{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this lesson we will learn how to create a **basic pandas plot**. Discover why MatPlotLib is Python's default charting library and how it is used to create **Pandas visualizations**.\n", "\n", "Pandas is built on top of Numpy and MatPlotLib. It uses MatPlotLib for most of its charting capabilities. \n", "\n", "> Tip: If you ever have a plotting question and are not finding an answer. Try searching for it using the ***MatPlotLib*** keyword instead of Pandas. \n", "\n", "Let's start by importing Pandas. Note that we do not have to import MatPlotLib as it is already part of Pandas. But in order to get the version of MatPlotLib installed on my machine, I need to import it as shown below. \n", "\n", "You can set up your Notebook to automatically import frequently used libraries. To do this, search online for the specific steps. I personally try to avoid any custom configurations." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib as mpl\n", "import sys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the versions of the libraries I'm currently on." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python: 3.11.7 \n", "Pandas: 2.2.1\n", "MatPlotLib: 3.8.4\n" ] } ], "source": [ "print('Python: ' + sys.version.split('|')[0])\n", "print('Pandas: ' + pd.__version__)\n", "print('MatPlotLib: ' + mpl.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's steamroll straight into creating our dataframe. We are going to be creating a Pandas dataframe out of a Python dictionary. If you haven't seen my post on [creating Pandas dataframes](https://hedaro.com/Programming-Languages/Python/Pandas/Pandas---Create-DataFrame), I encourage you to do that before moving on. \n", "\n", "Here we have a one column dataframe with a few numeric rows. This is the data we will be plotting. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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