├── README.md
├── hello.ipynb
├── .gitignore
├── prediction.ipynb
└── Main.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # Weather-Analysis-Jupyter
2 | Weather-Analysis-Jupyter
3 | 
4 | Run ```jupyter notebook```
5 | 


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/hello.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 2,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "hello\n"
13 |      ]
14 |     }
15 |    ],
16 |    "source": [
17 |     "print(\"hello\")"
18 |    ]
19 |   },
20 |   {
21 |    "cell_type": "code",
22 |    "execution_count": 2,
23 |    "metadata": {},
24 |    "outputs": [],
25 |    "source": [
26 |     "a =10"
27 |    ]
28 |   },
29 |   {
30 |    "cell_type": "code",
31 |    "execution_count": 3,
32 |    "metadata": {},
33 |    "outputs": [],
34 |    "source": [
35 |     "b=20\n"
36 |    ]
37 |   },
38 |   {
39 |    "cell_type": "code",
40 |    "execution_count": 4,
41 |    "metadata": {},
42 |    "outputs": [],
43 |    "source": [
44 |     "c =a+b"
45 |    ]
46 |   },
47 |   {
48 |    "cell_type": "code",
49 |    "execution_count": 5,
50 |    "metadata": {},
51 |    "outputs": [
52 |     {
53 |      "name": "stdout",
54 |      "output_type": "stream",
55 |      "text": [
56 |       "30\n"
57 |      ]
58 |     }
59 |    ],
60 |    "source": [
61 |     "print(c)"
62 |    ]
63 |   },
64 |   {
65 |    "cell_type": "code",
66 |    "execution_count": null,
67 |    "metadata": {},
68 |    "outputs": [],
69 |    "source": []
70 |   }
71 |  ],
72 |  "metadata": {
73 |   "kernelspec": {
74 |    "display_name": "Python 3.10.1 64-bit",
75 |    "language": "python",
76 |    "name": "python3"
77 |   },
78 |   "language_info": {
79 |    "codemirror_mode": {
80 |     "name": "ipython",
81 |     "version": 3
82 |    },
83 |    "file_extension": ".py",
84 |    "mimetype": "text/x-python",
85 |    "name": "python",
86 |    "nbconvert_exporter": "python",
87 |    "pygments_lexer": "ipython3",
88 |    "version": "3.10.1"
89 |   },
90 |   "vscode": {
91 |    "interpreter": {
92 |     "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
93 |    }
94 |   }
95 |  },
96 |  "nbformat": 4,
97 |  "nbformat_minor": 4
98 | }
99 | 


--------------------------------------------------------------------------------
/.gitignore:
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  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | *.py,cover
 51 | .hypothesis/
 52 | .pytest_cache/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # Jupyter Notebook
 78 | .ipynb_checkpoints
 79 | 
 80 | # IPython
 81 | profile_default/
 82 | ipython_config.py
 83 | 
 84 | # pyenv
 85 | .python-version
 86 | 
 87 | # pipenv
 88 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 89 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 90 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 91 | #   install all needed dependencies.
 92 | #Pipfile.lock
 93 | 
 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 95 | __pypackages__/
 96 | 
 97 | # Celery stuff
 98 | celerybeat-schedule
 99 | celerybeat.pid
100 | 
101 | # SageMath parsed files
102 | *.sage.py
103 | 
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 | 
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 | 
117 | # Rope project settings
118 | .ropeproject
119 | 
120 | # mkdocs documentation
121 | /site
122 | 
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 | 
128 | # Pyre type checker
129 | .pyre/
130 | 


--------------------------------------------------------------------------------
/prediction.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 2,
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "df = pd.read_csv(\"data/data2001To2020.csv\",skiprows = 13)"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 3,
 24 |    "metadata": {},
 25 |    "outputs": [
 26 |     {
 27 |      "data": {
 28 |       "text/plain": [
 29 |        "(175310, 9)"
 30 |       ]
 31 |      },
 32 |      "execution_count": 3,
 33 |      "metadata": {},
 34 |      "output_type": "execute_result"
 35 |     }
 36 |    ],
 37 |    "source": [
 38 |     "df.shape"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 4,
 44 |    "metadata": {},
 45 |    "outputs": [
 46 |     {
 47 |      "data": {
 48 |       "text/html": [
 49 |        "<div>\n",
 50 |        "<style scoped>\n",
 51 |        "    .dataframe tbody tr th:only-of-type {\n",
 52 |        "        vertical-align: middle;\n",
 53 |        "    }\n",
 54 |        "\n",
 55 |        "    .dataframe tbody tr th {\n",
 56 |        "        vertical-align: top;\n",
 57 |        "    }\n",
 58 |        "\n",
 59 |        "    .dataframe thead th {\n",
 60 |        "        text-align: right;\n",
 61 |        "    }\n",
 62 |        "</style>\n",
 63 |        "<table border=\"1\" class=\"dataframe\">\n",
 64 |        "  <thead>\n",
 65 |        "    <tr style=\"text-align: right;\">\n",
 66 |        "      <th></th>\n",
 67 |        "      <th>YEAR</th>\n",
 68 |        "      <th>MO</th>\n",
 69 |        "      <th>DY</th>\n",
 70 |        "      <th>HR</th>\n",
 71 |        "      <th>T2M</th>\n",
 72 |        "      <th>QV2M</th>\n",
 73 |        "      <th>RH2M</th>\n",
 74 |        "      <th>WS10M</th>\n",
 75 |        "      <th>WD10M</th>\n",
 76 |        "    </tr>\n",
 77 |        "  </thead>\n",
 78 |        "  <tbody>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>0</th>\n",
 81 |        "      <td>2001</td>\n",
 82 |        "      <td>1</td>\n",
 83 |        "      <td>1</td>\n",
 84 |        "      <td>5</td>\n",
 85 |        "      <td>11.63</td>\n",
 86 |        "      <td>8.24</td>\n",
 87 |        "      <td>95.75</td>\n",
 88 |        "      <td>1.43</td>\n",
 89 |        "      <td>152.31</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>1</th>\n",
 93 |        "      <td>2001</td>\n",
 94 |        "      <td>1</td>\n",
 95 |        "      <td>1</td>\n",
 96 |        "      <td>6</td>\n",
 97 |        "      <td>11.23</td>\n",
 98 |        "      <td>8.30</td>\n",
 99 |        "      <td>99.62</td>\n",
100 |        "      <td>1.88</td>\n",
101 |        "      <td>165.79</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>2</th>\n",
105 |        "      <td>2001</td>\n",
106 |        "      <td>1</td>\n",
107 |        "      <td>1</td>\n",
108 |        "      <td>7</td>\n",
109 |        "      <td>12.86</td>\n",
110 |        "      <td>8.73</td>\n",
111 |        "      <td>93.94</td>\n",
112 |        "      <td>2.62</td>\n",
113 |        "      <td>175.21</td>\n",
114 |        "    </tr>\n",
115 |        "    <tr>\n",
116 |        "      <th>3</th>\n",
117 |        "      <td>2001</td>\n",
118 |        "      <td>1</td>\n",
119 |        "      <td>1</td>\n",
120 |        "      <td>8</td>\n",
121 |        "      <td>13.87</td>\n",
122 |        "      <td>8.97</td>\n",
123 |        "      <td>90.44</td>\n",
124 |        "      <td>2.34</td>\n",
125 |        "      <td>179.04</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>4</th>\n",
129 |        "      <td>2001</td>\n",
130 |        "      <td>1</td>\n",
131 |        "      <td>1</td>\n",
132 |        "      <td>9</td>\n",
133 |        "      <td>15.67</td>\n",
134 |        "      <td>8.85</td>\n",
135 |        "      <td>79.69</td>\n",
136 |        "      <td>2.11</td>\n",
137 |        "      <td>176.38</td>\n",
138 |        "    </tr>\n",
139 |        "    <tr>\n",
140 |        "      <th>...</th>\n",
141 |        "      <td>...</td>\n",
142 |        "      <td>...</td>\n",
143 |        "      <td>...</td>\n",
144 |        "      <td>...</td>\n",
145 |        "      <td>...</td>\n",
146 |        "      <td>...</td>\n",
147 |        "      <td>...</td>\n",
148 |        "      <td>...</td>\n",
149 |        "      <td>...</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>175305</th>\n",
153 |        "      <td>2020</td>\n",
154 |        "      <td>12</td>\n",
155 |        "      <td>31</td>\n",
156 |        "      <td>19</td>\n",
157 |        "      <td>12.76</td>\n",
158 |        "      <td>3.72</td>\n",
159 |        "      <td>40.44</td>\n",
160 |        "      <td>2.99</td>\n",
161 |        "      <td>285.92</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>175306</th>\n",
165 |        "      <td>2020</td>\n",
166 |        "      <td>12</td>\n",
167 |        "      <td>31</td>\n",
168 |        "      <td>20</td>\n",
169 |        "      <td>12.05</td>\n",
170 |        "      <td>3.60</td>\n",
171 |        "      <td>41.12</td>\n",
172 |        "      <td>3.01</td>\n",
173 |        "      <td>292.30</td>\n",
174 |        "    </tr>\n",
175 |        "    <tr>\n",
176 |        "      <th>175307</th>\n",
177 |        "      <td>2020</td>\n",
178 |        "      <td>12</td>\n",
179 |        "      <td>31</td>\n",
180 |        "      <td>21</td>\n",
