

Practice

Load a sales CSV with Pandas, inspect its structure, check missing values, parse dates, calculate revenue from quantity and unit price, and group by category and month. Compare grouped and overall totals, then describe one pattern to investigate.

PRACTICAL EXAMPLE

Example / Code

Example 
import pandas as pd
sales = pd.read_csv('sales.csv')
print(sales.shape, sales.dtypes)
sales['order_date'] = pd.to_datetime(sales['order_date'], errors='coerce')
sales['revenue'] = sales['quantity'] * sales['unit_price']
summary = sales.groupby('category')['revenue'].sum().sort_values(ascending=False)
print(summary)
TRY IT YOURSELF

Practice Task

Apply what you learned with a practical activity.

Create a notebook that loads a CSV, reports types, checks missing values, creates a revenue column, groups by category, and validates totals.

TOOLKIT

Tools & Technologies

  • Python
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Matplotlib
CONTINUE LEARNING

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