This is my first Pandas project using Matplotlib within a Jupyter Notebook.
Updated: Jan 18, 2021 | Created: Jan 14, 2021 Version: 1 Copyright: open source
== License =========================== None. See Installation instructions below for a list of applications.
== Configuration Instructions ======== None. See Installation instructions below for a list of applications.
== Installation Instructions ========== Applications used for the Pandas_Challenge:
- Jupyter Notebook
- GitBash terminal
- Visual Studio Code for the Readme.md
- Git Hub (to save versions and share code while in development)
- Image viewer such as Microsoft Photos or Microsoft Paint
Similar applications may also work.
== Operating Instructions ============= Open pymaceuticals_analysis file in Jupyter Notebook. Review Analysis at the top of the notebook. Play/run all rows in order from top to bottom to review code output and data analysis.
== List of Files ==================== \matplotlib .ipynb_checkpoints \data Mouse_metadata.csv Study_results.csv \images (all image files auto-generated from script) bar_pandas.png bar_pyplot.png boxplot.png line_onemouse.png linearregress_capomulin.png pie_pandas.png pie_pyplot.png scatter_capomulin.png pymaceuticals_analysis.ipynb (data analysis & code notebook) pymaceuticals_starter.ipynb (starter file with instructions) README.md
== Data Set ======================= Two .csv data files were provided as part of this project. See \Resources folder above or in pymaceuticals directory.
== Data Alterations ======================= Original data set contains 249 mouse IDs (numeric value based on column "Mouse ID"). The study results in file sutdy_results.csv contains duplicate drug data for mouse ID g989. All data for mouse ID g989 was removed resulting in an adjusted data set containing 248 Mouse IDs upon which all calculations and charts are based. See "Data Clean" section in Jupyter Notebook for code details.
Analysis Impact: Removing Mouse ID record g989 should not impact the overall data results and analysis.
== Known Bugs ===================== None.
== Troubleshooting =============== #print hashtags are used liberally throughout the code to run individual lines of code for additional testing/troubleshooting, and general comment hashtags are used for code notes/additional information.
Resources used to build and troubleshoot this code are listed below, in addition to help and code peer review from students, instructor, and TA's in class and external tutor (N.Tsai). Additional Git Hub Resources: B.Anderson, E.Gaga, D.Weeks
Course materials: mpg.ipynb NJ_temp.ipynb aesthetics.ipynb bar_chart.ipynb pie_chart.ipynb samples.ipynb correlation.ipynb correlations.ipynb regression.ipynb
Web URLs: https://www.latex-project.org/about/ https://jamesrledoux.com/code/group-by-aggregate-pandas https://matplotlib.org/3.1.1/gallery/misc/zorder_demo.html https://matplotlib.org/gallery/pyplots/boxplot_demo_pyplot.html https://matplotlib.org/3.1.1/gallery/statistics/boxplot_demo.html https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.scatter.html https://railsware.com/blog/python-for-machine-learning-pandas-axis-explained/ https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html https://www.shanelynn.ie/summarising-aggregation-and-grouping-data-in-python-pandas/ https://www.kite.com/python/answers/how-to-plot-pandas-dataframes-in-a-subplot-in-python https://matplotlib.org/3.1.1/gallery/text_labels_and_annotations/annotation_demo.html https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reset_index.html https://towardsdatascience.com/the-top-5-magic-commands-for-jupyter-notebooks-2bf0c5ae4bb8 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.pie.html https://www.geeksforgeeks.org/find-duplicate-rows-in-a-dataframe-based-on-all-or-selected-columns/ https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sem.html?highlight=sem#pandas.DataFrame.sem
URLs last used: January 18, 2021
== Contact Information =============== Colorado United States
== Random Notes =============== This is my first Pandas project using Matplotlib within a Jupyter Notebook. Time to complete: approximately 17 hours