181 |        "      <td>11.43</td>\n",
182 |        "      <td>3.48</td>\n",
183 |        "      <td>41.44</td>\n",
184 |        "      <td>3.03</td>\n",
185 |        "      <td>298.68</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>175308</th>\n",
189 |        "      <td>2020</td>\n",
190 |        "      <td>12</td>\n",
191 |        "      <td>31</td>\n",
192 |        "      <td>22</td>\n",
193 |        "      <td>10.84</td>\n",
194 |        "      <td>3.36</td>\n",
195 |        "      <td>41.44</td>\n",
196 |        "      <td>3.04</td>\n",
197 |        "      <td>305.57</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>175309</th>\n",
201 |        "      <td>2020</td>\n",
202 |        "      <td>12</td>\n",
203 |        "      <td>31</td>\n",
204 |        "      <td>23</td>\n",
205 |        "      <td>10.31</td>\n",
206 |        "      <td>3.23</td>\n",
207 |        "      <td>41.25</td>\n",
208 |        "      <td>3.01</td>\n",
209 |        "      <td>311.95</td>\n",
210 |        "    </tr>\n",
211 |        "  </tbody>\n",
212 |        "</table>\n",
213 |        "<p>175310 rows × 9 columns</p>\n",
214 |        "</div>"
215 |       ],
216 |       "text/plain": [
217 |        "        YEAR  MO  DY  HR    T2M  QV2M   RH2M  WS10M   WD10M\n",
218 |        "0       2001   1   1   5  11.63  8.24  95.75   1.43  152.31\n",
219 |        "1       2001   1   1   6  11.23  8.30  99.62   1.88  165.79\n",
220 |        "2       2001   1   1   7  12.86  8.73  93.94   2.62  175.21\n",
221 |        "3       2001   1   1   8  13.87  8.97  90.44   2.34  179.04\n",
222 |        "4       2001   1   1   9  15.67  8.85  79.69   2.11  176.38\n",
223 |        "...      ...  ..  ..  ..    ...   ...    ...    ...     ...\n",
224 |        "175305  2020  12  31  19  12.76  3.72  40.44   2.99  285.92\n",
225 |        "175306  2020  12  31  20  12.05  3.60  41.12   3.01  292.30\n",
226 |        "175307  2020  12  31  21  11.43  3.48  41.44   3.03  298.68\n",
227 |        "175308  2020  12  31  22  10.84  3.36  41.44   3.04  305.57\n",
228 |        "175309  2020  12  31  23  10.31  3.23  41.25   3.01  311.95\n",
229 |        "\n",
230 |        "[175310 rows x 9 columns]"
231 |       ]
232 |      },
233 |      "execution_count": 4,
234 |      "metadata": {},
235 |      "output_type": "execute_result"
236 |     }
237 |    ],
238 |    "source": [
239 |     "df.dropna()"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "code",
244 |    "execution_count": 5,
245 |    "metadata": {},
246 |    "outputs": [
247 |     {
248 |      "data": {
249 |       "text/html": [
250 |        "<div>\n",
251 |        "<style scoped>\n",
252 |        "    .dataframe tbody tr th:only-of-type {\n",
253 |        "        vertical-align: middle;\n",
254 |        "    }\n",
255 |        "\n",
256 |        "    .dataframe tbody tr th {\n",
257 |        "        vertical-align: top;\n",
258 |        "    }\n",
259 |        "\n",
260 |        "    .dataframe thead th {\n",
261 |        "        text-align: right;\n",
262 |        "    }\n",
263 |        "</style>\n",
264 |        "<table border=\"1\" class=\"dataframe\">\n",
265 |        "  <thead>\n",
266 |        "    <tr style=\"text-align: right;\">\n",
267 |        "      <th></th>\n",
268 |        "      <th>YEAR</th>\n",
269 |        "      <th>MO</th>\n",
270 |        "      <th>DY</th>\n",
271 |        "      <th>HR</th>\n",
272 |        "      <th>T2M</th>\n",
273 |        "      <th>QV2M</th>\n",
274 |        "      <th>RH2M</th>\n",
275 |        "      <th>WS10M</th>\n",
276 |        "      <th>WD10M</th>\n",
277 |        "    </tr>\n",
278 |        "  </thead>\n",
279 |        "  <tbody>\n",
280 |        "    <tr>\n",
281 |        "      <th>count</th>\n",
282 |        "      <td>175310.000000</td>\n",
283 |        "      <td>175310.000000</td>\n",
284 |        "      <td>175310.000000</td>\n",
285 |        "      <td>175310.000000</td>\n",
286 |        "      <td>175310.000000</td>\n",
287 |        "      <td>175310.000000</td>\n",
288 |        "      <td>175310.000000</td>\n",
289 |        "      <td>175310.000000</td>\n",
290 |        "      <td>175310.000000</td>\n",
291 |        "    </tr>\n",
292 |        "    <tr>\n",
293 |        "      <th>mean</th>\n",
294 |        "      <td>2010.501283</td>\n",
295 |        "      <td>6.523245</td>\n",
296 |        "      <td>15.730477</td>\n",
297 |        "      <td>11.500542</td>\n",
298 |        "      <td>26.273119</td>\n",
299 |        "      <td>10.716629</td>\n",
300 |        "      <td>49.699891</td>\n",
301 |        "      <td>2.907421</td>\n",
302 |        "      <td>207.538628</td>\n",
303 |        "    </tr>\n",
304 |        "    <tr>\n",
305 |        "      <th>std</th>\n",
306 |        "      <td>5.766298</td>\n",
307 |        "      <td>3.448559</td>\n",
308 |        "      <td>8.799665</td>\n",
309 |        "      <td>6.922024</td>\n",
310 |        "      <td>8.666733</td>\n",
311 |        "      <td>6.487275</td>\n",
312 |        "      <td>26.519416</td>\n",
313 |        "      <td>1.581211</td>\n",
314 |        "      <td>103.475862</td>\n",
315 |        "    </tr>\n",
316 |        "    <tr>\n",
317 |        "      <th>min</th>\n",
318 |        "      <td>2001.000000</td>\n",
319 |        "      <td>1.000000</td>\n",
320 |        "      <td>1.000000</td>\n",
321 |        "      <td>0.000000</td>\n",
322 |        "      <td>0.010000</td>\n",
323 |        "      <td>0.670000</td>\n",
324 |        "      <td>1.560000</td>\n",
325 |        "      <td>0.010000</td>\n",
326 |        "      <td>0.000000</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>25%</th>\n",
330 |        "      <td>2006.000000</td>\n",
331 |        "      <td>4.000000</td>\n",
332 |        "      <td>8.000000</td>\n",
333 |        "      <td>6.000000</td>\n",
334 |        "      <td>20.100000</td>\n",
335 |        "      <td>5.190000</td>\n",
336 |        "      <td>26.380000</td>\n",
337 |        "      <td>1.830000</td>\n",
338 |        "      <td>107.290000</td>\n",
339 |        "    </tr>\n",
340 |        "    <tr>\n",
341 |        "      <th>50%</th>\n",
342 |        "      <td>2011.000000</td>\n",
343 |        "      <td>7.000000</td>\n",
344 |        "      <td>16.000000</td>\n",
345 |        "      <td>12.000000</td>\n",
346 |        "      <td>27.010000</td>\n",
347 |        "      <td>8.420000</td>\n",
348 |        "      <td>47.380000</td>\n",
349 |        "      <td>2.660000</td>\n",
350 |        "      <td>246.180000</td>\n",
351 |        "    </tr>\n",
352 |        "    <tr>\n",
353 |        "      <th>75%</th>\n",
354 |        "      <td>2016.000000</td>\n",
355 |        "      <td>10.000000</td>\n",
356 |        "      <td>23.000000</td>\n",
357 |        "      <td>18.000000</td>\n",
358 |        "      <td>32.000000</td>\n",
359 |        "      <td>17.330000</td>\n",
360 |        "      <td>71.690000</td>\n",
361 |        "      <td>3.600000</td>\n",
362 |        "      <td>296.750000</td>\n",
363 |        "    </tr>\n",
364 |        "    <tr>\n",
365 |        "      <th>max</th>\n",
366 |        "      <td>2020.000000</td>\n",
367 |        "      <td>12.000000</td>\n",
368 |        "      <td>31.000000</td>\n",
369 |        "      <td>23.000000</td>\n",
370 |        "      <td>49.470000</td>\n",
371 |        "      <td>25.450000</td>\n",
372 |        "      <td>100.000000</td>\n",
373 |        "      <td>14.330000</td>\n",
374 |        "      <td>359.920000</td>\n",
375 |        "    </tr>\n",
376 |        "  </tbody>\n",
377 |        "</table>\n",
378 |        "</div>"
379 |       ],
380 |       "text/plain": [
381 |        "                YEAR             MO             DY             HR  \\\n",
382 |        "count  175310.000000  175310.000000  175310.000000  175310.000000   \n",
383 |        "mean     2010.501283       6.523245      15.730477      11.500542   \n",
384 |        "std         5.766298       3.448559       8.799665       6.922024   \n",
385 |        "min      2001.000000       1.000000       1.000000       0.000000   \n",
386 |        "25%      2006.000000       4.000000       8.000000       6.000000   \n",
387 |        "50%      2011.000000       7.000000      16.000000      12.000000   \n",
388 |        "75%      2016.000000      10.000000      23.000000      18.000000   \n",
389 |        "max      2020.000000      12.000000      31.000000      23.000000   \n",
390 |        "\n",
391 |        "                 T2M           QV2M           RH2M          WS10M  \\\n",
392 |        "count  175310.000000  175310.000000  175310.000000  175310.000000   \n",
393 |        "mean       26.273119      10.716629      49.699891       2.907421   \n",
394 |        "std         8.666733       6.487275      26.519416       1.581211   \n",
395 |        "min         0.010000       0.670000       1.560000       0.010000   \n",
