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d45a91cc · aslesha · dbfd1b34 · d45a91cc
Commit d45a91cc authored 6 years ago by aslesha
--- a/lab8/perq1.ipynb
+++ b/lab8/perq1.ipynb
+{
+  "cells": [
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "import csv \nimport numpy as np \nimport matplotlib.pyplot as plt \nfrom sklearn import datasets",
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "import pandas as pd\nAutomobile_data= pd.read_csv('Automobile_data.csv')",
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "Automobile_data.head(n=5)",
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 3,
+          "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>index</th>\n      <th>company</th>\n      <th>body-style</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>engine-type</th>\n      <th>num-of-cylinders</th>\n      <th>horsepower</th>\n      <th>average-mileage</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>alfa-romero</td>\n      <td>convertible</td>\n      <td>88.6</td>\n      <td>168.8</td>\n      <td>dohc</td>\n      <td>four</td>\n      <td>111</td>\n      <td>21</td>\n      <td>13495.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>alfa-romero</td>\n      <td>convertible</td>\n      <td>88.6</td>\n      <td>168.8</td>\n      <td>dohc</td>\n      <td>four</td>\n      <td>111</td>\n      <td>21</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>alfa-romero</td>\n      <td>hatchback</td>\n      <td>94.5</td>\n      <td>171.2</td>\n      <td>ohcv</td>\n      <td>six</td>\n      <td>154</td>\n      <td>19</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>audi</td>\n      <td>sedan</td>\n      <td>99.8</td>\n      <td>176.6</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>102</td>\n      <td>24</td>\n      <td>13950.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>audi</td>\n      <td>sedan</td>\n      <td>99.4</td>\n      <td>176.6</td>\n      <td>ohc</td>\n      <td>five</td>\n      <td>115</td>\n      <td>18</td>\n      <td>17450.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
+            "text/plain": "   index      company   body-style  wheel-base  length engine-type  \\\n0      0  alfa-romero  convertible        88.6   168.8        dohc   \n1      1  alfa-romero  convertible        88.6   168.8        dohc   \n2      2  alfa-romero    hatchback        94.5   171.2        ohcv   \n3      3         audi        sedan        99.8   176.6         ohc   \n4      4         audi        sedan        99.4   176.6         ohc   \n\n  num-of-cylinders  horsepower  average-mileage    price  \n0             four         111               21  13495.0  \n1             four         111               21  16500.0  \n2              six         154               19  16500.0  \n3             four         102               24  13950.0  \n4             five         115               18  17450.0  "
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "Automobile_data.tail(n=5)",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 4,
