資料分析範例/幽浮報告.ipynb

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   "cell_type": "code",
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     "data": {
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       "\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></th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>Ithaca</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>Willingboro</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <th>Holyoke</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <th>Abilene</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>New York Worlds Fair</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <th>Grant Park</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>12/31/2000 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <th>Spirit Lake</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>12/31/2000 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WI</th>\n",
       "      <th>Eagle River</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagle River</th>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <th>Ybor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>12/31/2000 23:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Colors Reported Shape Reported              Time\n",
       "State City                                                                 \n",
       "NY    Ithaca                           NaN       TRIANGLE    6/1/1930 22:00\n",
       "NJ    Willingboro                      NaN          OTHER   6/30/1930 20:00\n",
       "CO    Holyoke                          NaN           OVAL   2/15/1931 14:00\n",
       "KS    Abilene                          NaN           DISK    6/1/1931 13:00\n",
       "NY    New York Worlds Fair             NaN          LIGHT   4/18/1933 19:00\n",
       "...                                    ...            ...               ...\n",
       "IL    Grant Park                       NaN       TRIANGLE  12/31/2000 23:00\n",
       "IA    Spirit Lake                      NaN           DISK  12/31/2000 23:00\n",
       "WI    Eagle River                      NaN            NaN  12/31/2000 23:45\n",
       "      Eagle River                      RED          LIGHT  12/31/2000 23:45\n",
       "FL    Ybor                             NaN           OVAL  12/31/2000 23:59\n",
       "\n",
       "[18241 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "CSV_URL = 'http://bit.ly/uforeports'\n",
    "response = requests.get(CSV_URL)\n",
    "if response.status_code == 200:\n",
    "    with open('uforeports.csv','wb') as f:\n",
    "        for chunk in response.iter_content(chunk_size=128):\n",
    "            f.write(chunk)\n",
    "else:\n",
    "    print('下載錯誤')\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"uforeports.csv\")\n",
    "df1 = df.set_index(['State', 'City'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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></th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th>Adak</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>6/1/1986 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>1/15/1998 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/1/1999 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anchorage</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>6/10/1949 21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anchorage</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>8/30/1972 1:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">WY</th>\n",
       "      <th>Wheeling</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/15/1984 20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>7/12/1967 2:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>4/20/1983 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yellowstone National Park</th>\n",
       "      <td>NaN</td>\n",
       "      <td>EGG</td>\n",
       "      <td>6/30/1971 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yellowstone Park</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>7/15/1956 0:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Colors Reported Shape Reported  \\\n",
       "State City                                                       \n",
       "AK    Adak                                  NaN          LIGHT   \n",
       "      Alaska                                NaN           DISK   \n",
       "      Alaska                                NaN          LIGHT   \n",
       "      Anchorage                             NaN           DISK   \n",
       "      Anchorage                            BLUE         CIRCLE   \n",
       "...                                         ...            ...   \n",
       "WY    Wheeling                              NaN            NaN   \n",
       "      Wyoming                               NaN          CIGAR   \n",
       "      Wyoming                               NaN          OTHER   \n",
       "      Yellowstone National Park             NaN            EGG   \n",
       "      Yellowstone Park                      NaN           DISK   \n",
       "\n",
       "                                            Time  \n",
       "State City                                        \n",
       "AK    Adak                         6/1/1986 0:00  \n",
       "      Alaska                     1/15/1998 13:00  \n",
       "      Alaska                      9/1/1999 23:00  \n",
       "      Anchorage                  6/10/1949 21:00  \n",
       "      Anchorage                   8/30/1972 1:30  \n",
       "...                                          ...  \n",
       "WY    Wheeling                   6/15/1984 20:30  \n",
       "      Wyoming                     7/12/1967 2:00  \n",
       "      Wyoming                     4/20/1983 0:00  \n",
       "      Yellowstone National Park  6/30/1971 14:00  \n",
       "      Yellowstone Park            7/15/1956 0:00  \n",
       "\n",
       "[18241 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "AK     116\n",
       "AL     193\n",
       "AR     206\n",
       "AZ     738\n",
       "CA    2529\n",
       "CO     367\n",
       "CT     225\n",
       "Ca       1\n",
       "DE      43\n",
       "FL     837\n",
       "Fl       4\n",
       "GA     325\n",
       "HI      85\n",
       "IA     162\n",
       "ID     130\n",
       "IL     613\n",
       "IN     326\n",
       "KS     176\n",
       "KY     244\n",
       "LA     174\n",
       "MA     322\n",
       "MD     215\n",
       "ME     181\n",
       "MI     591\n",
       "MN     254\n",
       "MO     448\n",
       "MS     139\n",
       "MT     144\n",
       "NC     356\n",
       "ND      51\n",
       "NE     101\n",
       "NH     125\n",
       "NJ     370\n",
       "NM     241\n",
       "NV     284\n",
       "NY     914\n",
       "OH     667\n",
       "OK     193\n",
       "OR     534\n",
       "PA     598\n",
       "RI      67\n",
       "SC     166\n",
       "SD      57\n",
       "TN     286\n",
       "TX    1027\n",
       "UT     193\n",
       "VA     299\n",
       "VT      44\n",
       "WA    1322\n",
       "WI     357\n",
       "WV     132\n",
       "WY      69\n",
       "Name: Time, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.groupby(level=['State']).count()\n",
    "df3 = df2['Time']\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '美國各州幽浮出現次數')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "font=FontProperties(fname='/Library/Fonts/Arial Unicode.ttf',size=15)\n",
    "figure = plt.figure()\n",
    "ax1 = figure.add_subplot(1, 1, 1)\n",
    "df3.plot.barh(ax = ax1)\n",
    "ax1.figure.set_size_inches(25, 40)\n",
    "ax1.set_title(\"美國各州幽浮出現次數\",fontproperties=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'AK各城幽浮出現次數'}, ylabel='City'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4 = df1.groupby(level=['State','City']).count()\n",
    "figure1 = plt.figure()\n",
    "figure1.set_size_inches(25, 40)\n",
    "states = np.sort(df['State'].unique())\n",
    "ax2 = figure1.add_subplot(1,1,1)\n",
    "ax2.figure.set_size_inches(25, 40)\n",
    "ax2.set_title(\"AK各城幽浮出現次數\",fontproperties=font)\n",
    "stateFrame = df4.loc['AK','Time'] \n",
    "stateFrame.plot.barh(ax = ax2)"
   ]
  }
 ],
 "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}