/NOAA_EDA

In this notebook, we will use pandas to examine 2018 lightning strike data collected by the National Oceanic and Atmospheric Administration (NOAA). Then, we will calculate the total number of strikes for each month and plot this information on a bar graph.

Primary LanguageJupyter NotebookMIT LicenseMIT

NOAA_EDA

In this repository, we analyze 2018 lightning strike data collected by the National Oceanic and Atmospheric Administration (NOAA) on different states of the USA. The analysis includes various data manipulation and visualization techniques using Python and Jupyter Notebooks.

Notebooks

1. Date String Manipulations and Visualization

Date string manipulations and Visualization.ipynb

This notebook covers:

  • Parsing and formatting date strings with pandas
  • Aggregating data by time periods (e.g., monthly totals)
  • Visualizing aggregated data using bar graphs

2. Dealing with Missing Data in Python

Dealing with missing data in Python.ipynb

This notebook includes:

  • Identifying missing data within datasets
  • Techniques to handle missing data (e.g., imputation, removal)
  • Impact analysis of missing data on overall dataset integrity

3. Exploratory Data Analysis (EDA)

EDA.ipynb

This notebook focuses on:

  • Preliminary data investigation to understand the data structure
  • Statistical summaries and visualizations to identify patterns and anomalies
  • Insights and observations derived from the 2018 NOAA lightning strike data