396 |        "25%        20.100000       5.190000      26.380000       1.830000   \n",
397 |        "50%        27.010000       8.420000      47.380000       2.660000   \n",
398 |        "75%        32.000000      17.330000      71.690000       3.600000   \n",
399 |        "max        49.470000      25.450000     100.000000      14.330000   \n",
400 |        "\n",
401 |        "               WD10M  \n",
402 |        "count  175310.000000  \n",
403 |        "mean      207.538628  \n",
404 |        "std       103.475862  \n",
405 |        "min         0.000000  \n",
406 |        "25%       107.290000  \n",
407 |        "50%       246.180000  \n",
408 |        "75%       296.750000  \n",
409 |        "max       359.920000  "
410 |       ]
411 |      },
412 |      "execution_count": 5,
413 |      "metadata": {},
414 |      "output_type": "execute_result"
415 |     }
416 |    ],
417 |    "source": [
418 |     "df.describe()"
419 |    ]
420 |   },
421 |   {
422 |    "cell_type": "code",
423 |    "execution_count": 8,
424 |    "metadata": {},
425 |    "outputs": [
426 |     {
427 |      "ename": "NameError",
428 |      "evalue": "name 'x' is not defined",
429 |      "output_type": "error",
430 |      "traceback": [
431 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
432 |       "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
433 |       "\u001b[1;32m<ipython-input-8-9d19cd88b2de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
434 |       "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
435 |      ]
436 |     }
437 |    ],
438 |    "source": [
439 |     "from sklearn.model_selection import train_test_split\n",
440 |     "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.20)"
441 |    ]
442 |   },
443 |   {
444 |    "cell_type": "code",
445 |    "execution_count": null,
446 |    "metadata": {},
447 |    "outputs": [],
448 |    "source": [
449 |     "print(X_train.shape, X_test.shape, y_train.shap, y_test.shape)"
450 |    ]
451 |   },
452 |   {
453 |    "cell_type": "code",
454 |    "execution_count": null,
455 |    "metadata": {},
456 |    "outputs": [],
457 |    "source": []
458 |   },
459 |   {
460 |    "cell_type": "code",
461 |    "execution_count": null,
462 |    "metadata": {},
463 |    "outputs": [],
464 |    "source": []
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": null,
469 |    "metadata": {},
470 |    "outputs": [],
471 |    "source": []
472 |   },
473 |   {
474 |    "cell_type": "code",
475 |    "execution_count": null,
476 |    "metadata": {},
477 |    "outputs": [],
478 |    "source": []
479 |   },
480 |   {
481 |    "cell_type": "code",
482 |    "execution_count": null,
483 |    "metadata": {},
484 |    "outputs": [],
485 |    "source": []
486 |   }
487 |  ],
488 |  "metadata": {
489 |   "kernelspec": {
490 |    "display_name": "Python 3",
491 |    "language": "python",
492 |    "name": "python3"
493 |   },
494 |   "language_info": {
495 |    "codemirror_mode": {
496 |     "name": "ipython",
497 |     "version": 3
498 |    },
499 |    "file_extension": ".py",
500 |    "mimetype": "text/x-python",
501 |    "name": "python",
502 |    "nbconvert_exporter": "python",
503 |    "pygments_lexer": "ipython3",
504 |    "version": "3.8.3"
505 |   }
506 |  },
507 |  "nbformat": 4,
508 |  "nbformat_minor": 4
509 | }
510 | 


--------------------------------------------------------------------------------
/Main.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 29,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 30,
  15 |    "metadata": {
  16 |     "scrolled": true
  17 |    },
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "df = pd.read_csv(\"data/data2001To2020.csv\",skiprows = 13)"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 80,
  26 |    "metadata": {},
  27 |    "outputs": [
  28 |     {
  29 |      "data": {
  30 |       "text/plain": [
  31 |        "(175310, 9)"
  32 |       ]
  33 |      },
  34 |      "execution_count": 80,
  35 |      "metadata": {},
  36 |      "output_type": "execute_result"
  37 |     }
  38 |    ],
  39 |    "source": [
  40 |     "df.shape"
  41 |    ]
  42 |   },
  43 |   {
  44 |    "cell_type": "code",
  45 |    "execution_count": 31,
  46 |    "metadata": {},
  47 |    "outputs": [
  48 |     {
  49 |      "data": {
  50 |       "text/html": [
  51 |        "<div>\n",
  52 |        "<style scoped>\n",
  53 |        "    .dataframe tbody tr th:only-of-type {\n",
  54 |        "        vertical-align: middle;\n",
  55 |        "    }\n",
  56 |        "\n",
  57 |        "    .dataframe tbody tr th {\n",
  58 |        "        vertical-align: top;\n",
  59 |        "    }\n",
  60 |        "\n",
  61 |        "    .dataframe thead th {\n",
  62 |        "        text-align: right;\n",
  63 |        "    }\n",
  64 |        "</style>\n",
  65 |        "<table border=\"1\" class=\"dataframe\">\n",
  66 |        "  <thead>\n",
  67 |        "    <tr style=\"text-align: right;\">\n",
  68 |        "      <th></th>\n",
  69 |        "      <th>YEAR</th>\n",
  70 |        "      <th>MO</th>\n",
  71 |        "      <th>DY</th>\n",
  72 |        "      <th>HR</th>\n",
  73 |        "      <th>T2M</th>\n",
  74 |        "      <th>QV2M</th>\n",
  75 |        "      <th>RH2M</th>\n",
  76 |        "      <th>WS10M</th>\n",
  77 |        "      <th>WD10M</th>\n",
  78 |        "    </tr>\n",
  79 |        "  </thead>\n",
  80 |        "  <tbody>\n",
  81 |        "    <tr>\n",
  82 |        "      <th>0</th>\n",
  83 |        "      <td>2001</td>\n",
  84 |        "      <td>1</td>\n",
  85 |        "      <td>1</td>\n",
  86 |        "      <td>5</td>\n",
  87 |        "      <td>11.63</td>\n",
  88 |        "      <td>8.24</td>\n",
  89 |        "      <td>95.75</td>\n",
  90 |        "      <td>1.43</td>\n",
  91 |        "      <td>152.31</td>\n",
  92 |        "    </tr>\n",
  93 |        "    <tr>\n",
  94 |        "      <th>1</th>\n",
  95 |        "      <td>2001</td>\n",
  96 |        "      <td>1</td>\n",
  97 |        "      <td>1</td>\n",
  98 |        "      <td>6</td>\n",
  99 |        "      <td>11.23</td>\n",
 100 |        "      <td>8.30</td>\n",
 101 |        "      <td>99.62</td>\n",
 102 |        "      <td>1.88</td>\n",
 103 |        "      <td>165.79</td>\n",
 104 |        "    </tr>\n",
 105 |        "    <tr>\n",
 106 |        "      <th>2</th>\n",
 107 |        "      <td>2001</td>\n",
 108 |        "      <td>1</td>\n",
 109 |        "      <td>1</td>\n",
 110 |        "      <td>7</td>\n",
 111 |        "      <td>12.86</td>\n",
 112 |        "      <td>8.73</td>\n",
 113 |        "      <td>93.94</td>\n",
 114 |        "      <td>2.62</td>\n",
 115 |        "      <td>175.21</td>\n",
 116 |        "    </tr>\n",
 117 |        "    <tr>\n",
 118 |        "      <th>3</th>\n",
 119 |        "      <td>2001</td>\n",
 120 |        "      <td>1</td>\n",
 121 |        "      <td>1</td>\n",
 122 |        "      <td>8</td>\n",
 123 |        "      <td>13.87</td>\n",
 124 |        "      <td>8.97</td>\n",
 125 |        "      <td>90.44</td>\n",
 126 |        "      <td>2.34</td>\n",
 127 |        "      <td>179.04</td>\n",
 128 |        "    </tr>\n",
 129 |        "    <tr>\n",
 130 |        "      <th>4</th>\n",
 131 |        "      <td>2001</td>\n",
 132 |        "      <td>1</td>\n",
 133 |        "      <td>1</td>\n",
 134 |        "      <td>9</td>\n",
 135 |        "      <td>15.67</td>\n",
 136 |        "      <td>8.85</td>\n",
 137 |        "      <td>79.69</td>\n",
 138 |        "      <td>2.11</td>\n",
 139 |        "      <td>176.38</td>\n",
 140 |        "    </tr>\n",
 141 |        "  </tbody>\n",
 142 |        "</table>\n",
 143 |        "</div>"
 144 |       ],
 145 |       "text/plain": [
 146 |        "   YEAR  MO  DY  HR    T2M  QV2M   RH2M  WS10M   WD10M\n",
 147 |        "0  2001   1   1   5  11.63  8.24  95.75   1.43  152.31\n",
 148 |        "1  2001   1   1   6  11.23  8.30  99.62   1.88  165.79\n",
 149 |        "2  2001   1   1   7  12.86  8.73  93.94   2.62  175.21\n",
 150 |        "3  2001   1   1   8  13.87  8.97  90.44   2.34  179.04\n",
 151 |        "4  2001   1   1   9  15.67  8.85  79.69   2.11  176.38"
 152 |       ]
 153 |      },
 154 |      "execution_count": 31,
 155 |      "metadata": {},
 156 |      "output_type": "execute_result"
 157 |     }
 158 |    ],
 159 |    "source": [
 160 |     "df.head()"
 161 |    ]
 162 |   },
 163 |   {
 164 |    "cell_type": "code",
 165 |    "execution_count": 32,
 166 |    "metadata": {},
 167 |    "outputs": [
 168 |     {
 169 |      "data": {
 170 |       "text/html": [
 171 |        "<div>\n",
 172 |        "<style scoped>\n",
 173 |        "    .dataframe tbody tr th:only-of-type {\n",
 174 |        "        vertical-align: middle;\n",
 175 |        "    }\n",
 176 |        "\n",
