{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "61d37431-11a2-4e6f-b714-6b4928123fad", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "802e2a95-0f59-4f64-bc2d-d5736e852fad", "metadata": {}, "source": [ "###
San Jose State University
Department of Applied Data Science

**DATA 200
Computational Programming for Data Analytics**

Spring 2024
Instructor: Ron Mak

**Assignment #12
`pandas` and `seaborn`graphs**

Assigned: April 25, 2024
Due: May 2 at 5:30 PM

120 points maximum
Individual work only!
" ] }, { "cell_type": "markdown", "id": "17d912f7-8858-479f-81fb-bcc35dd498a5", "metadata": {}, "source": [ "#### This assignment will give you practice making various graphs using `pandas DataFrame` graphing methods and using the `seaborn` module." ] }, { "cell_type": "markdown", "id": "ebb884a2-c72d-4a8c-8c45-7de8447ff3e5", "metadata": {}, "source": [ "#### **PROBLEM 1.** [20 points each] Choose three of the graphs that you did for Assignment #11 and redo them using `DataFrame` methods. Do not use `seaborn`.\n", "#### If you prefer, can may another data source other than `covid_data.csv` or create completly different graphs." ] }, { "cell_type": "markdown", "id": "9b630a46-886b-4e06-93ea-2beee7f32082", "metadata": {}, "source": [ "#### **PROBLEM 2.** [20 points] Create three more graphs, this time use `seaborn`. They can be the same graphs you created for Problem 1, three different graphs from Assignment #11, or three completely different graphs." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }