6_Heatmap_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Haetmap in Seaborn\n",
    "\n",
    "> In heatmap we represnt data with colors\n",
    "> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heatmap with arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Mahzaib Khalid\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  1.47368421,  1.94736842,  2.42105263,  2.89473684],\n",
       "       [ 3.36842105,  3.84210526,  4.31578947,  4.78947368,  5.26315789],\n",
       "       [ 5.73684211,  6.21052632,  6.68421053,  7.15789474,  7.63157895],\n",
       "       [ 8.10526316,  8.57894737,  9.05263158,  9.52631579, 10.        ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var=np.linspace(1,10,20).reshape(4,5)\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(var)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heatmap with Dataset\n",
    "\n",
    "> Categorical and String values we can't use to create heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subidr</th>\n",
       "      <th>attnr</th>\n",
       "      <th>num1</th>\n",
       "      <th>num2</th>\n",
       "      <th>num3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>divided</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>divided</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>divided</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>divided</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>divided</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>divided</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>divided</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>divided</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>divided</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>divided</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>focused</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>focused</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>focused</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>focused</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>focused</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>focused</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>focused</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>focused</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>focused</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>focused</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subidr    attnr  num1  num2  num3\n",
       "0        1  divided     2   4.0     7\n",
       "1        2  divided     3   4.0     5\n",
       "2        3  divided     3   5.0     6\n",
       "3        4  divided     5   7.0     5\n",
       "4        5  divided     4   5.0     8\n",
