Python for Data Engineering — 30 Days of Practice

This repository contains 30 beginner-to-intermediate level Python problems designed to build a strong foundation for data engineering work. Each day focuses on a single concept and includes a small coding challenge to practice it.

In addition, I have also started working on 30 Days of Pandas Data Analysis, which builds directly on these exercises and focuses on data wrangling, cleaning, transformation, and exploratory analysis using Pandas.

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Daily Problems Overview

Day	Topic	Description
1	Variables & Arithmetic	Take two inputs, print sum, difference, product, quotient
2	String Formatting	Store name, age, city and print using .format()
3	Conditional Statements	Check if number is positive, negative, or zero
4	Loops	Print even numbers from 1 to 20
5	List Operations	Sum & average of list elements
6	Functions	Multiply two numbers using a function
7	List Comprehensions	Generate squares of numbers from 1 to 10
8	Tuples & Sets	Convert list to tuple and remove duplicates using set
9	Dictionaries	Add & remove key-value pairs
10	File Handling	Create and read a .txt file
11	File Writing	Write multiple lines to a file
12	CSV Files	Read and write CSV using csv module
13	String Manipulation	Count vowels in a string
14	String Slicing	Extract first 5 and last 3 characters
15	NumPy Basics	Generate random integers and calculate sum & mean
16	NumPy Operations	Find max, min, mean of an array
17	Pandas Basics	Create a DataFrame from a dictionary
18	Pandas Filtering	Filter rows based on a column condition
19	Pandas GroupBy	Group by column and calculate mean
20	Handling Missing Data	Fill NaN values with column mean
21	Sorting Data	Sort DataFrame by a column
22	Dates in Pandas	Filter rows based on date conditions
23	Pandas & CSV	Save and read a CSV file
24	Merging DataFrames	Merge on a common column
25	Pivot Tables	Summarize data by category
26	Categorical Data	Convert column to categorical type
27	Apply + Lambda	Calculate new column using apply()
28	String Methods in Pandas	.str.upper() and .str.lower()
29	Filtering with Multiple Conditions	Combine boolean conditions with & and `	`
30	Final Mini Project	Read, filter, aggregate, and save CSV output

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Skills Learned

• Python Basics: variables, data types, loops, conditionals
• Data Structures: lists, tuples, sets, dictionaries
• File Handling: text, CSV
• NumPy: arrays, basic math operations
• Pandas: creating DataFrames, filtering, grouping, sorting, pivoting
• Data Cleaning: handling missing data, type conversions
• Dates in Pandas: filtering by time periods
• Mini Project: combining reading, filtering, aggregation, and saving

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Next Steps

Along with this series, I am extending my learning with 30 Days of Pandas Data Analysis, which includes more advanced use cases:

• Complex filtering and transformations
• Multi-level indexing and hierarchical data
• Time series and rolling window operations
• Advanced aggregations and custom functions
• Exploratory analysis workflows

This second track is aimed at strengthening practical data analysis skills and preparing for real-world data engineering and analytics projects.