 177 |        "    .dataframe tbody tr th {\n",
 178 |        "        vertical-align: top;\n",
 179 |        "    }\n",
 180 |        "\n",
 181 |        "    .dataframe thead th {\n",
 182 |        "        text-align: right;\n",
 183 |        "    }\n",
 184 |        "</style>\n",
 185 |        "<table border=\"1\" class=\"dataframe\">\n",
 186 |        "  <thead>\n",
 187 |        "    <tr style=\"text-align: right;\">\n",
 188 |        "      <th></th>\n",
 189 |        "      <th>YEAR</th>\n",
 190 |        "      <th>MO</th>\n",
 191 |        "      <th>DY</th>\n",
 192 |        "      <th>HR</th>\n",
 193 |        "      <th>T2M</th>\n",
 194 |        "      <th>QV2M</th>\n",
 195 |        "      <th>RH2M</th>\n",
 196 |        "      <th>WS10M</th>\n",
 197 |        "      <th>WD10M</th>\n",
 198 |        "    </tr>\n",
 199 |        "  </thead>\n",
 200 |        "  <tbody>\n",
 201 |        "    <tr>\n",
 202 |        "      <th>175305</th>\n",
 203 |        "      <td>2020</td>\n",
 204 |        "      <td>12</td>\n",
 205 |        "      <td>31</td>\n",
 206 |        "      <td>19</td>\n",
 207 |        "      <td>12.76</td>\n",
 208 |        "      <td>3.72</td>\n",
 209 |        "      <td>40.44</td>\n",
 210 |        "      <td>2.99</td>\n",
 211 |        "      <td>285.92</td>\n",
 212 |        "    </tr>\n",
 213 |        "    <tr>\n",
 214 |        "      <th>175306</th>\n",
 215 |        "      <td>2020</td>\n",
 216 |        "      <td>12</td>\n",
 217 |        "      <td>31</td>\n",
 218 |        "      <td>20</td>\n",
 219 |        "      <td>12.05</td>\n",
 220 |        "      <td>3.60</td>\n",
 221 |        "      <td>41.12</td>\n",
 222 |        "      <td>3.01</td>\n",
 223 |        "      <td>292.30</td>\n",
 224 |        "    </tr>\n",
 225 |        "    <tr>\n",
 226 |        "      <th>175307</th>\n",
 227 |        "      <td>2020</td>\n",
 228 |        "      <td>12</td>\n",
 229 |        "      <td>31</td>\n",
 230 |        "      <td>21</td>\n",
 231 |        "      <td>11.43</td>\n",
 232 |        "      <td>3.48</td>\n",
 233 |        "      <td>41.44</td>\n",
 234 |        "      <td>3.03</td>\n",
 235 |        "      <td>298.68</td>\n",
 236 |        "    </tr>\n",
 237 |        "    <tr>\n",
 238 |        "      <th>175308</th>\n",
 239 |        "      <td>2020</td>\n",
 240 |        "      <td>12</td>\n",
 241 |        "      <td>31</td>\n",
 242 |        "      <td>22</td>\n",
 243 |        "      <td>10.84</td>\n",
 244 |        "      <td>3.36</td>\n",
 245 |        "      <td>41.44</td>\n",
 246 |        "      <td>3.04</td>\n",
 247 |        "      <td>305.57</td>\n",
 248 |        "    </tr>\n",
 249 |        "    <tr>\n",
 250 |        "      <th>175309</th>\n",
 251 |        "      <td>2020</td>\n",
 252 |        "      <td>12</td>\n",
 253 |        "      <td>31</td>\n",
 254 |        "      <td>23</td>\n",
 255 |        "      <td>10.31</td>\n",
 256 |        "      <td>3.23</td>\n",
 257 |        "      <td>41.25</td>\n",
 258 |        "      <td>3.01</td>\n",
 259 |        "      <td>311.95</td>\n",
 260 |        "    </tr>\n",
 261 |        "  </tbody>\n",
 262 |        "</table>\n",
 263 |        "</div>"
 264 |       ],
 265 |       "text/plain": [
 266 |        "        YEAR  MO  DY  HR    T2M  QV2M   RH2M  WS10M   WD10M\n",
 267 |        "175305  2020  12  31  19  12.76  3.72  40.44   2.99  285.92\n",
 268 |        "175306  2020  12  31  20  12.05  3.60  41.12   3.01  292.30\n",
 269 |        "175307  2020  12  31  21  11.43  3.48  41.44   3.03  298.68\n",
 270 |        "175308  2020  12  31  22  10.84  3.36  41.44   3.04  305.57\n",
 271 |        "175309  2020  12  31  23  10.31  3.23  41.25   3.01  311.95"
 272 |       ]
 273 |      },
 274 |      "execution_count": 32,
 275 |      "metadata": {},
 276 |      "output_type": "execute_result"
 277 |     }
 278 |    ],
 279 |    "source": [
 280 |     "df.tail()"
 281 |    ]
 282 |   },
 283 |   {
 284 |    "cell_type": "code",
 285 |    "execution_count": 39,
 286 |    "metadata": {},
 287 |    "outputs": [
 288 |     {
 289 |      "data": {
 290 |       "text/html": [
 291 |        "<div>\n",
 292 |        "<style scoped>\n",
 293 |        "    .dataframe tbody tr th:only-of-type {\n",
 294 |        "        vertical-align: middle;\n",
 295 |        "    }\n",
 296 |        "\n",
 297 |        "    .dataframe tbody tr th {\n",
 298 |        "        vertical-align: top;\n",
 299 |        "    }\n",
 300 |        "\n",
 301 |        "    .dataframe thead th {\n",
 302 |        "        text-align: right;\n",
 303 |        "    }\n",
 304 |        "</style>\n",
 305 |        "<table border=\"1\" class=\"dataframe\">\n",
 306 |        "  <thead>\n",
 307 |        "    <tr style=\"text-align: right;\">\n",
 308 |        "      <th></th>\n",
 309 |        "      <th>YEAR</th>\n",
 310 |        "      <th>MO</th>\n",
 311 |        "      <th>DY</th>\n",
 312 |        "      <th>HR</th>\n",
 313 |        "      <th>T2M</th>\n",
 314 |        "      <th>QV2M</th>\n",
 315 |        "      <th>RH2M</th>\n",
 316 |        "      <th>WS10M</th>\n",
 317 |        "      <th>WD10M</th>\n",
 318 |        "    </tr>\n",
 319 |        "  </thead>\n",
 320 |        "  <tbody>\n",
 321 |        "    <tr>\n",
 322 |        "      <th>count</th>\n",
 323 |        "      <td>175310.000000</td>\n",
 324 |        "      <td>175310.000000</td>\n",
 325 |        "      <td>175310.000000</td>\n",
 326 |        "      <td>175310.000000</td>\n",
 327 |        "      <td>175310.000000</td>\n",
 328 |        "      <td>175310.000000</td>\n",
 329 |        "      <td>175310.000000</td>\n",
 330 |        "      <td>175310.000000</td>\n",
 331 |        "      <td>175310.000000</td>\n",
 332 |        "    </tr>\n",
 333 |        "    <tr>\n",
 334 |        "      <th>mean</th>\n",
 335 |        "      <td>2010.501283</td>\n",
 336 |        "      <td>6.523245</td>\n",
 337 |        "      <td>15.730477</td>\n",
 338 |        "      <td>11.500542</td>\n",
 339 |        "      <td>26.273119</td>\n",
 340 |        "      <td>10.716629</td>\n",
 341 |        "      <td>49.699891</td>\n",
 342 |        "      <td>2.907421</td>\n",
 343 |        "      <td>207.538628</td>\n",
 344 |        "    </tr>\n",
 345 |        "    <tr>\n",
 346 |        "      <th>std</th>\n",
 347 |        "      <td>5.766298</td>\n",
 348 |        "      <td>3.448559</td>\n",
 349 |        "      <td>8.799665</td>\n",
 350 |        "      <td>6.922024</td>\n",
 351 |        "      <td>8.666733</td>\n",
 352 |        "      <td>6.487275</td>\n",
 353 |        "      <td>26.519416</td>\n",
 354 |        "      <td>1.581211</td>\n",
 355 |        "      <td>103.475862</td>\n",
 356 |        "    </tr>\n",
 357 |        "    <tr>\n",
 358 |        "      <th>min</th>\n",
 359 |        "      <td>2001.000000</td>\n",
 360 |        "      <td>1.000000</td>\n",
 361 |        "      <td>1.000000</td>\n",
 362 |        "      <td>0.000000</td>\n",
 363 |        "      <td>0.010000</td>\n",
 364 |        "      <td>0.670000</td>\n",
 365 |        "      <td>1.560000</td>\n",
 366 |        "      <td>0.010000</td>\n",
 367 |        "      <td>0.000000</td>\n",
 368 |        "    </tr>\n",
 369 |        "    <tr>\n",
 370 |        "      <th>25%</th>\n",
 371 |        "      <td>2006.000000</td>\n",
 372 |        "      <td>4.000000</td>\n",
 373 |        "      <td>8.000000</td>\n",
 374 |        "      <td>6.000000</td>\n",
 375 |        "      <td>20.100000</td>\n",
 376 |        "      <td>5.190000</td>\n",
 377 |        "      <td>26.380000</td>\n",
 378 |        "      <td>1.830000</td>\n",
 379 |        "      <td>107.290000</td>\n",
 380 |        "    </tr>\n",
 381 |        "    <tr>\n",
 382 |        "      <th>50%</th>\n",
 383 |        "      <td>2011.000000</td>\n",
 384 |        "      <td>7.000000</td>\n",
 385 |        "      <td>16.000000</td>\n",
 386 |        "      <td>12.000000</td>\n",
 387 |        "      <td>27.010000</td>\n",
 388 |        "      <td>8.420000</td>\n",
 389 |        "      <td>47.380000</td>\n",
 390 |        "      <td>2.660000</td>\n",
 391 |        "      <td>246.180000</td>\n",
 392 |        "    </tr>\n",
 393 |        "    <tr>\n",
 394 |        "      <th>75%</th>\n",
 395 |        "      <td>2016.000000</td>\n",
 396 |        "      <td>10.000000</td>\n",
 397 |        "      <td>23.000000</td>\n",
 398 |        "      <td>18.000000</td>\n",
 399 |        "      <td>32.000000</td>\n",
 400 |        "      <td>17.330000</td>\n",
 401 |        "      <td>71.690000</td>\n",
 402 |        "      <td>3.600000</td>\n",
 403 |        "      <td>296.750000</td>\n",
 404 |        "    </tr>\n",
 405 |        "    <tr>\n",
 406 |        "      <th>max</th>\n",
 407 |        "      <td>2020.000000</td>\n",
