Asignment-1_Q2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "017fa092",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4eb199e",
   "metadata": {},
   "source": [
    "#### q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9e4d8e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.randint(1, 100, size = (4, 4, 4))   #NP array of 4*4*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d3d8f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[82,  8, 86, 93],\n",
       "        [39, 48, 21, 31],\n",
       "        [51, 25, 52, 81],\n",
       "        [99, 29, 51, 23]],\n",
       "\n",
       "       [[92, 55, 89,  1],\n",
       "        [17, 86, 69, 20],\n",
       "        [14, 92, 65, 87],\n",
       "        [37, 57, 80, 79]],\n",
       "\n",
       "       [[89, 49,  6, 39],\n",
       "        [67, 38, 80, 58],\n",
       "        [65, 46, 95, 29],\n",
       "        [41, 23, 34, 97]],\n",
       "\n",
       "       [[65, 79, 11,  5],\n",
       "        [58, 40, 40, 10],\n",
       "        [55, 68, 36, 54],\n",
       "        [15, 85, 77, 72]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2642ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f962bd74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.890625"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf7bb13e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.890625"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc39033",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "bf81cb6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('hawks_main.csv')\n",
    "df1 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4f0e0ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(446, 9)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "a1b9a161",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df.Weight >= 500)]   #Filtered by weight > 500lb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b55c8bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(294, 9)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "4dd5ca24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Species</th>\n",
       "      <th>Wing</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Tail</th>\n",
       "      <th>Hallux</th>\n",
       "      <th>Coopers?</th>\n",
       "      <th>Red-tailed?</th>\n",
       "      <th>Sharp-shinned?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994</td>\n",
       "      <td>RT</td>\n",
       "      <td>277.0</td>\n",
       "      <td>940.0</td>\n",
       "      <td>218</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997</td>\n",
       "      <td>RT</td>\n",
       "      <td>373.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>221</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>RT</td>\n",
       "      <td>379.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>217</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>RT</td>\n",
       "      <td>392.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>229</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>RT</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>213</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2001</td>\n",
       "      <td>RT</td>\n",
       "      <td>408.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>245</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1998</td>\n",
       "      <td>RT</td>\n",
       "      <td>410.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>233</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>1995</td>\n",
       "      <td>RT</td>\n",
       "      <td>398.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>243</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>2002</td>\n",
       "      <td>RT</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>230</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1994</td>\n",
       "      <td>RT</td>\n",
       "      <td>418.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>235</td>\n",
       "      <td>341.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year Species   Wing  Weight  Tail  Hallux  Coopers?  Red-tailed?  \\\n",
       "0    1994      RT  277.0   940.0   218    30.4         0            1   \n",
       "1    1997      RT  373.0   914.0   221    30.4         0            1   \n",
       "2    2000      RT  379.0  1000.0   217    32.2         0            1   \n",
       "3    2000      RT  392.0  1185.0   229    33.0         0            1   \n",
       "4    2002      RT  365.0  1125.0   213    29.3         0            1   \n",
       "..    ...     ...    ...     ...   ...     ...       ...          ...   \n",
       "439  2001      RT  408.0  1390.0   245    32.7         0            1   \n",
       "440  1998      RT  410.0  1054.0   233    31.5         0            1   \n",
       "441  1995      RT  398.0  1240.0   243    30.8         0            1   \n",
       "443  2002      RT  412.0  1215.0   230    34.1         0            1   \n",
       "445  1994      RT  418.0  1180.0   235   341.4         0            1   \n",
       "\n",
       "     Sharp-shinned?  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "..              ...  \n",
       "439               0  \n",
       "440               0  \n",
       "441               0  \n",
       "443               0  \n",
       "445               0  \n",
       "\n",
       "[294 rows x 9 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "017590dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['Species'] == 'RT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "91d9c4d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Wing</th>\n",
       "      <th>Tail</th>\n",
       "      <th>Hallux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>749.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>216</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>775.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>200</td>\n",
       "      <td>29.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>785.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>206</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>790.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>211</td>\n",