+          "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>index</th>\n      <th>company</th>\n      <th>body-style</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>engine-type</th>\n      <th>num-of-cylinders</th>\n      <th>horsepower</th>\n      <th>average-mileage</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>56</th>\n      <td>81</td>\n      <td>volkswagen</td>\n      <td>sedan</td>\n      <td>97.3</td>\n      <td>171.7</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>85</td>\n      <td>27</td>\n      <td>7975.0</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>82</td>\n      <td>volkswagen</td>\n      <td>sedan</td>\n      <td>97.3</td>\n      <td>171.7</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>52</td>\n      <td>37</td>\n      <td>7995.0</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>86</td>\n      <td>volkswagen</td>\n      <td>sedan</td>\n      <td>97.3</td>\n      <td>171.7</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>100</td>\n      <td>26</td>\n      <td>9995.0</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>87</td>\n      <td>volvo</td>\n      <td>sedan</td>\n      <td>104.3</td>\n      <td>188.8</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>114</td>\n      <td>23</td>\n      <td>12940.0</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>88</td>\n      <td>volvo</td>\n      <td>wagon</td>\n      <td>104.3</td>\n      <td>188.8</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>114</td>\n      <td>23</td>\n      <td>13415.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
+            "text/plain": "    index     company body-style  wheel-base  length engine-type  \\\n56     81  volkswagen      sedan        97.3   171.7         ohc   \n57     82  volkswagen      sedan        97.3   171.7         ohc   \n58     86  volkswagen      sedan        97.3   171.7         ohc   \n59     87       volvo      sedan       104.3   188.8         ohc   \n60     88       volvo      wagon       104.3   188.8         ohc   \n\n   num-of-cylinders  horsepower  average-mileage    price  \n56             four          85               27   7975.0  \n57             four          52               37   7995.0  \n58             four         100               26   9995.0  \n59             four         114               23  12940.0  \n60             four         114               23  13415.0  "
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "print('Maximum:', Automobile_data.price.max(),Automobile_data.company)",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": "Maximum: 45400.0 0       alfa-romero\n1       alfa-romero\n2       alfa-romero\n3              audi\n4              audi\n5              audi\n6              audi\n7               bmw\n8               bmw\n9               bmw\n10              bmw\n11              bmw\n12              bmw\n13        chevrolet\n14        chevrolet\n15        chevrolet\n16            dodge\n17            dodge\n18            honda\n19            honda\n20            honda\n21            isuzu\n22            isuzu\n23            isuzu\n24           jaguar\n25           jaguar\n26           jaguar\n27            mazda\n28            mazda\n29            mazda\n          ...      \n31            mazda\n32    mercedes-benz\n33    mercedes-benz\n34    mercedes-benz\n35    mercedes-benz\n36       mitsubishi\n37       mitsubishi\n38       mitsubishi\n39       mitsubishi\n40           nissan\n41           nissan\n42           nissan\n43           nissan\n44           nissan\n45          porsche\n46          porsche\n47          porsche\n48           toyota\n49           toyota\n50           toyota\n51           toyota\n52           toyota\n53           toyota\n54           toyota\n55       volkswagen\n56       volkswagen\n57       volkswagen\n58       volkswagen\n59            volvo\n60            volvo\nName: company, Length: 61, dtype: object\n",
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "Automobile_data.company.value_counts()",
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 6,
+          "data": {
+            "text/plain": "toyota           7\nbmw              6\nnissan           5\nmazda            5\nvolkswagen       4\naudi             4\nmitsubishi       4\nmercedes-benz    4\nchevrolet        3\nalfa-romero      3\nisuzu            3\njaguar           3\nhonda            3\nporsche          3\ndodge            2\nvolvo            2\nName: company, dtype: int64"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "index = np.arange(len(Automobile_data.company.value_counts()))",
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "def plot_bar_x():\n    # this is for plotting purpose\n    index = np.arange(len(Automobile_data.company.value_counts()))\n    plt.bar(index, Automobile_data.company.value_counts())\n    plt.xlabel('company', fontsize=20)\n    plt.ylabel('No of cars', fontsize=20)\n    plt.xticks(index, Automobile_data.company.value_counts(), fontsize=5, rotation=30)\n    plt.title('company cars analysis')\n    plt.show()",
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "plot_bar_x()",
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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XDtLJamZmtoCaX1F4HNha0rCaZT6b2MxsATa/8xR+BXwdeFHSC9myL0s6pIeYERFrlJKdmZkNqvkVhf8E3iBNa70i709+p/lsQy/Wm5lZk+q2KETEXOD07Iak94CzIuJbg5SbmZkNsiLnKVwE3DtQiZiZWfWKXE+hp7EEMzMb4gpPiCepHfg8sDHpJ6pzgGnALyKiL9dnNjOzJlGoKEj6AvADYDjzDijvCXxd0jER8aMS8zMzs0HU6zEFSZ8Afgi8BXwb2B5YN7v/L+BN4NysnZmZDUFFjhS+CrwCbBoRj9UsfxiYIukiUjfSV4E/lZeimZkNliK/PtoMuKyuIOSy5b/O2pmZ2RBUpCgsBszqoc3zWTszMxuCihSFGaTxg/nZjjRnkpmZDUFFisJVwMcknSdpntlSJS0l6WxS19GVZSZoZmaDp8hA82nA7sARwOck3QfMBJYHNgJGAQ9l7czMbAjq9ZFCRLwMbAn8GBgGbA3sA/xr9vzHwFZZu16RtKikOyXdJ+l+SacWyt7MzEpV6OS1iJgDHC7pi8A6wFKkM5ofjoh3+rD/t4DtI+JVSYsAt0iaHBG39yGWmZn1U+FpLiCfQfXv/d15RATwavZ0kezmC/mYmVWkT0WhTNmV3aYBawLnRsQdDdqMB8YDtLe3D26CZgV1TJjU5207T9+ltFj18cqM1d94zRqrUbxWU+TXRwMiIt6NiI2AlYHNJG3QoM3EiBgTEWPa2toGP0kzsxZReVHoEhGzgSnAThWnYmbWsiotCpLaus55kLQYsAPpZ61mZlaBqscUVgAuysYVFiLNrXRNxTmZmbWsSotCRPyVdLEeMzNrAn0uCpKWJDtPocgJa2Zm1rwKjSlIGiZpgqRHgZeATuAlSY9my6vujjIzs37o9Ye4pOHAdcC2pBPMniDNfbQC0EG6GttOknaMiLfLT9XMzAZakSOFrwBjgUnAuhHRERFbREQHsDbwO9I8SF8pO0kzMxscRYrCZ0lTW+wZEY/UrsiuurYXcD/wufLSMzOzwVSkKKwJTI6I9xqtzJZPBtYoIzEzMxt8RYrC28DIHtosAfRltlQzM2sCRYrCX4G9JTWcfEjSaGBv4L4yEjMzs8FXpCicA7QBd0o6VNLqkhaTtJqkQ4A7svXnDESiZmY28Hr9k9SIuEzSRsAEYGKDJgLOiIjLykrOzMwGV9Err50k6WrgUNL0FF1XXrsHuCAibis/RTMzGyyFz0DOLpXpy2WamS2AmuZ6CmZmVr35HilI6lPR6O5cBjMza249dR/15ZyD6EVcMzNrQj19eD9B+pDvjZHAh/uXjpmZVWm+RSGb7G6+JC0CfAn4Wraos99ZmZlZJfo10CxpH+BB4Duk8xROANYtIS8zM6tAn/r+JW0JfBfYDJgL/AD4VkS8VGJuZmY2yAoVBUlrAqcDnyYdGVwOTIiI6QOQm5mZDbJedR9JWkbS2aTrKexFOnlty4jYtz8FQdIqkm6U9KCk+yUd09dYZmbWfz2dpzAcOBY4kTSlxWOkI4MrStr/XOC4iLhb0ihgmqQ/RsQDJcU3M7MCeuo+ehhoB14kFYdzI+LdsnYeETNJ13kmIl6R9CCwEuCiYGZWgZ6Kwqqk8xQEHA8cL6mnmBERqxZNRFIHaZK9O4pua2Zm5ejNQLOAZbLbgJA0ErgCODYiXm6wfjwwHqC9vX2g0jAzm0fHhEn92r7z9F1Ki1cfa6DMd6A5Ihbqy61IAtnJb1cAF0fEld3kMTEixkTEmLa2hhd+MzOzElQ6S6pSX9RPgAcj4ntV5mJmZtVPnb0VcBCwvaR7s9vOFedkZtayKp3NNCJuIY1ZmJlZE6j6SMHMzJqIi4KZmeVcFMzMLOeiYGZmORcFMzPLuSiYmVnORcHMzHIuCmZmlnNRMDOznIuCmZnlXBTMzCznomBmZjkXBTMzy7komJlZzkXBzMxyLgpmZpZzUTAzs5yLgpmZ5VwUzMws56JgZmY5FwUzM8tVWhQkXSDpOUl/rzIPMzNLqj5SuBDYqeIczMwsU2lRiIibgBerzMHMzN5X9ZFCr0gaL2mqpKnPP/981emYmS2whkRRiIiJETEmIsa0tbVVnY6Z2QJrSBQFMzMbHC4KZmaWq/onqb8CbgPWlvSkpEOrzMfMrNUtXOXOI+KAKvdvZmbzcveRmZnlXBTMzCznomBmZjkXBTMzy7komJlZzkXBzMxyLgpmZpZzUTAzs5yLgpmZ5VwUzMws56JgZmY5FwUzM8u5KJiZWc5FwczMci4KZmaWc1EwM7Oci4KZmeVcFMzMLOeiYGZmORcFMzPLuSiYmVmu8qIgaSdJD0t6VNKEqvMxM2tllRYFScOAc4FxwHrAAZLWqzInM7NWVvWRwmbAoxExPSLeBi4B9qg4JzOzlqWIqG7n0t7AThFxWPb8IGDziDiqrt14YHz2dG3g4QFIZzQwq0njNWussuO1Qqyy47VCrLLjtUKseqtGRFtvGi48QAn0lhos+0CVioiJwMQBTUSaGhFjmjFes8YqO14rxCo7XivEKjteK8Tqj6q7j54EVql5vjLwdEW5mJm1vKqLwl3AWpJWkzQc2B+4uuKczMxaVqXdRxExV9JRwO+BYcAFEXF/RemU3T1VZrxmjVV2vFaIVXa8VohVdrxWiNVnlQ40m5lZc6m6+8jMzJqIi4KZmeVcFABJ+0jaoKRYH5W0dBmxsnirSlqsCWONkTS62WKVHa/kWJtIWq6MWGXHa4VYZceTtKGkXv32fzBj9VdLFwVJi0raE1gfWLOEeDsC+wHrlhBrlKRjgX2BFZolVhZvQ2BHYNNmilV2vLJiSRoh6dPAF4B+/w69zHitEGsAcltc0heBbYGDmiVWWao+ea1SEfGmpCnADLKT5iQp+j76vhzwALCKpCUj4vf9SO9V4A3gBWA7SWv1I16ZsQBeJp1VvpOktyJiSpPEasrcIuItSTcAiwBLS9oyIm7ta1JlxmuFWAOQ2+uSLgdeAj7W15zKjlWWlj5SAIiI2cB7pG/49KMgANwKfAhYAmiX1OiM7d7mFcBPIuLnwFtAn7t9yoyVeRVYC5gGjOzP6yw5VtPmFhFzIuIy4F1g1X7kVHq8Vog1ALk9S5q77QBJY5slVhn8k9SMpI8Dd/SzKCBpHdIf3J8iYm4JeW0MdABXR8S7zRLLisv+xlYEflvG+19mvFaINQC5fZ50BH5FRLzXLLH6y0WhyfWzO2vAYpUdrxVya4XX2Myxyo4naaGyPsDLjNVfLgpmZpZr+TEFMzN7n4uCmZnlXBTMzCznomBmZjkXBTMzy7komJlZzkXBzMxyLgrW1CRtJulSSU9JekvSTEl/kLRvXbt9Jd0kaY6kNyT9TdKJkkY0iNmZ3UZKOkvSE9k292YTJCJpYUknSXpE0puSHlO6SmB9rLGSQtI3JW0h6fosh1ck/V7SByZfk7SipG9I+oukZyS9LelpSb+U9IHJFCV1ZPu4MHt8iaRZWV5TJe1a1/6IrP03unlPl5f0jqS/9fwvYK3GJ69Z05L0BeB80lw1VwOPAMuSZrmcHRFjs3b/DZwIzAIuJ81ZNI40++2fgU9GxDs1cTtJE6M9DiwD/BEYDhwALE6aGfVIYHNgMmm+qH2yfe8fEZfWxBoL3AhcB2wPXA/cR5p1dy/gHWDHiLi5Zpv9gQuy7Tp5f46lXYG3ga0i4r6a9h3AP4Ep2WuaDtyW5b5f9lp2iIgbs/YjgaeAOcBq9dM5SDoJ+DbwpYg4p7v331pURPjmW9PdgPVIH6gvAus3WL9ydr8FaYbbx4Hla9YvDPwuW3dS3bad2fLfASNqlv9rtvxF4C5g6Zp1q5M+sO+pizU22yaAo+rW7ZEtfwRYqGb5ssCoBq9pQ1KBmFy3vKNmH6fUrftUtvzauuXnZMt3rVsuUlF5DViq6n9n35rvVnkCvvnW6Ab8b/ah9uUe2v04aze+wbqPkI4yptct7yoKazTYZnq2bvsG624E5gLDapZ1FYV5Pvhr1k/J1m/by9d9NfAmsEjNsq6i0Fm775r1M4BZdcvW7yp8dcu7isgFVf8b+9acN48pWLP6eHY/uYd2m2T3N9SviIh/AE8Cq+mDV8ObHRGPNYj3dHY/rcG6p4BhwPIN1t0cjSc0m5Ldb1y7UNIukn6XjZG8k40BBLAbMAJodLW3e6PxzJ5PkKZsz0XE/cBNwDhJq9SsGp/d/7BBHLPWvsiONbWuD/Gnemi3VHY/s5v1M4H2rN3smuVzumk/F9Lc+92tI/Xh13u2m3jP1OWJpKOBs0kXVvkjqevrddI3+D1J3UgfGCBn3vzr82r0Be88YBvgMOAUScsDu5OKy