{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Homework: Plotting With Pandas\n", "\n", "_Authors: Kevin Coyle (L.A.)_\n", "\n", "---\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Pandas: Plotting Practice Problems\n", "\n", "In this homework, you're going to write code for a few problems. \n", "\n", "You'll practice the following programming concepts we've covered in class:\n", "* Plotting with Pandas.\n", "* Determining best plot given a data set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #1. Import Pandas, `Matplotlib.pyplot`, and NumPy. Don't forget the line that makes `matplotlib` render in a Jupyter Notebook!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #2. Read in the NBA players `csv` into a variable called `nba_df`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a data set of NBA players from 2015. The filename is `NBA_players_2015.csv`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #3. Look at the first five rows of the data set." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #4. Create a histogram of the `age` column." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #5. Create a histogram of the `age` column, but change the number of bins to `20`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #6. Discuss the difference in the two plots and the implications." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While skewed, the plot with fewer bins leads one to believe that the bin to the right of the highest-numbered age bin is the second largest. The second-largest bin occurs right after 22 and before 25." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #7. Rename the `position` column `'pos'` with the following `C:5`, `G:1`, and `F:3`. Then create a scatter matrix plot with the `'pos'`, `'pts'`, `'age'`, and `'fg'` columns." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### #8. Plot the number of guards, centers, and forwards in this data set." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The end!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Great job!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }