Date Created

08/10/23

Statistics from Stock Data

This project involves analyzing stock data using the Pandas library in Python. The goal is to calculate summary statistics and create visualizations to gain insights into the data.

Project Files

The project files include the following:

• stock_datas: This are the three(.csv files) dataset containing the stock data.
• stats of stock data.ipynb: This Jupyter notebook contains the code for analyzing the stock data.

Project Steps

The project involves the following steps:

1. Data Cleaning: The stock data is loaded into a Pandas DataFrame and any missing or inconsistent values are handled.
2. Summary Statistics: Measures of central tendency (mean, median) and measures of spread (standard deviation, range) are calculated for the quantitative variables in the dataset.
3. Data Visualization: Visualizations such as bar charts, histograms, scatter plots, and box plots are created to explore the data and gain insights.

How to Run the Code

To run the code, follow these steps:

1. Install the necessary libraries: Pandas, Matplotlib, and Seaborn.
2. Download the project files from the repository.
3. Open the stats of stock data.ipyn in Jupyter notebook or any other Python environment(e.g: code).
4. Run each cell in the notebook to execute the code and generate the analysis results.
5. View the presentation.pdf file to see the slide show presentation.

Resources

The following resources were used in completing this project:

• Udacity's Programming for Data Science with Python Nanodegree program
• Pandas documentation: https://pandas.pydata.org/docs/
• Matplotlib documentation: https://matplotlib.org/stable/contents.html
• Seaborn documentation: https://seaborn.pydata.org/tutorial.html