 408 |        "      <td>12.000000</td>\n",
 409 |        "      <td>31.000000</td>\n",
 410 |        "      <td>23.000000</td>\n",
 411 |        "      <td>49.470000</td>\n",
 412 |        "      <td>25.450000</td>\n",
 413 |        "      <td>100.000000</td>\n",
 414 |        "      <td>14.330000</td>\n",
 415 |        "      <td>359.920000</td>\n",
 416 |        "    </tr>\n",
 417 |        "  </tbody>\n",
 418 |        "</table>\n",
 419 |        "</div>"
 420 |       ],
 421 |       "text/plain": [
 422 |        "                YEAR             MO             DY             HR  \\\n",
 423 |        "count  175310.000000  175310.000000  175310.000000  175310.000000   \n",
 424 |        "mean     2010.501283       6.523245      15.730477      11.500542   \n",
 425 |        "std         5.766298       3.448559       8.799665       6.922024   \n",
 426 |        "min      2001.000000       1.000000       1.000000       0.000000   \n",
 427 |        "25%      2006.000000       4.000000       8.000000       6.000000   \n",
 428 |        "50%      2011.000000       7.000000      16.000000      12.000000   \n",
 429 |        "75%      2016.000000      10.000000      23.000000      18.000000   \n",
 430 |        "max      2020.000000      12.000000      31.000000      23.000000   \n",
 431 |        "\n",
 432 |        "                 T2M           QV2M           RH2M          WS10M  \\\n",
 433 |        "count  175310.000000  175310.000000  175310.000000  175310.000000   \n",
 434 |        "mean       26.273119      10.716629      49.699891       2.907421   \n",
 435 |        "std         8.666733       6.487275      26.519416       1.581211   \n",
 436 |        "min         0.010000       0.670000       1.560000       0.010000   \n",
 437 |        "25%        20.100000       5.190000      26.380000       1.830000   \n",
 438 |        "50%        27.010000       8.420000      47.380000       2.660000   \n",
 439 |        "75%        32.000000      17.330000      71.690000       3.600000   \n",
 440 |        "max        49.470000      25.450000     100.000000      14.330000   \n",
 441 |        "\n",
 442 |        "               WD10M  \n",
 443 |        "count  175310.000000  \n",
 444 |        "mean      207.538628  \n",
 445 |        "std       103.475862  \n",
 446 |        "min         0.000000  \n",
 447 |        "25%       107.290000  \n",
 448 |        "50%       246.180000  \n",
 449 |        "75%       296.750000  \n",
 450 |        "max       359.920000  "
 451 |       ]
 452 |      },
 453 |      "execution_count": 39,
 454 |      "metadata": {},
 455 |      "output_type": "execute_result"
 456 |     }
 457 |    ],
 458 |    "source": [
 459 |     "df.describe()\n"
 460 |    ]
 461 |   },
 462 |   {
 463 |    "cell_type": "code",
 464 |    "execution_count": 81,
 465 |    "metadata": {},
 466 |    "outputs": [
 467 |     {
 468 |      "name": "stdout",
 469 |      "output_type": "stream",
 470 |      "text": [
 471 |       "<class 'pandas.core.frame.DataFrame'>\n",
 472 |       "RangeIndex: 175310 entries, 0 to 175309\n",
 473 |       "Data columns (total 9 columns):\n",
 474 |       " #   Column  Non-Null Count   Dtype  \n",
 475 |       "---  ------  --------------   -----  \n",
 476 |       " 0   YEAR    175310 non-null  int64  \n",
 477 |       " 1   MO      175310 non-null  int64  \n",
 478 |       " 2   DY      175310 non-null  int64  \n",
 479 |       " 3   HR      175310 non-null  int64  \n",
 480 |       " 4   T2M     175310 non-null  float64\n",
 481 |       " 5   QV2M    175310 non-null  float64\n",
 482 |       " 6   RH2M    175310 non-null  float64\n",
 483 |       " 7   WS10M   175310 non-null  float64\n",
 484 |       " 8   WD10M   175310 non-null  float64\n",
 485 |       "dtypes: float64(5), int64(4)\n",
 486 |       "memory usage: 12.0 MB\n"
 487 |      ]
 488 |     }
 489 |    ],
 490 |    "source": [
 491 |     "df.info()"
 492 |    ]
 493 |   },
 494 |   {
 495 |    "cell_type": "code",
 496 |    "execution_count": 82,
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "data": {
 501 |       "text/plain": [
 502 |        "YEAR     0\n",
 503 |        "MO       0\n",
 504 |        "DY       0\n",
 505 |        "HR       0\n",
 506 |        "T2M      0\n",
 507 |        "QV2M     0\n",
 508 |        "RH2M     0\n",
 509 |        "WS10M    0\n",
 510 |        "WD10M    0\n",
 511 |        "dtype: int64"
 512 |       ]
 513 |      },
 514 |      "execution_count": 82,
 515 |      "metadata": {},
 516 |      "output_type": "execute_result"
 517 |     }
 518 |    ],
 519 |    "source": [
 520 |     "df.isnull().sum()"
 521 |    ]
 522 |   },
 523 |   {
 524 |    "cell_type": "code",
 525 |    "execution_count": 40,
 526 |    "metadata": {},
 527 |    "outputs": [
 528 |     {
 529 |      "data": {
 530 |       "text/html": [
 531 |        "<div>\n",
 532 |        "<style scoped>\n",
 533 |        "    .dataframe tbody tr th:only-of-type {\n",
 534 |        "        vertical-align: middle;\n",
 535 |        "    }\n",
 536 |        "\n",
 537 |        "    .dataframe tbody tr th {\n",
 538 |        "        vertical-align: top;\n",
 539 |        "    }\n",
 540 |        "\n",
 541 |        "    .dataframe thead th {\n",
 542 |        "        text-align: right;\n",
 543 |        "    }\n",
 544 |        "</style>\n",
 545 |        "<table border=\"1\" class=\"dataframe\">\n",
 546 |        "  <thead>\n",
 547 |        "    <tr style=\"text-align: right;\">\n",
 548 |        "      <th></th>\n",
 549 |        "      <th>YEAR</th>\n",
 550 |        "      <th>MO</th>\n",
 551 |        "      <th>DY</th>\n",
 552 |        "      <th>HR</th>\n",
 553 |        "      <th>T2M</th>\n",
 554 |        "      <th>QV2M</th>\n",
 555 |        "      <th>RH2M</th>\n",
 556 |        "      <th>WS10M</th>\n",
 557 |        "      <th>WD10M</th>\n",
 558 |        "    </tr>\n",
 559 |        "  </thead>\n",
 560 |        "  <tbody>\n",
 561 |        "    <tr>\n",
 562 |        "      <th>117723</th>\n",
 563 |        "      <td>2014</td>\n",
 564 |        "      <td>6</td>\n",
 565 |        "      <td>7</td>\n",
 566 |        "      <td>13</td>\n",
 567 |        "      <td>49.47</td>\n",
 568 |        "      <td>6.29</td>\n",
 569 |        "      <td>8.19</td>\n",
 570 |        "      <td>7.07</td>\n",
 571 |        "      <td>326.31</td>\n",
 572 |        "    </tr>\n",
 573 |        "    <tr>\n",
 574 |        "      <th>117747</th>\n",
 575 |        "      <td>2014</td>\n",
 576 |        "      <td>6</td>\n",
 577 |        "      <td>8</td>\n",
 578 |        "      <td>13</td>\n",
 579 |        "      <td>49.26</td>\n",
 580 |        "      <td>5.92</td>\n",
 581 |        "      <td>7.75</td>\n",
 582 |        "      <td>6.49</td>\n",
 583 |        "      <td>316.71</td>\n",
 584 |        "    </tr>\n",
 585 |        "    <tr>\n",
 586 |        "      <th>117722</th>\n",
 587 |        "      <td>2014</td>\n",
 588 |        "      <td>6</td>\n",
 589 |        "      <td>7</td>\n",
 590 |        "      <td>12</td>\n",
 591 |        "      <td>49.23</td>\n",
 592 |        "      <td>6.23</td>\n",
 593 |        "      <td>8.25</td>\n",
 594 |        "      <td>6.72</td>\n",
 595 |        "      <td>330.63</td>\n",
 596 |        "    </tr>\n",
 597 |        "    <tr>\n",
 598 |        "      <th>117724</th>\n",
 599 |        "      <td>2014</td>\n",
 600 |        "      <td>6</td>\n",
 601 |        "      <td>7</td>\n",
 602 |        "      <td>14</td>\n",
 603 |        "      <td>49.18</td>\n",
 604 |        "      <td>6.23</td>\n",
 605 |        "      <td>8.25</td>\n",
 606 |        "      <td>7.22</td>\n",
 607 |        "      <td>322.65</td>\n",
 608 |        "    </tr>\n",
 609 |        "    <tr>\n",
 610 |        "      <th>117746</th>\n",
 611 |        "      <td>2014</td>\n",
 612 |        "      <td>6</td>\n",
 613 |        "      <td>8</td>\n",
 614 |        "      <td>12</td>\n",
 615 |        "      <td>49.11</td>\n",
 616 |        "      <td>6.10</td>\n",
 617 |        "      <td>8.12</td>\n",
 618 |        "      <td>5.95</td>\n",
 619 |        "      <td>319.37</td>\n",
 620 |        "    </tr>\n",
 621 |        "    <tr>\n",
 622 |        "      <th>...</th>\n",
 623 |        "      <td>...</td>\n",
 624 |        "      <td>...</td>\n",
 625 |        "      <td>...</td>\n",
 626 |        "      <td>...</td>\n",
 627 |        "      <td>...</td>\n",
 628 |        "      <td>...</td>\n",
 629 |        "      <td>...</td>\n",
 630 |        "      <td>...</td>\n",
 631 |        "      <td>...</td>\n",
 632 |        "    </tr>\n",
 633 |        "    <tr>\n",
 634 |        "      <th>166505</th>\n",
 635 |        "      <td>2019</td>\n",
 636 |        "      <td>12</td>\n",
 637 |        "      <td>31</td>\n",
 638 |        "      <td>3</td>\n",
