🐍 Python Data Analyst Portfolio

This repository showcases my Python Data Analyst Portfolio. It contains structured notes, coding exercises, interview questions, datasets, case studies, and mini-projects completed as part of my roadmap to becoming a Data Analyst.

📚 Learning Roadmap

• ✅ Module 1: Python Basics
• ✅ Module 2: Data Structures
• ✅ Module 3: NumPy
• ✅ Module 4: Pandas
• ✅ Module 5: Data Cleaning
• ⏳ Module 6: Data Visualization
• ⏳ Module 7: Mini Projects
• ⏳ Module 8: Interview Questions

📊 Portfolio Snapshot

• 📁 5 Modules Completed
• 📝 45+ Python Programs
• 📂 2 Practice Datasets
• 📊 4 Mini Projects
• 🚀 Updated Regularly

📂 Repository Structure

python-data-analyst-portfolio/
│
├── README.md
├── datasets/
│   ├── employees.csv
│   └── sales.csv
│
├── Module-01-Python-Basics/
│   ├── 01_variables.py
│   ├── 02_data_types.py
│   ├── 03_input.py
│   ├── 04_output.py
│   ├── 05_arithmetic_operators.py
│   ├── 06_comparison_operators.py
│   ├── 07_logical_operators.py
│   ├── 08_assignment_operators.py
│   ├── 09_identity_operators.py
│   ├── 10_membership_operators.py
│   ├── 11_if.py
│   ├── 12_if_else.py
│   ├── 13_elif.py
│   ├── 14_nested_if.py
│   ├── 15_for_loop.py
│   ├── 16_range.py
│   ├── 17_while_loop.py
│   ├── 18_functions.py
│   ├── 19_parameters_arguments.py
│   ├── 20_return.py
│   └── module1_revision.py
│
├── Module-02-Data-Structures/
│   ├── 01_lists.py
│   ├── 02_tuples.py
│   ├── 03_sets.py
│   ├── 04_dictionaries.py
│   └── module2_revision.py
│
├── Module-03-NumPy/
│   ├── 01_install_import.py
│   ├── 02_create_arrays.py
│   ├── 03_array_attributes.py
│   ├── 04_indexing_slicing.py
│   ├── 05_array_operations.py
│   ├── 06_broadcasting.py
│   ├── 07_aggregation_functions.py
│   ├── 08_axis.py
│   ├── 09_reshape.py
│   ├── 10_flatten_ravel.py
│   ├── 11_boolean_indexing.py
│   ├── 12_filtering.py
│   ├── 13_random_module.py
│   ├── 14_stacking_splitting.py
│   ├── 15_useful_functions.py
│   └── module3_revision.py
│
├── Module-04-Pandas/
│   ├── 01_pandas_introduction.py
│   ├── 02_series.py
│   ├── 03_dataframe.py
│   ├── 04_read_write_csv.py
│   ├── 05_select_rows_columns.py
│   ├── 06_filtering.py
│   ├── 07_sorting.py
│   ├── 08_handling_missing_values.py
│   ├── 09_groupby.py
│   ├── 10_merge.py
│   ├── 11_concat.py
│   ├── 12_apply_functions.py
│   ├── 13_pivot_table.py
│   ├── 14_useful_functions.py
│   └── module4_revision.py
│
├── Module-05-Data-Cleaning/
│   ├── 01_isnull.py
│   ├── 02_notnull.py
│   ├── 03_dropna.py
│   ├── 04_dropna_axis.py
│   ├── 05_fillna.py
│   ├── 06_fillna_mean.py
│   ├── 07_fillna_median.py
│   ├── 08_fillna_mode.py
│   ├── 09_duplicated.py
│   ├── 10_drop_duplicates.py
│   ├── 11_replace.py
│   ├── 12_astype.py
│   ├── 13_string_operations.py
│   ├── 14_datetime.py
│   └── module5_revision.py
│
├── Module-06-Visualization/
│
├── Module-07-Mini-Projects/
│   ├── 01_student_grade_calculator.py        ✅ Module 1
│   ├── 02_student_record_management.py       ✅ Module 2
│   ├── 03_numpy_sales_analysis_project.py    ✅ Module 3
│   └── 04_pandas_sales_analysis_project.py   ✅ Module 4
│
└── Module-08-Interview-Questions/

🚀 Skills Covered

✅ Module 1: Python Basics

• Variables
• Data Types
• Input & Output
• Type Casting
• Operators
• Conditional Statements (if, elif, else)
• for Loops
• while Loops
• Functions
• Common Errors & Debugging
• Strings
  • Indexing
  • Slicing
  • String Methods

---

✅ Module 2: Data Structures

Lists

• Creating Lists
• Indexing
• Negative Indexing
• Slicing
• Updating Elements
• append()
• insert()
• remove()
• pop()
• sort()
• reverse()
• len()
• Membership Operators (in, not in)

Tuples

• Creating Tuples
• Indexing
• Negative Indexing
• Immutability
• Single Element Tuples
• count()
• index()
• len()

Sets

• Creating Sets
• Unique Elements
• add()
• remove()
• discard()
• pop()
• clear()
• len()
• Membership Operators

Dictionaries

• Creating Dictionaries
• Accessing Values
• Adding & Updating Key-Value Pairs
• keys()
• values()
• items()
• get()
• update()
• pop()
• clear()
• len()
• Membership Operators

---

✅ Module 3: NumPy

• Installing & Importing NumPy
• Creating Arrays
• Array Attributes
  • ndim
  • shape
  • size
  • dtype
• Indexing
• Negative Indexing
• Slicing
• Element-wise Operations
• Scalar Operations
• Power Operator (**)
• Modulus Operator (%)
• Broadcasting
• Aggregation Functions
  • sum()
  • mean()
  • max()
  • min()
• Axis
• Reshape
• Flatten & Ravel
• Boolean Indexing
• Filtering
• Random Module
• Stacking & Splitting
• Useful NumPy Functions
  • arange()
  • linspace()
  • zeros()
  • ones()
  • eye()
  • unique()
  • sort()
  • where()
  • clip()

---

✅ Module 4: Pandas

Basics

• Introduction to Pandas
• Installing & Importing Pandas
• Checking Pandas Version
• Why Pandas?

