Using Pandas & Python to Analyze PyCitySchool District

Resources

• Data Sources:
  • clean_students_complete.csv
  • missing_grades.csv
  • schools_complete.csv
  • students_complete.csv
• Software: Python 3.7.10, Jupyter Lab Server 1.4.1, Visual Code Editor, Pandas, Numpy

Challenge Project Overview

Using Jupyter Lab, Python, and the Pandas library, I analyzed PyberCitySchools district data and showcased trends in school performance. During this analysis, I read raw data from CSV files, inspected and cleaned data, merged datasets, performed mathematical calculations, and visualized the data with charts and graphs to tell a story.

Challange Project Analysis

Analysis Results Notebook:

• Analysis Results Notebook: PyCitySchools_Challenge.ipynb

Challange Project Summary