 639 |        "      <td>1.05</td>\n",
 640 |        "      <td>4.39</td>\n",
 641 |        "      <td>100.00</td>\n",
 642 |        "      <td>2.15</td>\n",
 643 |        "      <td>97.31</td>\n",
 644 |        "    </tr>\n",
 645 |        "    <tr>\n",
 646 |        "      <th>166483</th>\n",
 647 |        "      <td>2019</td>\n",
 648 |        "      <td>12</td>\n",
 649 |        "      <td>30</td>\n",
 650 |        "      <td>5</td>\n",
 651 |        "      <td>0.94</td>\n",
 652 |        "      <td>4.76</td>\n",
 653 |        "      <td>100.00</td>\n",
 654 |        "      <td>2.68</td>\n",
 655 |        "      <td>174.81</td>\n",
 656 |        "    </tr>\n",
 657 |        "    <tr>\n",
 658 |        "      <th>166506</th>\n",
 659 |        "      <td>2019</td>\n",
 660 |        "      <td>12</td>\n",
 661 |        "      <td>31</td>\n",
 662 |        "      <td>4</td>\n",
 663 |        "      <td>0.40</td>\n",
 664 |        "      <td>4.39</td>\n",
 665 |        "      <td>100.00</td>\n",
 666 |        "      <td>2.22</td>\n",
 667 |        "      <td>102.81</td>\n",
 668 |        "    </tr>\n",
 669 |        "    <tr>\n",
 670 |        "      <th>166508</th>\n",
 671 |        "      <td>2019</td>\n",
 672 |        "      <td>12</td>\n",
 673 |        "      <td>31</td>\n",
 674 |        "      <td>6</td>\n",
 675 |        "      <td>0.36</td>\n",
 676 |        "      <td>4.33</td>\n",
 677 |        "      <td>100.00</td>\n",
 678 |        "      <td>2.01</td>\n",
 679 |        "      <td>118.56</td>\n",
 680 |        "    </tr>\n",
 681 |        "    <tr>\n",
 682 |        "      <th>166507</th>\n",
 683 |        "      <td>2019</td>\n",
 684 |        "      <td>12</td>\n",
 685 |        "      <td>31</td>\n",
 686 |        "      <td>5</td>\n",
 687 |        "      <td>0.01</td>\n",
 688 |        "      <td>4.39</td>\n",
 689 |        "      <td>100.00</td>\n",
 690 |        "      <td>2.22</td>\n",
 691 |        "      <td>110.41</td>\n",
 692 |        "    </tr>\n",
 693 |        "  </tbody>\n",
 694 |        "</table>\n",
 695 |        "<p>175310 rows × 9 columns</p>\n",
 696 |        "</div>"
 697 |       ],
 698 |       "text/plain": [
 699 |        "        YEAR  MO  DY  HR    T2M  QV2M    RH2M  WS10M   WD10M\n",
 700 |        "117723  2014   6   7  13  49.47  6.29    8.19   7.07  326.31\n",
 701 |        "117747  2014   6   8  13  49.26  5.92    7.75   6.49  316.71\n",
 702 |        "117722  2014   6   7  12  49.23  6.23    8.25   6.72  330.63\n",
 703 |        "117724  2014   6   7  14  49.18  6.23    8.25   7.22  322.65\n",
 704 |        "117746  2014   6   8  12  49.11  6.10    8.12   5.95  319.37\n",
 705 |        "...      ...  ..  ..  ..    ...   ...     ...    ...     ...\n",
 706 |        "166505  2019  12  31   3   1.05  4.39  100.00   2.15   97.31\n",
 707 |        "166483  2019  12  30   5   0.94  4.76  100.00   2.68  174.81\n",
 708 |        "166506  2019  12  31   4   0.40  4.39  100.00   2.22  102.81\n",
 709 |        "166508  2019  12  31   6   0.36  4.33  100.00   2.01  118.56\n",
 710 |        "166507  2019  12  31   5   0.01  4.39  100.00   2.22  110.41\n",
 711 |        "\n",
 712 |        "[175310 rows x 9 columns]"
 713 |       ]
 714 |      },
 715 |      "execution_count": 40,
 716 |      "metadata": {},
 717 |      "output_type": "execute_result"
 718 |     }
 719 |    ],
 720 |    "source": [
 721 |     "df.sort_values('T2M',ascending=False)"
 722 |    ]
 723 |   },
 724 |   {
 725 |    "cell_type": "code",
 726 |    "execution_count": 51,
 727 |    "metadata": {},
 728 |    "outputs": [
 729 |     {
 730 |      "data": {
 731 |       "text/html": [
 732 |        "<div>\n",
 733 |        "<style scoped>\n",
 734 |        "    .dataframe tbody tr th:only-of-type {\n",
 735 |        "        vertical-align: middle;\n",
 736 |        "    }\n",
 737 |        "\n",
 738 |        "    .dataframe tbody tr th {\n",
 739 |        "        vertical-align: top;\n",
 740 |        "    }\n",
 741 |        "\n",
 742 |        "    .dataframe thead th {\n",
 743 |        "        text-align: right;\n",
 744 |        "    }\n",
 745 |        "</style>\n",
 746 |        "<table border=\"1\" class=\"dataframe\">\n",
 747 |        "  <thead>\n",
 748 |        "    <tr style=\"text-align: right;\">\n",
 749 |        "      <th></th>\n",
 750 |        "      <th>YEAR</th>\n",
 751 |        "      <th>MO</th>\n",
 752 |        "      <th>DY</th>\n",
 753 |        "      <th>HR</th>\n",
 754 |        "      <th>T2M</th>\n",
 755 |        "      <th>QV2M</th>\n",
 756 |        "      <th>RH2M</th>\n",
 757 |        "      <th>WS10M</th>\n",
 758 |        "      <th>WD10M</th>\n",
 759 |        "    </tr>\n",
 760 |        "  </thead>\n",
 761 |        "  <tbody>\n",
 762 |        "    <tr>\n",
 763 |        "      <th>117722</th>\n",
 764 |        "      <td>2014</td>\n",
 765 |        "      <td>6</td>\n",
 766 |        "      <td>7</td>\n",
 767 |        "      <td>12</td>\n",
 768 |        "      <td>49.23</td>\n",
 769 |        "      <td>6.23</td>\n",
 770 |        "      <td>8.25</td>\n",
 771 |        "      <td>6.72</td>\n",
 772 |        "      <td>330.63</td>\n",
 773 |        "    </tr>\n",
 774 |        "    <tr>\n",
 775 |        "      <th>117723</th>\n",
 776 |        "      <td>2014</td>\n",
 777 |        "      <td>6</td>\n",
 778 |        "      <td>7</td>\n",
 779 |        "      <td>13</td>\n",
 780 |        "      <td>49.47</td>\n",
 781 |        "      <td>6.29</td>\n",
 782 |        "      <td>8.19</td>\n",
 783 |        "      <td>7.07</td>\n",
 784 |        "      <td>326.31</td>\n",
 785 |        "    </tr>\n",
 786 |        "    <tr>\n",
 787 |        "      <th>117724</th>\n",
 788 |        "      <td>2014</td>\n",
 789 |        "      <td>6</td>\n",
 790 |        "      <td>7</td>\n",
 791 |        "      <td>14</td>\n",
 792 |        "      <td>49.18</td>\n",
 793 |        "      <td>6.23</td>\n",
 794 |        "      <td>8.25</td>\n",
 795 |        "      <td>7.22</td>\n",
 796 |        "      <td>322.65</td>\n",
 797 |        "    </tr>\n",
 798 |        "    <tr>\n",
 799 |        "      <th>117746</th>\n",
 800 |        "      <td>2014</td>\n",
 801 |        "      <td>6</td>\n",
 802 |        "      <td>8</td>\n",
 803 |        "      <td>12</td>\n",
 804 |        "      <td>49.11</td>\n",
 805 |        "      <td>6.10</td>\n",
 806 |        "      <td>8.12</td>\n",
 807 |        "      <td>5.95</td>\n",
 808 |        "      <td>319.37</td>\n",
 809 |        "    </tr>\n",
 810 |        "    <tr>\n",
 811 |        "      <th>117747</th>\n",
 812 |        "      <td>2014</td>\n",
 813 |        "      <td>6</td>\n",
 814 |        "      <td>8</td>\n",
 815 |        "      <td>13</td>\n",
 816 |        "      <td>49.26</td>\n",
 817 |        "      <td>5.92</td>\n",
 818 |        "      <td>7.75</td>\n",
 819 |        "      <td>6.49</td>\n",
 820 |        "      <td>316.71</td>\n",
 821 |        "    </tr>\n",
 822 |        "    <tr>\n",
 823 |        "      <th>117795</th>\n",
 824 |        "      <td>2014</td>\n",
 825 |        "      <td>6</td>\n",
 826 |        "      <td>10</td>\n",
 827 |        "      <td>13</td>\n",
 828 |        "      <td>49.07</td>\n",
 829 |        "      <td>4.09</td>\n",
 830 |        "      <td>5.44</td>\n",
 831 |        "      <td>5.97</td>\n",
 832 |        "      <td>307.93</td>\n",
 833 |        "    </tr>\n",
 834 |        "  </tbody>\n",
 835 |        "</table>\n",
 836 |        "</div>"
 837 |       ],
 838 |       "text/plain": [
 839 |        "        YEAR  MO  DY  HR    T2M  QV2M  RH2M  WS10M   WD10M\n",
 840 |        "117722  2014   6   7  12  49.23  6.23  8.25   6.72  330.63\n",
 841 |        "117723  2014   6   7  13  49.47  6.29  8.19   7.07  326.31\n",
 842 |        "117724  2014   6   7  14  49.18  6.23  8.25   7.22  322.65\n",
 843 |        "117746  2014   6   8  12  49.11  6.10  8.12   5.95  319.37\n",
 844 |        "117747  2014   6   8  13  49.26  5.92  7.75   6.49  316.71\n",
 845 |        "117795  2014   6  10  13  49.07  4.09  5.44   5.97  307.93"
 846 |       ]
 847 |      },
 848 |      "execution_count": 51,
 849 |      "metadata": {},
 850 |      "output_type": "execute_result"
 851 |     }
 852 |    ],
 853 |    "source": [
 854 |     "df[df['T2M'] > 49 ]"
 855 |    ]
 856 |   },
 857 |   {
 858 |    "cell_type": "code",
 859 |    "execution_count": 68,
 860 |    "metadata": {},
 861 |    "outputs": [
 862 |     {
 863 |      "data": {
 864 |       "text/html": [
 865 |        "<div>\n",
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 867 |        "    .dataframe tbody tr th:only-of-type {\n",
 868 |        "        vertical-align: middle;\n",
 869 |        "    }\n",
 870 |        "\n",
 871 |        "    .dataframe tbody tr th {\n",
 872 |        "        vertical-align: top;\n",
 873 |        "    }\n",
 874 |        "\n",
 875 |        "    .dataframe thead th {\n",
 876 |        "        text-align: right;\n",
 877 |        "    }\n",
 878 |        "</style>\n",
 879 |        "<table border=\"1\" class=\"dataframe\">\n",
 880 |        "  <thead>\n",
 881 |        "    <tr style=\"text-align: right;\">\n",
 882 |        "      <th></th>\n",
 883 |        "      <th>YEAR</th>\n",
 884 |        "      <th>MO</th>\n",
 885 |        "      <th>DY</th>\n",
 886 |        "      <th>HR</th>\n",
 887 |        "      <th>T2M</th>\n",
 888 |        "      <th>QV2M</th>\n",
 889 |        "      <th>RH2M</th>\n",
 890 |        "      <th>WS10M</th>\n",
 891 |        "      <th>WD10M</th>\n",
 892 |        "    </tr>\n",
 893 |        "  </thead>\n",
 894 |        "  <tbody>\n",
 895 |        "    <tr>\n",
 896 |        "      <th>157766</th>\n",
 897 |        "      <td>2019</td>\n",
 898 |        "      <td>1</td>\n",
 899 |        "      <td>1</td>\n",
 900 |        "      <td>0</td>\n",
 901 |        "      <td>10.40</td>\n",
 902 |        "      <td>3.66</td>\n",
 903 |        "      <td>46.62</td>\n",
 904 |        "      <td>1.87</td>\n",
 905 |        "      <td>277.21</td>\n",
 906 |        "    </tr>\n",
 907 |        "    <tr>\n",
 908 |        "      <th>157767</th>\n",
 909 |        "      <td>2019</td>\n",
 910 |        "      <td>1</td>\n",
 911 |        "      <td>1</td>\n",
 912 |        "      <td>1</td>\n",
 913 |        "      <td>9.12</td>\n",
 914 |        "      <td>3.66</td>\n",
 915 |        "      <td>50.81</td>\n",
 916 |        "      <td>2.24</td>\n",
 917 |        "      <td>273.00</td>\n",
 918 |        "    </tr>\n",
 919 |        "    <tr>\n",
 920 |        "      <th>157768</th>\n",
 921 |        "      <td>2019</td>\n",
 922 |        "      <td>1</td>\n",
 923 |        "      <td>1</td>\n",
 924 |        "      <td>2</td>\n",
 925 |        "      <td>7.63</td>\n",
 926 |        "      <td>3.66</td>\n",
 927 |        "      <td>56.62</td>\n",
 928 |        "      <td>2.63</td>\n",
 929 |        "      <td>274.61</td>\n",
 930 |        "    </tr>\n",
 931 |        "    <tr>\n",
 932 |        "      <th>157769</th>\n",
 933 |        "      <td>2019</td>\n",
 934 |        "      <td>1</td>\n",
 935 |        "      <td>1</td>\n",
 936 |        "      <td>3</td>\n",
 937 |        "      <td>6.58</td>\n",
 938 |        "      <td>3.66</td>\n",
 939 |        "      <td>60.88</td>\n",
 940 |        "      <td>2.82</td>\n",
 941 |        "      <td>277.79</td>\n",
 942 |        "    </tr>\n",
 943 |        "    <tr>\n",
 944 |        "      <th>157770</th>\n",
 945 |        "      <td>2019</td>\n",
 946 |        "      <td>1</td>\n",
 947 |        "      <td>1</td>\n",
 948 |        "      <td>4</td>\n",
 949 |        "      <td>5.90</td>\n",
 950 |        "      <td>3.66</td>\n",
 951 |        "      <td>63.56</td>\n",
 952 |        "      <td>2.88</td>\n",
 953 |        "      <td>278.75</td>\n",
 954 |        "    </tr>\n",
 955 |        "    <tr>\n",
 956 |        "      <th>...</th>\n",
 957 |        "      <td>...</td>\n",
 958 |        "      <td>...</td>\n",
 959 |        "      <td>...</td>\n",
 960 |        "      <td>...</td>\n",
 961 |        "      <td>...</td>\n",
 962 |        "      <td>...</td>\n",
 963 |        "      <td>...</td>\n",
 964 |        "      <td>...</td>\n",
 965 |        "      <td>...</td>\n",
 966 |        "    </tr>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>165801</th>\n",
 969 |        "      <td>2019</td>\n",
 970 |        "      <td>12</td>\n",
 971 |        "      <td>1</td>\n",
 972 |        "      <td>19</td>\n",
 973 |        "      <td>18.04</td>\n",
 974 |        "      <td>10.07</td>\n",
 975 |        "      <td>77.88</td>\n",
 976 |        "      <td>2.11</td>\n",
 977 |        "      <td>290.58</td>\n",
 978 |        "    </tr>\n",
 979 |        "    <tr>\n",
 980 |        "      <th>165802</th>\n",
 981 |        "      <td>2019</td>\n",
 982 |        "      <td>12</td>\n",
 983 |        "      <td>1</td>\n",
 984 |        "      <td>20</td>\n",
 985 |        "      <td>17.20</td>\n",
 986 |        "      <td>9.89</td>\n",
 987 |        "      <td>80.69</td>\n",
 988 |        "      <td>2.32</td>\n",
 989 |        "      <td>298.81</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>165803</th>\n",
 993 |        "      <td>2019</td>\n",
 994 |        "      <td>12</td>\n",
 995 |        "      <td>1</td>\n",
 996 |        "      <td>21</td>\n",
 997 |        "      <td>16.51</td>\n",
 998 |        "      <td>9.70</td>\n",
 999 |        "      <td>82.75</td>\n",
1000 |        "      <td>2.47</td>\n",
1001 |        "      <td>305.20</td>\n",
1002 |        "    </tr>\n",
1003 |        "    <tr>\n",
1004 |        "      <th>165804</th>\n",
1005 |        "      <td>2019</td>\n",
1006 |        "      <td>12</td>\n",
1007 |        "      <td>1</td>\n",
1008 |        "      <td>22</td>\n",
1009 |        "      <td>15.94</td>\n",
1010 |        "      <td>9.46</td>\n",
1011 |        "      <td>83.62</td>\n",
1012 |        "      <td>2.50</td>\n",
1013 |        "      <td>310.56</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>165805</th>\n",
1017 |        "      <td>2019</td>\n",
1018 |        "      <td>12</td>\n",
1019 |        "      <td>1</td>\n",
1020 |        "      <td>23</td>\n",
1021 |        "      <td>15.42</td>\n",
1022 |        "      <td>9.22</td>\n",
1023 |        "      <td>84.19</td>\n",
1024 |        "      <td>2.46</td>\n",
1025 |        "      <td>315.51</td>\n",
1026 |        "    </tr>\n",
1027 |        "  </tbody>\n",
1028 |        "</table>\n",
1029 |        "<p>288 rows × 9 columns</p>\n",
1030 |        "</div>"
1031 |       ],
1032 |       "text/plain": [
1033 |        "        YEAR  MO  DY  HR    T2M   QV2M   RH2M  WS10M   WD10M\n",
1034 |        "157766  2019   1   1   0  10.40   3.66  46.62   1.87  277.21\n",
1035 |        "157767  2019   1   1   1   9.12   3.66  50.81   2.24  273.00\n",
1036 |        "157768  2019   1   1   2   7.63   3.66  56.62   2.63  274.61\n",
1037 |        "157769  2019   1   1   3   6.58   3.66  60.88   2.82  277.79\n",
1038 |        "157770  2019   1   1   4   5.90   3.66  63.56   2.88  278.75\n",
1039 |        "...      ...  ..  ..  ..    ...    ...    ...    ...     ...\n",
1040 |        "165801  2019  12   1  19  18.04  10.07  77.88   2.11  290.58\n",
1041 |        "165802  2019  12   1  20  17.20   9.89  80.69   2.32  298.81\n",
1042 |        "165803  2019  12   1  21  16.51   9.70  82.75   2.47  305.20\n",
1043 |        "165804  2019  12   1  22  15.94   9.46  83.62   2.50  310.56\n",
1044 |        "165805  2019  12   1  23  15.42   9.22  84.19   2.46  315.51\n",
1045 |        "\n",
1046 |        "[288 rows x 9 columns]"
1047 |       ]
1048 |      },
1049 |      "execution_count": 68,
1050 |      "metadata": {},
1051 |      "output_type": "execute_result"
1052 |     }
1053 |    ],
1054 |    "source": [
1055 |     "df[(df['YEAR'] == 2019) & (df['DY'] == 1) ]\n",
1056 |     "# 2019 only 1st day of the month "
1057 |    ]
1058 |   },
1059 |   {
1060 |    "cell_type": "code",
1061 |    "execution_count": 76,
1062 |    "metadata": {},
1063 |    "outputs": [],
1064 |    "source": [
1065 |     "tempdf = df[(df['YEAR'] == 2019) & (df['MO'] == 12) & (df['HR'] == 23)]"
1066 |    ]
1067 |   },
1068 |   {
1069 |    "cell_type": "code",
1070 |    "execution_count": 77,
1071 |    "metadata": {},
1072 |    "outputs": [
1073 |     {
1074 |      "data": {
1075 |       "text/html": [
1076 |        "<div>\n",
1077 |        "<style scoped>\n",
1078 |        "    .dataframe tbody tr th:only-of-type {\n",
1079 |        "        vertical-align: middle;\n",
1080 |        "    }\n",
1081 |        "\n",
1082 |        "    .dataframe tbody tr th {\n",
1083 |        "        vertical-align: top;\n",
1084 |        "    }\n",
1085 |        "\n",
1086 |        "    .dataframe thead th {\n",
1087 |        "        text-align: right;\n",
1088 |        "    }\n",
1089 |        "</style>\n",
1090 |        "<table border=\"1\" class=\"dataframe\">\n",
1091 |        "  <thead>\n",
1092 |        "    <tr style=\"text-align: right;\">\n",
1093 |        "      <th></th>\n",
1094 |        "      <th>YEAR</th>\n",
1095 |        "      <th>MO</th>\n",
1096 |        "      <th>DY</th>\n",
1097 |        "      <th>HR</th>\n",
1098 |        "      <th>T2M</th>\n",
1099 |        "      <th>QV2M</th>\n",
1100 |        "      <th>RH2M</th>\n",
1101 |        "      <th>WS10M</th>\n",
1102 |        "      <th>WD10M</th>\n",
1103 |        "    </tr>\n",
1104 |        "  </thead>\n",
1105 |        "  <tbody>\n",
1106 |        "    <tr>\n",
1107 |        "      <th>165805</th>\n",
1108 |        "      <td>2019</td>\n",