       "      <td>27.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>805.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>217</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1525.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>224</td>\n",
       "      <td>27.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1540.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>235</td>\n",
       "      <td>35.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1560.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>228</td>\n",
       "      <td>33.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1598.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>239</td>\n",
       "      <td>35.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2030.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>196</td>\n",
       "      <td>30.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Weight   Wing  Tail  Hallux\n",
       "55    749.0  369.0   216   26.80\n",
       "201   775.0  351.0   200   29.30\n",
       "26    785.0  355.0   206   25.00\n",
       "198   790.0  360.0   211   27.60\n",
       "305   805.0  366.0   217   25.70\n",
       "..      ...    ...   ...     ...\n",
       "81   1525.0  380.0   224   27.60\n",
       "113  1540.0  420.0   235   35.50\n",
       "370  1560.0  399.0   228   33.85\n",
       "252  1598.0  480.0   239   35.40\n",
       "281  2030.0  372.0   196   30.20\n",
       "\n",
       "[279 rows x 4 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Weight', 'Wing', 'Tail', 'Hallux']]\n",
    "df.sort_values(by=['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9eb08daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2240f3e2550>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df.set_index('Weight')\n",
    "# plt.figure();\n",
    "# df.plot(x = 'Weight', figsize = (11, 7));\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 7))\n",
    "ax.plot(list(df.Weight), list(df.Wing), label = 'Wing') \n",
    "ax.plot(list(df.Weight), list(df.Tail), label = 'Tail')  \n",
    "ax.plot(list(df.Weight), list(df.Hallux), label = 'Hallux')  \n",
    "ax.set_xlabel('Weight')  \n",
    "ax.set_ylabel('Length') \n",
    "ax.set_title(\"Hawks_data\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "9c9d7615",
   "metadata": {},
   "outputs": [],
   "source": [
    "df  = df[['Wing', 'Tail']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "4e89d20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure();\n",
    "df.plot(x = 'Wing', figsize = (11, 7));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082acb41",
   "metadata": {},
   "source": [
    "#### q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "abeccc82",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dp = pd.DataFrame( np.random.randint(1, 100, size =(99,4)), index = [x for x in range(99)], columns = ['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "30cba10b",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>89</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "      <td>97</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>92</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>83</td>\n",
       "      <td>94</td>\n",
       "      <td>55</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>10</td>\n",
       "      <td>94</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>39</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C   D\n",
       "0   34  37  89  34\n",
       "1   13  51  97  62\n",
       "2   46  53  47  85\n",
       "3   15  41  52  47\n",
       "4   62   7  92  45\n",
       "..  ..  ..  ..  ..\n",
       "94  83  94  55  65\n",
       "95  10  94  40  85\n",
       "96  55  64  39  57\n",
       "97  41  52  77   7\n",
       "98  35  56  13  26\n",
       "\n",
       "[99 rows x 4 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b65d0902",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in dp.columns:\n",
    "    dp.loc[dp.sample(frac= .1).index, col] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8e1a76ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>47.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>10.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>55.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>41.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>35.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       A     B     C     D\n",
       "0   34.0  37.0   NaN  34.0\n",
       "1    NaN   NaN  97.0  62.0\n",
       "2   46.0  53.0  47.0  85.0\n",
       "3   15.0  41.0  52.0  47.0\n",
       "4   62.0   7.0   NaN  45.0\n",
       "..   ...   ...   ...   ...\n",
       "94   NaN  94.0  55.0  65.0\n",
       "95  10.0  94.0   NaN  85.0\n",
       "96  55.0  64.0   NaN  57.0\n",
       "97  41.0  52.0  77.0   7.0\n",
       "98  35.0  56.0  13.0  26.0\n",
       "\n",
       "[99 rows x 4 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f2c2926b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>55.247191</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.629213</td>\n",
       "      <td>48.651685</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>55.247191</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>50.629213</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>55.247191</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>55.247191</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A          B          C     D\n",
       "0   34.000000  37.000000  55.247191  34.0\n",
       "1   50.629213  48.651685  97.000000  62.0\n",
       "2   46.000000  53.000000  47.000000  85.0\n",
       "3   15.000000  41.000000  52.000000  47.0\n",
       "4   62.000000   7.000000  55.247191  45.0\n",
       "..        ...        ...        ...   ...\n",
       "94  50.629213  94.000000  55.000000  65.0\n",
       "95  10.000000  94.000000  55.247191  85.0\n",
       "96  55.000000  64.000000  55.247191  57.0\n",
       "97  41.000000  52.000000  77.000000   7.0\n",
       "98  35.000000  56.000000  13.000000  26.0\n",
       "\n",
       "[99 rows x 4 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp = dp.fillna(dp.mean())\n",
    "dp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92e16d6",