       "5        6  divided     5   5.0     6\n",
       "6        7  divided     5   4.5     6\n",
       "7        8  divided     5   7.0     8\n",
       "8        9  divided     2   3.0     7\n",
       "9       10  divided     6   5.0     6\n",
       "10      11  focused     6   5.0     6\n",
       "11      12  focused     8   9.0     8\n",
       "12      13  focused     6   5.0     9\n",
       "13      14  focused     8   8.0     7\n",
       "14      15  focused     8   8.0     7\n",
       "15      16  focused     6   8.0     7\n",
       "16      17  focused     7   7.0     6\n",
       "17      18  focused     7   8.0     6\n",
       "18      19  focused     5   6.0     6\n",
       "19      20  focused     6   6.0     5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=sns.load_dataset(\"anagrams\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subidr</th>\n",
       "      <th>num1</th>\n",
       "      <th>num2</th>\n",
       "      <th>num3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subidr  num1  num2  num3\n",
       "0        1     2   4.0     7\n",
       "1        2     3   4.0     5\n",
       "2        3     3   5.0     6\n",
       "3        4     5   7.0     5\n",
       "4        5     4   5.0     8\n",
       "5        6     5   5.0     6\n",
       "6        7     5   4.5     6\n",
       "7        8     5   7.0     8\n",
       "8        9     2   3.0     7\n",
       "9       10     6   5.0     6\n",
       "10      11     6   5.0     6\n",
       "11      12     8   9.0     8\n",
       "12      13     6   5.0     9\n",
       "13      14     8   8.0     7\n",
       "14      15     8   8.0     7\n",
       "15      16     6   8.0     7\n",
       "16      17     7   7.0     6\n",
       "17      18     7   8.0     6\n",
       "18      19     5   6.0     6\n",
       "19      20     6   6.0     5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping the columns that contains string values\n",
    "\n",
    "x= data.drop(columns=['attnr'],axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=x.head(5)\n",
    "sns.heatmap(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters of Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vmax,vmin parameter\n",
    "# High values means more changes in color of heatmap\n",
    "sns.heatmap(x,vmin=0,vmax=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To change color of heatmap\n",
    "\n",
    "sns.heatmap(x,vmin=0,vmax=10,cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To give values in the boxes we use annot parameter\n",
    "\n",
    "plt.show()\n",
    "sns.heatmap(x,vmin=0,vmax=10,cmap=\"PuOr\",annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To change font color and size\n",
    "\n",
