Photo by Brett Jordan on Unsplash
Big Mac: Data Wrangling
"Exploring Big Mac Prices Across Currencies: A Hands-On Pandas Project for Data Analysts using Python."
This project focuses on analyzing and comparing Big Mac prices from the year 2000 to 2022 using the Python Pandas library and summary statistics data from three different currencies: Japanese Yen (JPY), Swedish Krona (SEK), and United States Dollar (USD).
The main objectives include extracting, cleaning, and analyzing the data from Kaggle, specifically emphasizing currency datasets. The project aims to provide a practical example for individuals interested in data science, financial analysis, or leveraging Kaggle as a data source.
Contents
Extracting the dataset from Kaggle into Pandas DataFrame
Identify and handle possible missing values
Prepare Bigmac DataFrames by specific currencies (SEK, JPY, USD)
Group data and use data binning on currency basis
Use summary statistics to gain basic insight of data
Combine cleaned datasets into one DataFrame
Export DataFrame from Jupyter Notebook to CSV or Excel file
Github Documentation
Exploratory Data Analysis of BigMac coming soon!
I recommend checking the Big Mac Dataset: Data Acquisition (hashnode.dev) article to know more about how the Bigmac Dataset was exported from Kaggle.
Check out Understanding Data Wrangling (hashnode.dev) to know more about the basic theory of Data Wrangling in Python.
Pandas Library, the foundation of this project, can be found here. My Pandas Documentation as a Beginner (hashnode.dev)