   "metadata": {},
   "source": [
    "#### q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "9837b672",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.randint(10, 178, size = (5, 6))\n",
    "B = np.random.randint(0, 256, size = (6, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "713ce4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 28790, 105539,  89486,  52793,  76600],\n",
       "       [ 22880,  71355,  63985,  42872,  61375],\n",
       "       [ 26535,  86585,  70551,  54288,  82353],\n",
       "       [ 18867,  61426,  47611,  29691,  61191],\n",
       "       [ 23165,  78974,  75739,  49727,  75043]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A@B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "c90d477e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.dot(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "ab7ae385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 28790, 105539,  89486,  52793,  76600],\n",
       "       [ 22880,  71355,  63985,  42872,  61375],\n",
       "       [ 26535,  86585,  70551,  54288,  82353],\n",
       "       [ 18867,  61426,  47611,  29691,  61191],\n",
       "       [ 23165,  78974,  75739,  49727,  75043]])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "b7dd1190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.6513002112041596e+20"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.det(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec62ba6",
   "metadata": {},
   "source": [
    "#### q6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "be11ca5a",
   "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>Wing</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Tail</th>\n",
       "      <th>Hallux</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>9204.0</td>\n",
       "      <td>16244.0</td>\n",
       "      <td>7599</td>\n",
       "      <td>862.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RT</th>\n",
       "      <td>108008.2</td>\n",
       "      <td>307669.0</td>\n",
       "      <td>62645</td>\n",
       "      <td>9313.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS</th>\n",
       "      <td>23224.0</td>\n",
       "      <td>18604.0</td>\n",
       "      <td>18557</td>\n",
       "      <td>1887.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Wing    Weight   Tail   Hallux\n",
       "Species                                    \n",
       "CH         9204.0   16244.0   7599   862.50\n",
       "RT       108008.2  307669.0  62645  9313.70\n",
       "SS        23224.0   18604.0  18557  1887.85"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(by=['Species']).sum().drop(columns=['Year', 'Red-tailed?', 'Coopers?', 'Sharp-shinned?'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed5b009",
   "metadata": {},
   "source": [
    "#### q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "07c5387e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, sigma = 0, 0.1 # normal distribution with mean and standard deviation\n",
    "s = np.random.normal(mu, sigma, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "06729f1f",
   "metadata": {},
   "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": [
    "count, bins, ignored = plt.hist(s, 30, density=True)\n",
    "plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) ), linewidth=2, color='r')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f456723",
   "metadata": {},
   "source": [
    "#### q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "6ca38f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.randint(2, 200, size = (4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "3f01f6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 71,  41, 185, 174],\n",
       "       [130, 123, 107,  93],\n",
       "       [188,  82,  52,   9],\n",
       "       [129, 162,  53,   8]])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "29d16173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([129, 162,  53,   8])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1] # second row\n",
    "arr[-1] # last row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "bca8e752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([185, 107,  52,  53])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:, 2] # 3rd column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "53571e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123, 107],\n",
       "       [ 82,  52]])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[3, 3] # last element\n",
    "arr[1:3, 1:3] # 2*d subarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8cc410",
   "metadata": {},
   "source": [
    "#### q9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "f261989d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dp1 = pd.DataFrame( np.random.randint(1, 100, size =(100,4)), index = [x for x in range(100)], columns = ['A', 'B', 'C', 'D'])\n",
    "dp2 = pd.DataFrame( np.random.randint(200, 400, size =(100,4)), index = [x for x in range(100)], columns = ['E', 'F', 'G', 'H'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e338b083",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53</td>\n",
       "      <td>50</td>\n",
       "      <td>93</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "      <td>83</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>81</td>\n",
       "      <td>85</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>79</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>42</td>\n",
       "      <td>93</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>53</td>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>24</td>\n",
       "      <td>74</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C   D\n",
       "0   53  50  93  31\n",
       "1   32   4  38  38\n",
       "2   51  17  83  12\n",
       "3   24  81  85  21\n",
       "4   37  79  47  54\n",
       "..  ..  ..  ..  ..\n",
       "95  42  93  28   9\n",
       "96  53  49  48  51\n",
       "97  24  67  44  44\n",
       "98  77   1  24  87\n",
       "99  24  74   6  18\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "c3509f62",
   "metadata": {},
   "outputs": [
    {