53dxLIW56JgzarrA3Al4KH5tOv68F4eaPTNf4W6dgOlu+v+dh1VzIH0qybgVFKx2CQi5ilmkrYoMacrScXqUEnfAv6N9H/+RyXuwxYw7j6yZnV7dj+uh3b3ZPdj61dIWhNYGfhnpCvsDaStJTX6/9SVV1eeo0lHQbc2KAgjeb87rN8i/eLq/0iFdTfSEcOrwMVl7cMWPC4K1qzOJ3WLfF3SevUrJa2cPbwguz9ZUlvN+mHAmaS/8Z8McK6QflJ6ZF2Oe5AuyP4o0PWT1OdIXUWbZkWgq+0ipC6lRmMJ/TGRNNh+DrAa8MuIeKXkfdgCxN1H1pQi4gFJR5IGRO+R9FvSL3w+TDpP4RVgu4i4VdIZwAnA35Uugv4a6QhjA+AW4DuDkPJ1wHcljWPe8xTeBA7tGoSOiPck/QCYAPwte13Dge1I5x3cmD0uRUQ8LmkSaSwB3HVkPfCRgjWtiPgxsDVwDakb5qukD7dZwLk17f6DdOLZI8DngaNJf9snk05ce3sQ0r0jy3EEcBSpKN0AbBMRN9W1/TpwHOmavIeTisdU0sXbHx+A3LqOpqZGxN0DEN8WID6j2awfas5oPjUivlltNo1J+iZwCnBYRAxGV5oNYT5SMFuASRoFHEE6S/tXFadjQ4DHFMwWQJJ2If2SaTfSz2WPj4jXq83KhgIXBbMF0z7A/yOdp3AacFa16dhQ4TEFMzPLeUzBzMxyLgpmZpZzUTAzs5yLgpmZ5VwUzMws9/8B6whrPHT9ggMAAAAASUVORK5CYII=\n",
+            "text/plain": "<Figure size 432x288 with 1 Axes>"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "import seaborn as sns\nsns.set_style(\"darkgrid\")\nplt.plot(Automobile_data.company,Automobile_data.price)\nplt.show()",
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": "<Figure size 432x288 with 1 Axes>"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "name": "python36",
+      "display_name": "Python 3.6",
+      "language": "python"
+    },
+    "language_info": {
+      "mimetype": "text/x-python",
+      "nbconvert_exporter": "python",
+      "name": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6",
+      "file_extension": ".py",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
+%% Cell type:code id: tags:
+
+``` python
+import csv 
+import numpy as np 
+import matplotlib.pyplot as plt 
+from sklearn import datasets
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+Automobile_data= pd.read_csv('Automobile_data.csv')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+Automobile_data.head(n=5)
+```
+
+%% Output
+
+   index      company   body-style  wheel-base  length engine-type  \
+0      0  alfa-romero  convertible        88.6   168.8        dohc   
+1      1  alfa-romero  convertible        88.6   168.8        dohc   
+2      2  alfa-romero    hatchback        94.5   171.2        ohcv   
+3      3         audi        sedan        99.8   176.6         ohc   
+4      4         audi        sedan        99.4   176.6         ohc   
+
+  num-of-cylinders  horsepower  average-mileage    price  
+0             four         111               21  13495.0  
+1             four         111               21  16500.0  
+2              six         154               19  16500.0  
+3             four         102               24  13950.0  
+4             five         115               18  17450.0  
+
+%% Cell type:code id: tags:
+
+``` python
+Automobile_data.tail(n=5)
+```
+
+%% Output
+
+    index     company body-style  wheel-base  length engine-type  \
+56     81  volkswagen      sedan        97.3   171.7         ohc   
+57     82  volkswagen      sedan        97.3   171.7         ohc   
+58     86  volkswagen      sedan        97.3   171.7         ohc   
+59     87       volvo      sedan       104.3   188.8         ohc   
+60     88       volvo      wagon       104.3   188.8         ohc   