    "y={\"fontsize\":20,\"color\":\"r\"}\n",
    "sns.heatmap(x,cmap=\"PuOr\",vmin=0,vmax=10,annot=True,annot_kws=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To change Boxwidth and Color\n",
    "\n",
    "y={\"fontsize\":20,\"color\":\"r\"}\n",
    "sns.heatmap(x,cmap=\"PuOr\",vmin=0,vmax=10,annot=True,annot_kws=y,linewidth=10,linecolor=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD4CAYAAADM6gxlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAV3UlEQVR4nO3de3TU5Z3H8fc3CQIGhJAwiggrHtBaI6LgpWV1xbaWUMHtEo49ttLdoghn6UXU1mpb12W1rmKrVmsF1yJYrK172FapVlsjrS0XweUSlBLBG1oQCAi5kczMs38ktdQ1iSQzPPlOPq9/mMwzvzOfPPz48OSZX87PQgiIiIgfebEDiIjIoVFxi4g4o+IWEXFGxS0i4oyKW0TEmYJsv8Hjn+upy1YyaOQpR8WOkFOGTl0YO0LOeHzV4NgRcsqkKSOttTGtuEVEnFFxi4g4o+IWEXFGxS0i4oyKW0TEGRW3iIgzKm4REWdU3CIizqi4RUScUXGLiDij4hYRcUbFLSLijIpbRMQZFbeIiDMqbhERZ1TcIiLOqLhFRJzJ+h1wurrTrryfo8+YwIF9O1l27Rmx47iWf9SxlFx8L/l9EoSQpubFRexfNS92LPdSqTTls+4gUdKP++dMjx3HrXd2vsXDj37/va+r97zDpz9xCed+/DMRU3VMty/uN5ct4rVf38eof30wdhT/0in2PHMjjdvXY0cUMujy39Kw9Tmadm2Oncy1hUuWccLQo6mpa4gdxbXEwMHMnjUXgHQ6xZzbrqT05LMip+qYbr9VUr3peRpr98SOkRNSNTto3L4egNBYS9OuzeT3HRQ5lW/bd+5l2aqXmDL+nNhRckrVlkqKBxxDUdHA2FE6pN0Vt5l9BLgYGAwE4G3glyGEl7OcTRzL7zeEI445lQNvrYkdxbVb7lvCNZdPorZeq+1MWrfhD5w+cmzsGB3W5orbzL4B/BQwYBXwQsvjR8zsujaOm25mq81s9VNbUpnMKw5Yj0IGTvkx1U9/i9BYEzuOWxUrNlLcvw+lJw6JHSWnJJNNbNy0mpGlH4sdpcPaW3FPA04JITQd/KSZfQ/YCNz6QQeFEOYB8wAe/1zPkIGc4kVeAQOn/JjaDY9Rv2lp7DSuvbhxK8+uqGTZCy/R2Jikpq6Ba29dxO3XXRY7mmubqtYyeNAw+vbpHztKh7VX3GngWOD19z0/qGVM5G8UT7yTpl2b2b/yR7GjuHf1tIlcPW0iACvXVfHgYxUq7QxYu/55Th/597FjdEp7xf014LdmVgW82fLcUGA4MCuLuQ6bM768kOKPnscRfUv45L1b+NNjc3izYkHsWC71HHI2fUZeQuOOjQy6ogKAPRU30/DKbyInE2nW2HiAqlfWM/li35dVWght72SYWR5wFs0fThqwDXghhPChNq+1VZJZI085KnaEnDJ06sLYEXLG46sGx46QUyZNGWmtjbV7VUkIIQ2syGgiERHpsG5/HbeIiDcqbhERZ1TcIiLOqLhFRJxRcYuIOKPiFhFxRsUtIuKMiltExBkVt4iIMypuERFnVNwiIs6ouEVEnFFxi4g4o+IWEXFGxS0i4oyKW0TEmXZvpNBZumNLZh135sTYEUQ+0KcuOjF2hG5DK24REWdU3CIizqi4RUScUXGLiDij4hYRcUbFLSLijIpbRMQZFbeIiDMqbhERZ1TcIiLOqLhFRJxRcYuIOKPiFhFxRsUtIuKMiltExBkVt4iIM1m/kUJXl3/UsZRcfC/5fRKEkKbmxUXsXzUvdiyXDjSlmXr3BhqTaZLpwIWnlfDlCUNjx3IvlUpTPusOEiX9uH/O9NhxXCsrK6Ow8Ejy8vIpKMhn8eJHYkfqkG5f3KRT7HnmRhq3r8eOKGTQ5b+lYetzNO3aHDuZO0cUGA/OKqWwZz5NqTRfuGsD5320iNOO7xs7mmsLlyzjhKFHU1PXEDtKTpg//wGKiopix+iUbr9VkqrZQeP29QCExlqadm0mv++gyKl8MjMKe+YDkEwFkqkQOZF/23fuZdmql5gy/pzYUaQL0Yr7IPn9hnDEMady4K01saO4lUoHyueu442d9Vx67iCttjvplvuWcM3lk6it12o7E8xg5swZmBmTJ5dTXl4eO1KHqLhbWI9CBk75MdVPf4vQWBM7jlv5ecaSr49iX12Sr/zXy1S9XcuIYwtjx3KpYsVGivv3ofTEIaxcVxU7Tk5YsOAhEokE1dW7mTFjBsOGDWP06NGxYx2yDm+VmNm/tDE23cxWm9nqxasdrBTyChg45cfUbniM+k1LY6fJCUcdWcCZw/vx+017Y0dx68WNW3l2RSUXXHYTV9+ykJVrq7j21kWxY7mWSCQAGDCgmHHjLqCysjJyoo7pzB73Ta0NhBDmhRDGhBDGXDqmVyfe4vAonngnTbs2s3/lj2JHca26pol9dUkAGhpTLN/8LickekdO5dfV0yaybPFNPLvoRu64fipnjxrB7dddFjuWW/X1ddTW1r73ePny5QwfPjxyqo5pc6vEzNa3NgQcnfk4h1/PIWfTZ+QlNO7YyKArKgDYU3EzDa/8JnIyf3a+28g3f1JFOh1IBxh/ejHnlw6IHUsEgN27q5k9+yoAkskkZWUTGDt2bORUHWMhtP7Jv5ntAD4N7Hn/EPDHEMKx7b3B63MG6tKCDDruzImxI+SUvJOnxI6QMxoS42JHyCm9e/ey1sba+3DyCaBPCGHt+wfM7LnOxRIRkY5os7hDCNPaGLs083FERKQ93f4XcEREvFFxi4g4o+IWEXFGxS0i4oyKW0TEGRW3iIgzKm4REWdU3CIizqi4RUScUXGLiDij4hYRcUbFLSLijIpbRMQZFbeIiDMqbhERZ7J+l3fdsUW6Mt21JXPe2FYTO0JOOWlE6/fr1YpbRMQZFbeIiDMqbhERZ1TcIiLOqLhFRJxRcYuIOKPiFhFxRsUtIuKMiltExBkVt4iIMypuERFnVNwiIs6ouEVEnFFxi4g4o+IWEXFGxS0i4oyKW0TEmazfAaerO9CUZurdG2hMpkmmAxeeVsKXJwyNHcslzWXmlZWVUVh4JHl5+RQU5LN48SOxI7lWU7Ofe+6+ldff2IphfOWr1/ORk0tjxzpk3b64jygwHpxVSmHPfJpSab5w1wbO+2gRpx3fN3Y0dzSX2TF//gMUFRXFjpET5s+7kzNGn811199MU1MTBw40xI7UId1+q8TMKOyZD0AyFUimQuREfmkupSurq6tl48Z1fOrC5vvg9ujRgz59fC4q2l1xm9lHgMHAyhBCzUHPjw8hPJXNcIdLKh0on7uON3bWc+m5g7RC7ATNZWaZwcyZMzAzJk8up7y8PHYkt7Zvf4t+R/Xnrjtv5tVXX2H48JO4YvrX6NWrd+xoh6zNFbeZfQX4BfBloNLMLj5o+JY2jptuZqvNbPX8X/0pM0mzKD/PWPL1UVTcdCYbXt9P1du1sSO5pbnMrAULHuKnP32Ue++9l5/97FHWrFkTO5JbqVSKLVs2Uzbhs9x19wJ69ezNYz9fFDtWh7S3VXIFMDqE8I/A+cC3zeyrLWPW2kEhhHkhhDEhhDFXTDgpI0EPh6OOLODM4f34/aa9saO4p7nMjEQiAcCAAcWMG3cBlZWVkRP5VVKSoKRkICeddAoAHx97Plu3bI6cqmPaK+78v2yPhBBeo7m8y8zse7RR3J5U1zSxry4JQENjiuWb3+WEhL8fnboCzWVm1dfXUVtb+97j5cuXM3z48Mip/CoqKqakJMG2ba8DsG7dGoYMPT5uqA5qb497u5mNCiGsBQgh1JjZRcCDwKnZDnc47Hy3kW/+pIp0OpAOMP70Ys4vHRA7lkuay8zavbua2bOvAiCZTFJWNoGxY8dGTuXb9BlX8b25N9GUTHLMMcfy1a9dHztSh1gIrX/yb2bHAckQwvYPGBsbQvhDe2+QeupLurRAuqzGf/hh7Ag5441tNe2/SD60k0aUtLqr0eaKO4SwrY2xdktbREQyr9tfxy0i4o2KW0TEGRW3iIgzKm4REWdU3CIizqi4RUScUXGLiDij4hYRcUbFLSLijIpbRMQZFbeIiDMqbhERZ1TcIiLOqLhFRJxRcYuIOKPiFhFxpr1bl3Va3slTsv0W3UpDYlzsCDnlmSd83iy2K/rURSfGjtBtaMUtIuKMiltExBkVt4iIMypuERFnVNwiIs6ouEVEnFFxi4g4o+IWEXFGxS0i4oyKW0TEGRW3iIgzKm4REWdU3CIizqi4RUScUXGLiDij4hYRcSbrN1LwIJVKUz7rDhIl/bh/zvTYcVwrKyujsPBI8vLyKSjIZ/HiR2JHcuudnW/x8KPff+/r6j3v8OlPXMK5H/9MxFS+5cr5qeIGFi5ZxglDj6amriF2lJwwf/4DFBUVxY7hXmLgYGbPmgtAOp1izm1XUnryWZFT+ZcL52e33yrZvnMvy1a9xJTx58SOItKqqi2VFA84hqKigbGjSBfQ7Vfct9y3hGsun0RtvVbbmWAGM2fOwMyYPLmc8vLy2JFywroNf+D0kWNjx3AvV87PdovbzM4CQgjhBTP7KDAe2BRC+FXW02VZxYqNFPfvQ+mJQ1i5rip2nJywYMFDJBIJqqt3M2PGDIYNG8bo0aNjx3ItmWxi46bVlF14aewo7uXK+dnmVomZ3QjcDdxnZt8F7gH6ANeZ2Q1tHDfdzFab2ep5i5/MaOBMenHjVp5dUckFl93E1bcsZOXaKq69dVHsWK4lEgkABgwoZty4C6isrIycyL9NVWsZPGgYffv0jx3FvVw5P9tbcZcDo4CewHbguBDCPjO7HVgJ3PxBB4UQ5gHzAMLrT4aMpc2wq6dN5OppEwFYua6KBx+r4PbrLoucyq/6+jrS6UBhYSH19XUsX76cK6+8MnYs99auf57TR/597Bju5dL52V5xJ0MIKaDOzLaEEPYBhBDqzSyd/Xjiye7d1cyefRUAyWSSsrIJjB2rfdnOaGw8QNUr65l8sS5T7axcOj8thNYXxGa2EhgXQqgzs7wQQrrl+X5ARQjhjPbeoCuvuD1qSIyLHSGnPPPE5tgRcsanLjoxdoSc0rt3L2ttrL0V93khhAMAfyntFj2AL2Ygm4iIHKI2i/svpf0Bz+8CdmUlkYiItKnb/wKOiIg3Km4REWdU3CIizqi4RUScUXGLiDij4hYRcUbFLSLijIpbRMQZFbeIiDMqbhERZ1TcIiLOqLhFRJxRcYuIOKPiFhFxRsUtIuJMm3fAyYT6+gbdASeDer1TETtCTtEdhTLntm8vjR0hp9w4d3Krd8DRiltExBkVt4iIMypuERFnVNwiIs6ouEVEnFFxi4g4o+IWEXFGxS0i4oyKW0TEGRW3iIgzKm4REWdU3CIizqi4RUScUXGLiDij4hYRcUbFLSLijIpbRMSZgtgBuoKysjIKC48kLy+fgoJ8Fi9+JHYk11KpNOWz7iBR0o/750yPHcc1nZuZdc65wzn97GFAYMef9/GLR1eTSqZjxzpkKu4W8+c/QFFRUewYOWHhkmWcMPRoauoaYkfJCTo3M6PvUb0469zh/PC2p0km05Rfdjalo4awbvXrsaMdMm2VSEZt37mXZateYsr4c2JHEfl/8vKMgh75WJ7Ro0c++/fVx47UIYe84jazhSGEqdkIE4sZzJw5AzNj8uRyysvLY0dy65b7lnDN5ZOorddqOxN0bmbO/n0NLH+uiqu+NYGmphRbNu9g6+Z3YsfqkDaL28x++f6ngHFm1h8ghDCpleOmA9MBfvCDe5g2bVrnk2bRggUPkUgkqK7ezYwZMxg2bBijR4+OHcudihUbKe7fh9ITh7ByXVXsODlB52bm9Ordg5NKB3HXLU/SUN/ElKnncOoZQ9jw4puxox2y9lbcxwEvAQ8AgebiHgPc0dZBIYR5wDyA+vqG0PmY2ZVIJAAYMKCYceMuoLKyUv84OuDFjVt5dkUly154icbGJDV1DVx76yJuv+6y2NHc0rmZOSeMSLB3dy11tY0AvLzhLYYcX+yyuNvb4x4DrAFuAN4NITwH1IcQloUQlmU73OFQX19HbW3te4+XL1/O8OHDI6fy6eppE1m2+CaeXXQjd1w/lbNHjVBpd4LOzcx6d28dg/+umIIe+QAMG5Fg1479kVN1TJsr7hBCGvi+mf285c8d7R3jze7d1cyefRUAyWSSsrIJjB07NnIqEZ2bmfbWG3t4ef02rrzqE6TTaf781l7WrHg1dqwOsRA+/E6GmX0GGBtCuP7DHuNhq8STXu9UxI6QUxoS42JHyBm3fXtp7Ag55ca5k621sUNaPYcQlgL62xERiUjXcYuIOKPiFhFxRsUtIuKMiltExBkVt4iIMypuERFnVNwiIs6ouEVEnFFxi4g4o+IWEXFGxS0i4oyKW0TEGRW3iIgzKm4REWdU3CIizqi4RUScOaQ74OQyM5vecpNjyQDNZ+ZoLjMrF+ZTK+6/mh47QI7RfGaO5jKz3M+niltExBkVt4iIMyruv3K959UFaT4zR3OZWe7nUx9Oiog4oxW3iIgzKm4REWe6bXGb2b+Z2TUf8PyxZvZYK8c8Z2Zjsp8u95nZFDPbaGZpzWnnmdntZrbJzNab2RIz6x87k2dmNqdlLtea2dNmdmzsTAfrtsXdmhDC2yGE8kM5xszys5Unh1UC/wT8LnaQHPEMUBpCGAlsBr4ZOY93t4cQRoYQRgFPAN+JnOdv5FRxm1mhmS01s3VmVmlml5jZa2ZW0jI+xsyeO+iQ08zsWTOrMrMrWl5zvJlVtjzubWY/bfmf91Gg90HvVWNm/25mK4GPHb7v8vBqmY+XzWx+ywr56ZZ5ee+nDzMrMbPXWh7/s5n9j5k9bmavmtksM5ttZv9rZivMbABACOHlEMKfIn5rUWRxPp8OISRb3mYFcFyUb/Awy+J87jvobQqBLnUVR04VNzAeeDuEcFoIoRR4qp3XjwQ+Q3PxfucDfhyaCdS1rGJuBkYfNFYIVIYQzg4hPJ+Z+F3WCODeEMIpwF5gcjuvLwUuBc6ied7qQginA8uBqVnM6UW25/NLwJMZS9v1ZWU+zexmM3sT+DxacWfVBuCTZvafZnZuCOHddl7/ixBCfQhhF1BB81/kwc4DHgYIIawH1h80lgL+O0O5u7pXQwhrWx6vAY5v5/UVIYT9IYSdwLvA4y3Pb/gQx3YHWZtPM7sBSAI/yVRYB7IynyGEG0IIQ2iey1mZDNxZOVXcIYTNNK+KNwDfNbPv0HwS/+X77PX+Q9r5urXnABpCCKmOZnXmwEGPU0ABbc/rwa9PH/R1uuXY7i4r82lmXwQuAj4futcvaGT7/FxM+6v4wyqnirtlq6MuhPAwMBc4A3iNv25xvH/yLzazXmZWDJwPvPC+8d/R/GMSZlZK89aKNHuNv87rIX2YKx/oNToxn2Y2HvgGMCmEUJfBXF69Rufmc8RBX04CNmUgU8bkVHEDpwKrzGwtcAPwH8BNwF1m9nua/zc+2CpgKc0f5swJIbz9vvH7gD5mth74esvrpdlcYKaZ/REoOdSDzeyzZraN5s8XlprZrzMd0JlOzSdwD9AXeKblErYfZTSdP52dz1tbLnBYD1wIfDWj6TpJv/IuIuJMrq24RURynopbRMQZFbeIiDMqbhERZ1TcIiLOqLhFRJxRcYuIOPN/lAPVSz56R8cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To change Color bar and title\n",
    "\n",
    "sns.heatmap(x,cmap=\"PuOr\",vmin=0,vmax=10,annot=True,cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8de2fd29472d8520918489d2f7698684b73d156be72bb6a0af57d9dbbf4a217c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}