Skip to content

24_data_analysis

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History

24_data_analysis

README.md

Data Analysis Notebooks

Perform exploratory data analysis on real-world datasets with Python’s pandas library. Includes examples with weather data and the 2018 Central Park Squirrel Census.

Features

  • Downloadable Dataset: Easily access the 2018 Central Park Squirrel Census - Squirrel Data.
  • Weather Data Analysis: Learn the basics of pandas with a small, sample weather dataset-explore data types, calculate statistics, filter data, and more.
  • Advanced Census Analysis: Deep dive into the squirrel census, including aggregating fur color counts and saving results to CSV.
  • Practical DataOps: Create your own DataFrame, save it to CSV, and apply column-wise transformations using pandas' intuitive API.

Project Structure

Data-Analysis/
│── pandas_analysis.ipynb                                      # Main Jupyter notebook for all analysis
│── weather_data.csv                                           # Sample weather dataset
│── 2018_Central_Park_Squirrel_Census_-_Squirrel_Data.csv     # Squirrel census dataset (download link above)
│── data.csv                                               # Squirrel fur color count results (generated)
│── new_data.csv                                            # Sample exported dataframe
│── README.md                                             # Project documentation

How It Works

  • Explore the basics of pandas: loading data, inspecting types, filtering columns/rows, and simple statistics.
  • Apply Python functions to create new features (e.g., converting temperature to Fahrenheit).
  • Analyze the squirrel dataset to count populations by fur color and export results for visualization or further use.
  • All analysis documented in an interactive notebook—no complex setup required.

Requirements

  • Python 3.x
  • pandas
  • Jupyter Notebook (pip install notebook)
  • Download the squirrel census dataset as directed above.

Usage Instructions

  1. Clone this repository and download the datasets.
  2. Open the notebook:
    • In terminal, run: jupyter notebook pandas_analysis.ipynb
  3. Walk through the code and outputs, modifying or adding steps to suit new data or analysis goals.
  4. Results (such as custom CSV exports) will appear in the project directory.

References