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       "      <td>342</td>\n",
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       "      <th>96</th>\n",
       "      <td>328</td>\n",
       "      <td>278</td>\n",
       "      <td>312</td>\n",
       "      <td>212</td>\n",
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       "      <th>97</th>\n",
       "      <td>283</td>\n",
       "      <td>216</td>\n",
       "      <td>393</td>\n",
       "      <td>200</td>\n",
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       "      <th>98</th>\n",
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       "      <td>200</td>\n",
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       "<p>100 rows × 4 columns</p>\n",
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       "      E    F    G    H\n",
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       "3   257  339  206  274\n",
       "4   384  239  373  399\n",
       "..  ...  ...  ...  ...\n",
       "95  240  352  340  342\n",
       "96  328  278  312  212\n",
       "97  283  216  393  200\n",
       "98  327  265  366  200\n",
       "99  338  277  341  242\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "a4fb1127",
   "metadata": {},
   "outputs": [
    {
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       "      <td>328</td>\n",
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       "      <td>312</td>\n",
       "      <td>212</td>\n",
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       "      <th>97</th>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>283</td>\n",
       "      <td>216</td>\n",
       "      <td>393</td>\n",
       "      <td>200</td>\n",
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       "      <td>327</td>\n",
       "      <td>265</td>\n",
       "      <td>366</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
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       "<p>100 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "     A   B   C   D    E    F    G    H\n",
       "0   53  50  93  31  322  339  323  386\n",
       "1   32   4  38  38  250  232  399  258\n",
       "2   51  17  83  12  246  293  208  390\n",
       "3   24  81  85  21  257  339  206  274\n",
       "4   37  79  47  54  384  239  373  399\n",
       "..  ..  ..  ..  ..  ...  ...  ...  ...\n",
       "95  42  93  28   9  240  352  340  342\n",
       "96  53  49  48  51  328  278  312  212\n",
       "97  24  67  44  44  283  216  393  200\n",
       "98  77   1  24  87  327  265  366  200\n",
       "99  24  74   6  18  338  277  341  242\n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([dp1, dp2], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4447e8f3",
   "metadata": {},
   "source": [
    "#### q10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "b9b85fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1994</td>\n",
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       "      <th>1</th>\n",
       "      <td>1997</td>\n",
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       "      <td>914.0</td>\n",
       "      <td>221</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>RT</td>\n",
       "      <td>379.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>217</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>RT</td>\n",
       "      <td>392.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>229</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>RT</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>213</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>1995</td>\n",
       "      <td>RT</td>\n",
       "      <td>398.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>243</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>2002</td>\n",
       "      <td>SS</td>\n",
       "      <td>166.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>136</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>2002</td>\n",
       "      <td>RT</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>230</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>1994</td>\n",
       "      <td>SS</td>\n",
       "      <td>201.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>151</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1994</td>\n",
       "      <td>RT</td>\n",
       "      <td>418.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>235</td>\n",
       "      <td>341.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>446 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year Species   Wing  Weight  Tail  Hallux  Coopers?  Red-tailed?  \\\n",
       "0    1994      RT  277.0   940.0   218    30.4         0            1   \n",
       "1    1997      RT  373.0   914.0   221    30.4         0            1   \n",
       "2    2000      RT  379.0  1000.0   217    32.2         0            1   \n",
       "3    2000      RT  392.0  1185.0   229    33.0         0            1   \n",
       "4    2002      RT  365.0  1125.0   213    29.3         0            1   \n",
       "..    ...     ...    ...     ...   ...     ...       ...          ...   \n",
       "441  1995      RT  398.0  1240.0   243    30.8         0            1   \n",
       "442  2002      SS  166.0   115.0   136    11.6         0            0   \n",
       "443  2002      RT  412.0  1215.0   230    34.1         0            1   \n",
       "444  1994      SS  201.0   206.0   151    13.5         0            0   \n",
       "445  1994      RT  418.0  1180.0   235   341.4         0            1   \n",
       "\n",
       "     Sharp-shinned?  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "..              ...  \n",
       "441               0  \n",
       "442               1  \n",
       "443               0  \n",
       "444               1  \n",
       "445               0  \n",
       "\n",
       "[446 rows x 9 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "d7fc64ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['Red', 'Green']\n",
    "plt.figure(figsize = (7,5))\n",
    "plt.scatter(df1.Weight, df1.Wing, marker='*', c=colors[0])\n",
    "plt.scatter(df1.Weight, df1.Tail, marker='D', c=colors[1])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}