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Cleaning and manipulating mystery data with Python’s Pandas library, and visualizing with matplotlib

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YujiJeong/Mystery-Puzzle-Data

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Mystery-Puzzle-Data

Exploratory data analysis with Python Woohoo! I clean and manipulate this mystery data (I'm not given any information on the data prior to the analysis!) with Python’s Pandas library. I also visualize data with matplotlib.

See 'solving_puzzle_data.ipynb' to see the main exploratory data analysis.'solving_puzzle_data.ipynb' analyzes with 'puzzle.csv' and 'global-airports.geojson'

See 'top50_airports.ipynb' for additional exploratory data analysis. 'top50_airports.ipynb' analyzes with 'puzzle.csv', 'global-airports.geojson' and 'largest-global-airports-by-passenger-traffic-1.xls'

'puzzle.csv' is the mystery data that I obtained from Hopper

'global-airports.geojson' contains the coordinates and names (IATA) of over 6000 airports around the world

'largest-global-airports-by-passenger-traffic-1.xls' contains the names (IATA) of the top 50 most visited airports in the world

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Cleaning and manipulating mystery data with Python’s Pandas library, and visualizing with matplotlib

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