Series

• Creating Series
• Default Index
• Custom Index
• Accessing Elements
• Indexing & Slicing
• iloc
• Creating Series from Dictionary
• Series Attributes
  • index
  • values
  • dtype
  • shape
  • size
  • ndim

DataFrame

• Creating DataFrames
• Creating DataFrame from Dictionary
• DataFrame Attributes
  • columns
  • index
  • shape
  • size
  • ndim
  • dtypes
  • info()
  • describe()

Reading CSV Files

• pd.read_csv()
• Reading CSV Files
• head()
• tail()
• info()
• describe()

Selecting Data

• Selecting Single Column
• Selecting Multiple Columns
• df.columns
• df.index
• df.shape

Filtering

• Comparison Operators
  • >
  • <
  • ==
  • !=
  • >=
  • <=
• Multiple Conditions
  • & (AND)
  • | (OR)
• isin()
• between()

Sorting

• sort_values()
• Ascending Sorting
• Descending Sorting
• Sorting by Multiple Columns
• Different Sorting Order
• sort_index()
• inplace=True

Handling Missing Values

• Missing Values
• isnull()
• notnull()
• dropna()
• fillna()
• Filling with Mean
• Filling with Median
• Filling with Mode
• inplace=True

GroupBy

• groupby()
• sum()
• mean()
• count()
• min()
• max()
• agg()
• Grouping by Multiple Columns

Merge & Join

• merge()
• Inner Join
• Left Join
• Right Join
• Outer Join
• Merging on Multiple Columns

Concatenation

• concat()
• Vertical Concatenation
• Horizontal Concatenation
• ignore_index
• axis

Apply Functions

• apply()
• Lambda Functions
• User-defined Functions
• axis=0
• axis=1

Pivot Tables

• pivot_table()
• index
• columns
• values
• aggfunc
• Multiple Aggregations
• fill_value

Useful Pandas Functions

• unique()
• nunique()
• value_counts()
• duplicated()
• drop_duplicates()
• rename()
• astype()
• sample()

✅ Module 5: Data Cleaning

Missing Values

• Identifying Missing Values
• isnull()
• notnull()
• Counting Missing Values
• Counting Non-Missing Values

Removing & Filling Missing Values

• dropna()
• dropna(axis=1)
• fillna()
• inplace=True
• Filling with Mean
• Filling with Median
• Filling with Mode

Duplicate Records

• duplicated()
• drop_duplicates()
• Identifying Duplicate Rows
• Removing Duplicate Rows

Replacing Values

• replace()
• Replacing Numeric Values
• Replacing Text Values
• Replacing Multiple Values

Data Type Conversion

• astype()
• Integer Conversion
• Float Conversion
• String Conversion

String Operations

• str.lower()
• str.upper()
• str.title()
• str.strip()
• str.replace()

Date & Time

• pd.to_datetime()
• .dt.year
• .dt.month
• .dt.day

---

📚 Upcoming Modules

Module 6: Data Visualization

• Matplotlib
• Seaborn
• Line Charts
• Bar Charts
• Pie Charts
• Histograms
• Scatter Plots
• Dashboards

Module 7: Mini Projects

• Student Grade Calculator ✅ Module 1
• Student Record Management ✅ Module 2
• NumPy Sales Analysis Project ✅ Module 3
• Pandas Sales Analysis Project ✅ Module 4
• 🚧 Employee Analytics Project
• 🚧 E-commerce Data Analysis

Module 8: Interview Questions

• Python Interview Questions
• NumPy Interview Questions
• Pandas Interview Questions
• Data Cleaning Interview Questions
• Data Analytics Coding Questions

⭐ Repository Highlights

• 📘 Structured learning roadmap from Python basics to data analysis.
• 💻 Hands-on coding exercises for every concept.
• 📊 Practical NumPy and Pandas implementations using real-world datasets.
• 📂 Includes sample datasets (employees.csv and sales.csv) for hands-on practice.
• 🔍 Covers filtering, sorting, grouping, merging, pivot tables, and business insights.
• 🧹 Practical data-cleaning techniques using Pandas.
• 📈 End-to-end sales analysis case studies using Pandas.
• 🚀 Real-world mini projects demonstrating applied skills.
• 📈 Regularly updated as I progress through my Data Analytics journey.

🛠️ Tools & Technologies

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook
• VS Code
• Git
• GitHub

🎯 Goal

The goal of this portfolio is to demonstrate my Python programming skills for Data Analytics through structured learning, coding exercises, real-world datasets, case studies, and mini projects.

📌 Progress

• ✅ Module 1 Completed
• ✅ Module 2 Completed
• ✅ Module 3 Completed
• ✅ Module 4 Completed
• ✅ Module 5 Completed
• ⏳ Module 6 Upcoming

🎯 Future Goals

• Learn Data Visualization using Matplotlib & Seaborn
• Build 15+ Data Analysis Projects
• Learn SQL for Data Analytics
• Learn Power BI
• Build an End-to-End Data Analyst Portfolio

👋 About This Repository

This repository showcases my structured journey toward becoming a Data Analyst. Each module includes theory, coding exercises, hands-on practice, sample datasets, and real-world case studies using Python, NumPy, and Pandas. The repository is continuously updated as I progress through my Data Analytics portfolio.