Data Analytics with Python & NumPy

Python

NumPy

Pandas

📘 Overview

This repository demonstrates my learning and practical skills in Python programming and NumPy, essential for the role of a Data Analyst. It contains hands-on notebooks where I have implemented fundamental concepts, numerical computations, and data manipulation exercises.

These notebooks serve as a foundation for data analysis, statistical computation, and data-driven decision making.

🧩 Skills & Topics Covered

Python Basics (py.ipynb)

• Variables & Data Types (int, float, string, boolean)
• Collections: lists, tuples, sets, dictionaries
• Conditional Statements (if, elif, else)
• Loops (for, while)
• Functions: definition, parameters, return values
• Basic Error Handling (try/except)
• Practice exercises demonstrating problem-solving skills

NumPy (g_numpy.ipynb)

• Creating and initializing arrays: np.array(), np.zeros(), np.ones(), np.arange()
• Indexing, slicing, and modifying arrays
• Array operations: element-wise addition, subtraction, multiplication, division
• Broadcasting for operations on arrays of different shapes
• Statistical functions: np.mean(), np.median(), np.std(), np.sum()
• Linear algebra: dot products, matrix multiplication
• Random sampling using np.random
• Reshaping and resizing arrays

Pandas (g_pandas.ipynb)

• Data Structures
  • Series and DataFrame
  • Creating and converting between them
• Reading & Writing Data
  • pd.read_csv(), pd.read_excel(), to_csv(), etc.
• Data Exploration & Inspection
  • .head(), .info(), .describe(), .shape, .dtypes
• Indexing, Selection & Filtering
  • .loc, .iloc, boolean indexing
  • Conditional filtering
• Data Cleaning
  • Handling missing values (.isnull(), .dropna(), .fillna())
  • Removing duplicates
  • Changing data types
• Manipulation & Transformation
  • Adding, deleting, renaming columns
  • Applying functions with .apply(), .map()
  • Sorting, reindexing, resetting index
  • Grouping & Aggregation
  • .groupby() + aggregate functions (sum, mean, count, etc.)
  • pivot_table and crosstab
• Merging, Joining & Concatenation
  • pd.merge(), pd.concat(), .join()
• Time Series & Date Handling
  • Parsing dates, resampling, rolling windows
• Basic Data Visualization with Pandas
  • Plotting with .plot() (line, histogram, bar)
  • Integrating with Matplotlib / Seaborn if needed

🧰 Tools & Technologies

• Python 3.x
• NumPy
• Google Colab / Jupyter Notebook

🚀 How to Use

Using Google Colab (Recommended)

Click the Open in Colab badges above to interactively explore and run the notebooks.

Running Locally

1. Clone the repository:

   git clone https://github.com/garimaakashyap/python-basics.git