Created using Colaboratory

Author: DeepthiTabithaBennet

Stem&LeafDisplay_CrossTabulation.ipynb

@@ -0,0 +1,104 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOkPw5OKBHEeeVu/lIDPZuA",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DeepthiTabithaBennet/Python_AppliedStatistics/blob/main/Stem%26LeafDisplay_CrossTabulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TuCzqvKPvrPE"
+      },
+      "source": [
+        "**Stem and Leaf Display**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hcJrM0A5uwjT"
+      },
+      "source": [
+        "# Written by Deepthi Tabitha Bennet\n",
+        "\n",
+        "!pip install stemgraphic"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IoRuQCM7ovW-"
+      },
+      "source": [
+        "import stemgraphic\n",
+        "  \n",
+        "data = [23, 56, 76, 34, 87, 56, 98, 12, 46, 98, 9, 34, 56, 76, 35, 12, 84, 36, 45, 23, 12, 87, 3, 78, 94, 47]\n",
+        "  \n",
+        "stemgraphic.stem_graphic(data, scale = 10)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-fgwBjOQwIRq"
+      },
+      "source": [
+        "**Cross Tabulation**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LbkOj8xPx72N"
+      },
+      "source": [
+        "# importing packages\n",
+        "import pandas\n",
+        "import numpy\n",
+        "\n",
+        "# creating some data\n",
+        "a = numpy.array([\"Bad\", \"Good\", \"Very Good\", \"Excellent\", \"Excellent\", \"Very Good\", \"Good\", \"Bad\",\n",
+        "\t\t\t\t\"Bad\", \"Good\", \"Very Good\", \"Excellent\", \"Excellent\", \"Very Good\", \"Good\", \"Bad\",\n",
+        "\t\t\t\t\"Bad\", \"Good\", \"Very Good\", \"Excellent\", \"Excellent\", \"Very Good\", \"Good\", \"Bad\",\n",
+        "\t\t\t\t\"Bad\", \"Good\", \"Very Good\", \"Excellent\", \"Excellent\", \"Very Good\", \"Good\", \"Bad\"],\n",
+        "\t\t\t\tdtype=object)\n",
+        "\n",
+        "b = numpy.array([\"0 - 100\", \"100 - 250\", \"250 - 500\", \"500 - 1000\", \"1000 - 2500\", \"2500 - 5000\", \"5000 - 7500\", \"7500 - 10000\", \"10000 - 15000\", \"15000 - 20000\", \"20000 - 30000\", \"Above 30000\", \n",
+        "\t\t\t\t\"0 - 100\", \"100 - 250\", \"250 - 500\", \"500 - 1000\", \"1000 - 2500\", \"2500 - 5000\", \"5000 - 7500\", \"7500 - 10000\", \"10000 - 15000\", \"15000 - 20000\", \"20000 - 30000\", \"Above 30000\",\n",
+        "\t\t\t\t\"0 - 100\", \"100 - 250\", \"250 - 500\", \"500 - 1000\", \"1000 - 2500\", \"2500 - 5000\", \"5000 - 7500\", \"7500 - 10000\"],\n",
+        "\t\t\t\tdtype=object)\n",
+        "\n",
+        "# form the cross tab\n",
+        "pandas.crosstab(b, a, rownames=['Cost (in Rs.)'], colnames=['Rating'])\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}