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Using Python in Jupyter Notebook to recreate queries for imaginary stakeholders. Demonstrates connecting to MySQL, exporting tables to Excel, merging data, cleaning datasets, and counting orders. Visualizations include bar plots, revenue plots, pie charts, scatter charts, and map manipulation with geopandas. Dataset from MySQL.
A climate analysis conducted to assist with planning a trip to Honolulu, HI. This analysis uses Python, SQLAlchemy, Flask and Pandas in Jupyter notebook to analyze and share the climate data from a sqlite database.
Data Scientist/ Engineer spent majority of their time to clean-up data as it's not always "clean" due to many reasons, such as inconsistent user inputs, defective sensors, typos, special characters, etc. In this project, 2 Jupyter Notebooks with Python libraries: Pandas, SQLAlchemy & Psycopg were used to clean and load data into SQLite Database.