+
+   num-of-cylinders  horsepower  average-mileage    price  
+56             four          85               27   7975.0  
+57             four          52               37   7995.0  
+58             four         100               26   9995.0  
+59             four         114               23  12940.0  
+60             four         114               23  13415.0  
+
+%% Cell type:code id: tags:
+
+``` python
+print('Maximum:', Automobile_data.price.max(),Automobile_data.company)
+```
+
+%% Output
+
+Maximum: 45400.0 0       alfa-romero
+1       alfa-romero
+2       alfa-romero
+3              audi
+4              audi
+5              audi
+6              audi
+7               bmw
+8               bmw
+9               bmw
+10              bmw
+11              bmw
+12              bmw
+13        chevrolet
+14        chevrolet
+15        chevrolet
+16            dodge
+17            dodge
+18            honda
+19            honda
+20            honda
+21            isuzu
+22            isuzu
+23            isuzu
+24           jaguar
+25           jaguar
+26           jaguar
+27            mazda
+28            mazda
+29            mazda
+          ...      
+31            mazda
+32    mercedes-benz
+33    mercedes-benz
+34    mercedes-benz
+35    mercedes-benz
+36       mitsubishi
+37       mitsubishi
+38       mitsubishi
+39       mitsubishi
+40           nissan
+41           nissan
+42           nissan
+43           nissan
+44           nissan
+45          porsche
+46          porsche
+47          porsche
+48           toyota
+49           toyota
+50           toyota
+51           toyota
+52           toyota
+53           toyota
+54           toyota
+55       volkswagen
+56       volkswagen
+57       volkswagen
+58       volkswagen
+59            volvo
+60            volvo
+Name: company, Length: 61, dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+Automobile_data.company.value_counts()
+```
+
+%% Output
+
+toyota           7
+bmw              6
+nissan           5
+mazda            5
+volkswagen       4
+audi             4
+mitsubishi       4
+mercedes-benz    4
+chevrolet        3
+alfa-romero      3
+isuzu            3
+jaguar           3
+honda            3
+porsche          3
+dodge            2
+volvo            2
+Name: company, dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+index = np.arange(len(Automobile_data.company.value_counts()))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def plot_bar_x():
+    # this is for plotting purpose
+    index = np.arange(len(Automobile_data.company.value_counts()))
+    plt.bar(index, Automobile_data.company.value_counts())
+    plt.xlabel('company', fontsize=20)
+    plt.ylabel('No of cars', fontsize=20)
+    plt.xticks(index, Automobile_data.company.value_counts(), fontsize=5, rotation=30)
+    plt.title('company cars analysis')
+    plt.show()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+plot_bar_x()
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+import seaborn as sns
+sns.set_style("darkgrid")
+plt.plot(Automobile_data.company,Automobile_data.price)
+plt.show()
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+```