{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<small><small><i>\n",
    "All the IPython Notebooks in **[Python Seaborn Module](https://github.com/milaan9/12_Python_Seaborn_Module)** lecture series by **[Dr. Milaan Parmar](https://www.linkedin.com/in/milaanparmar/)** are available @ **[GitHub](https://github.com/milaan9)**\n",
    "</i></small></small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/milaan9/12_Python_Seaborn_Module/blob/main/018_Seaborn_Heat_Map.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is heat map?\n",
    "\n",
    "A heat map (or heatmap) is a graphical representation of data where the individual values contained in a matrix are represented as colors. It is a bit like looking a data table from above. It is really useful to display a general view of numerical data, not to extract specific data point. It is quite straight forward to make a heat map, as shown on the examples below. However be careful to understand the underlying mechanisms. You will probably need to normalise your matrix, choose a relevant colour palette, use cluster analysis and thus permute the rows and the columns of the matrix to place similar values near each other according to the clustering.\n",
    "\n",
    "A **[heatmap](http://seaborn.pydata.org/generated/seaborn.heatmap.html?highlight=heatmap#seaborn.heatmap)** is a plot of rectangular data as a color-encoded matrix. As parameter it takes a 2D dataset. That dataset can be coerced into an ndarray.\n",
    "\n",
    "This is a great way to visualize data, because it can show the relation between variabels including time. For instance, the number of fligths through the years.\n",
    "\n",
    "Various types of heatmap can be found **[here](https://python-graph-gallery.com/90-heatmaps-with-various-input-format/)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T17:50:40.741915Z",
     "start_time": "2021-07-18T17:50:14.092290Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap\n",
    "\n",
    "The heatmap plot below is based on random values generated by numpy. Many parameters are possible, this just shows the most basic plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T17:50:42.787791Z",
     "start_time": "2021-07-18T17:50:40.744845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a heatmap for a numpy array:\n",
    "\n",
    "uniform_data = np.random.rand(10,12)\n",
    "#uniform_data = np.arange(1,17).reshape(4,4)\n",
    "sns.heatmap(uniform_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T17:50:43.224313Z",
     "start_time": "2021-07-18T17:50:42.797561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [2, 3, 4, 1],\n",
       "       [5, 4, 2, 1],\n",
       "       [6, 7, 8, 5]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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weKeNOoj1OlCragH4KPCP7Jl4+FJVPdptq7oz7W10PbYF+CBwXpKHBsvFXTeqIxuBu5I8zJ4CZGtV3d5xmw5avb5sSpLeTL2uUCXpzWSgSlIjBqokNWKgSlIjBqokNWKgSlIjBqokNfJ/IRNWuyzvdDcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([[1,2,3,4],[2,3,4,1],[5,4,2,1],[6,7,8,5]])\n",
    "sns.heatmap(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Three** main types of **input** exist to plot heatmap, let’s study them one by one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wide format (untidy)\n",
    "\n",
    "We call **‘wide format‘** or **‘untidy format‘** a matrix where each row is an individual, and each column represents an observation. In this case, a **heatmap** consists to make a visual representation of the matrix: each square of the heatmap represents a cell. The color of the cell changes following its value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T17:50:43.691106Z",
     "start_time": "2021-07-18T17:50:43.238962Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.random((5,5)), columns=[\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
    "\n",
    "# Default heatmap: just a visualization of this square matrix\n",
    "p1 = sns.heatmap(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap colors\n",
    "\n",
    "The heatmap colors plot below uses random data again. This time it’s using a different color map (cmap), with the ‘Blues’ palette which as nothing but colors of bue. It also uses square blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T17:50:44.107116Z",
     "start_time": "2021-07-18T17:50:43.708194Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "\n",
    "ax1 = sns.heatmap(corr, cbar=0, linewidths=2,vmax=1, vmin=0, square=True, cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap data\n",
    "\n",
    "The heatmap data plot is similar, but uses a different color palette. It uses the airline or flights dataset that’s included in seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T17:50:45.328780Z",
     "start_time": "2021-07-18T17:50:44.113950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "flights = sns.load_dataset(\"flights\")\n",
    "flights = flights.pivot(\"month\", \"year\", \"passengers\")\n",
    "ax = sns.heatmap(flights)\n",
    "plt.title(\"Heatmap Flight Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next class we'll learn **[Cluster Map](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/019_Seaborn_Cluster_Map.ipynb)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
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    "lenVar": 40
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 "nbformat": 4,
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}