Data Analysis with Python: NumPy & Pandas

This repository consists of some of the assignments and hands-on practices that I have done by taking the Data Analysis with Python: NumPy & Pandas Masterclass course on Udemy.

The course covers NumPy & Pandas for Data Science, Data Analysis & Business Intelligence:

• Master the essentials of NumPy and Pandas
• Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
• Analyze and manipulate dates and times for time intelligence and time-series analysis
• Import and export flat files, Excel workbooks and SQL database tables using Pandas
• Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms

The Maven MegaMart Course Project

Act as a newly hired Data Analyst for Maven MegaMart, a multinational corporation that operates a chain of retail and grocery stores.

• Mid-Course Project - Maven Acquisition Target Data
• Final Project - Maven Acquisition Target Data

They recently received a sample of data from a new retailer they're looking to acquire, and they need you to identify and deliver key insights about their sales history.

The assignment is to analyze over 2 million transactions by product, household, and store to get a better understanding of the retailer's main strengths. From there, we need to review the company's discount scheme to assess whether they can expect to attract customers without losing margin.

Use Python to:

• Read in multiple flat files efficiently
• Join tables to prove a single source of information
• Shape & aggregate sales data to calculate KPIs
• Visualize the data to communicate findings

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Setup & Run Jupyter Notebooks in VS Code w/ Virtual Env & Kernels

I completed below setup instead of using Anaconda (course instruction):

• create a virtual environment

  python3 -m venv jupyter-env 
  

• activate the virtual env

  source jupyter-env/bin/activate
  

• Installation

  pip install jupyterlab
  
  pip install ipykernel
  

  Validate that the install has succeeded by running jupyter-lab from your command line. A new tab should open in your browser, with the JupyterLab application running.

  • install useful Python packages in this virtual env

  pip install numpy
  pip install pandas
  pip install openpyxl
  pip install matplotlib
  pip install seaborn
  pip install lxml
  

• register the new virtual env with Jupyter so that you can use it within JupyterLab

  python3 -m ipykernel install --user --name=‘maven-python‘ 
  

Now open an existing/create a new .ipynb file in VS Code and select the maven-python Kernel to use