1109 |        "      <td>12</td>\n",
1110 |        "      <td>1</td>\n",
1111 |        "      <td>23</td>\n",
1112 |        "      <td>15.42</td>\n",
1113 |        "      <td>9.22</td>\n",
1114 |        "      <td>84.19</td>\n",
1115 |        "      <td>2.46</td>\n",
1116 |        "      <td>315.51</td>\n",
1117 |        "    </tr>\n",
1118 |        "    <tr>\n",
1119 |        "      <th>165829</th>\n",
1120 |        "      <td>2019</td>\n",
1121 |        "      <td>12</td>\n",
1122 |        "      <td>2</td>\n",
1123 |        "      <td>23</td>\n",
1124 |        "      <td>12.74</td>\n",
1125 |        "      <td>7.14</td>\n",
1126 |        "      <td>77.56</td>\n",
1127 |        "      <td>2.69</td>\n",
1128 |        "      <td>285.01</td>\n",
1129 |        "    </tr>\n",
1130 |        "    <tr>\n",
1131 |        "      <th>165853</th>\n",
1132 |        "      <td>2019</td>\n",
1133 |        "      <td>12</td>\n",
1134 |        "      <td>3</td>\n",
1135 |        "      <td>23</td>\n",
1136 |        "      <td>10.99</td>\n",
1137 |        "      <td>6.23</td>\n",
1138 |        "      <td>76.12</td>\n",
1139 |        "      <td>3.06</td>\n",
1140 |        "      <td>246.51</td>\n",
1141 |        "    </tr>\n",
1142 |        "    <tr>\n",
1143 |        "      <th>165877</th>\n",
1144 |        "      <td>2019</td>\n",
1145 |        "      <td>12</td>\n",
1146 |        "      <td>4</td>\n",
1147 |        "      <td>23</td>\n",
1148 |        "      <td>13.18</td>\n",
1149 |        "      <td>6.53</td>\n",
1150 |        "      <td>69.19</td>\n",
1151 |        "      <td>1.71</td>\n",
1152 |        "      <td>187.35</td>\n",
1153 |        "    </tr>\n",
1154 |        "    <tr>\n",
1155 |        "      <th>165901</th>\n",
1156 |        "      <td>2019</td>\n",
1157 |        "      <td>12</td>\n",
1158 |        "      <td>5</td>\n",
1159 |        "      <td>23</td>\n",
1160 |        "      <td>13.86</td>\n",
1161 |        "      <td>7.39</td>\n",
1162 |        "      <td>74.88</td>\n",
1163 |        "      <td>0.99</td>\n",
1164 |        "      <td>166.84</td>\n",
1165 |        "    </tr>\n",
1166 |        "    <tr>\n",
1167 |        "      <th>165925</th>\n",
1168 |        "      <td>2019</td>\n",
1169 |        "      <td>12</td>\n",
1170 |        "      <td>6</td>\n",
1171 |        "      <td>23</td>\n",
1172 |        "      <td>13.95</td>\n",
1173 |        "      <td>8.06</td>\n",
1174 |        "      <td>81.44</td>\n",
1175 |        "      <td>1.25</td>\n",
1176 |        "      <td>170.66</td>\n",
1177 |        "    </tr>\n",
1178 |        "    <tr>\n",
1179 |        "      <th>165949</th>\n",
1180 |        "      <td>2019</td>\n",
1181 |        "      <td>12</td>\n",
1182 |        "      <td>7</td>\n",
1183 |        "      <td>23</td>\n",
1184 |        "      <td>14.61</td>\n",
1185 |        "      <td>8.48</td>\n",
1186 |        "      <td>82.19</td>\n",
1187 |        "      <td>1.24</td>\n",
1188 |        "      <td>243.92</td>\n",
1189 |        "    </tr>\n",
1190 |        "    <tr>\n",
1191 |        "      <th>165973</th>\n",
1192 |        "      <td>2019</td>\n",
1193 |        "      <td>12</td>\n",
1194 |        "      <td>8</td>\n",
1195 |        "      <td>23</td>\n",
1196 |        "      <td>14.39</td>\n",
1197 |        "      <td>9.09</td>\n",
1198 |        "      <td>89.38</td>\n",
1199 |        "      <td>1.57</td>\n",
1200 |        "      <td>220.17</td>\n",
1201 |        "    </tr>\n",
1202 |        "    <tr>\n",
1203 |        "      <th>165997</th>\n",
1204 |        "      <td>2019</td>\n",
1205 |        "      <td>12</td>\n",
1206 |        "      <td>9</td>\n",
1207 |        "      <td>23</td>\n",
1208 |        "      <td>14.19</td>\n",
1209 |        "      <td>8.00</td>\n",
1210 |        "      <td>79.44</td>\n",
1211 |        "      <td>1.50</td>\n",
1212 |        "      <td>201.64</td>\n",
1213 |        "    </tr>\n",
1214 |        "    <tr>\n",
1215 |        "      <th>166021</th>\n",
1216 |        "      <td>2019</td>\n",
1217 |        "      <td>12</td>\n",
1218 |        "      <td>10</td>\n",
1219 |        "      <td>23</td>\n",
1220 |        "      <td>13.12</td>\n",
1221 |        "      <td>7.63</td>\n",
1222 |        "      <td>80.75</td>\n",
1223 |        "      <td>2.03</td>\n",
1224 |        "      <td>220.31</td>\n",
1225 |        "    </tr>\n",
1226 |        "    <tr>\n",
1227 |        "      <th>166045</th>\n",
1228 |        "      <td>2019</td>\n",
1229 |        "      <td>12</td>\n",
1230 |        "      <td>11</td>\n",
1231 |        "      <td>23</td>\n",
1232 |        "      <td>14.93</td>\n",
1233 |        "      <td>8.00</td>\n",
1234 |        "      <td>75.38</td>\n",
1235 |        "      <td>1.24</td>\n",
1236 |        "      <td>139.87</td>\n",
1237 |        "    </tr>\n",
1238 |        "    <tr>\n",
1239 |        "      <th>166069</th>\n",
1240 |        "      <td>2019</td>\n",
1241 |        "      <td>12</td>\n",
1242 |        "      <td>12</td>\n",
1243 |        "      <td>23</td>\n",
1244 |        "      <td>15.83</td>\n",
1245 |        "      <td>10.56</td>\n",
1246 |        "      <td>93.38</td>\n",
1247 |        "      <td>6.88</td>\n",
1248 |        "      <td>128.04</td>\n",
1249 |        "    </tr>\n",
1250 |        "    <tr>\n",
1251 |        "      <th>166093</th>\n",
1252 |        "      <td>2019</td>\n",
1253 |        "      <td>12</td>\n",
1254 |        "      <td>13</td>\n",
1255 |        "      <td>23</td>\n",
1256 |        "      <td>15.16</td>\n",
1257 |        "      <td>11.17</td>\n",
1258 |        "      <td>100.00</td>\n",
1259 |        "      <td>2.51</td>\n",
1260 |        "      <td>138.41</td>\n",
1261 |        "    </tr>\n",
1262 |        "    <tr>\n",
1263 |        "      <th>166117</th>\n",
1264 |        "      <td>2019</td>\n",
1265 |        "      <td>12</td>\n",
1266 |        "      <td>14</td>\n",
1267 |        "      <td>23</td>\n",
1268 |        "      <td>15.06</td>\n",
1269 |        "      <td>9.58</td>\n",
1270 |        "      <td>90.12</td>\n",
1271 |        "      <td>1.96</td>\n",
1272 |        "      <td>69.18</td>\n",
1273 |        "    </tr>\n",
1274 |        "    <tr>\n",
1275 |        "      <th>166141</th>\n",
1276 |        "      <td>2019</td>\n",
1277 |        "      <td>12</td>\n",
1278 |        "      <td>15</td>\n",
1279 |        "      <td>23</td>\n",
1280 |        "      <td>12.55</td>\n",
1281 |        "      <td>8.42</td>\n",
1282 |        "      <td>93.31</td>\n",
1283 |        "      <td>2.11</td>\n",
1284 |        "      <td>279.60</td>\n",
1285 |        "    </tr>\n",
1286 |        "    <tr>\n",
1287 |        "      <th>166165</th>\n",
1288 |        "      <td>2019</td>\n",
1289 |        "      <td>12</td>\n",
1290 |        "      <td>16</td>\n",
1291 |        "      <td>23</td>\n",
1292 |        "      <td>9.40</td>\n",
1293 |        "      <td>6.35</td>\n",
1294 |        "      <td>86.50</td>\n",
1295 |        "      <td>3.08</td>\n",
1296 |        "      <td>291.24</td>\n",
1297 |        "    </tr>\n",
1298 |        "    <tr>\n",
1299 |        "      <th>166189</th>\n",
1300 |        "      <td>2019</td>\n",
1301 |        "      <td>12</td>\n",
1302 |        "      <td>17</td>\n",
1303 |        "      <td>23</td>\n",
1304 |        "      <td>8.57</td>\n",
1305 |        "      <td>6.16</td>\n",
1306 |        "      <td>88.94</td>\n",
1307 |        "      <td>3.03</td>\n",
1308 |        "      <td>292.46</td>\n",
1309 |        "    </tr>\n",
1310 |        "    <tr>\n",
1311 |        "      <th>166213</th>\n",
1312 |        "      <td>2019</td>\n",
1313 |        "      <td>12</td>\n",
1314 |        "      <td>18</td>\n",
1315 |        "      <td>23</td>\n",
1316 |        "      <td>9.61</td>\n",
1317 |        "      <td>5.68</td>\n",
1318 |        "      <td>76.62</td>\n",
1319 |        "      <td>1.34</td>\n",
1320 |        "      <td>227.84</td>\n",
1321 |        "    </tr>\n",
1322 |        "    <tr>\n",
1323 |        "      <th>166237</th>\n",
1324 |        "      <td>2019</td>\n",
1325 |        "      <td>12</td>\n",
1326 |        "      <td>19</td>\n",
1327 |        "      <td>23</td>\n",
1328 |        "      <td>8.79</td>\n",
1329 |        "      <td>5.49</td>\n",
1330 |        "      <td>78.25</td>\n",
1331 |        "      <td>2.25</td>\n",
1332 |        "      <td>236.92</td>\n",
1333 |        "    </tr>\n",
1334 |        "    <tr>\n",
1335 |        "      <th>166261</th>\n",
1336 |        "      <td>2019</td>\n",
1337 |        "      <td>12</td>\n",
1338 |        "      <td>20</td>\n",
1339 |        "      <td>23</td>\n",
1340 |        "      <td>10.94</td>\n",
1341 |        "      <td>5.62</td>\n",
1342 |        "      <td>69.19</td>\n",
1343 |        "      <td>1.42</td>\n",
1344 |        "      <td>265.58</td>\n",
1345 |        "    </tr>\n",
1346 |        "    <tr>\n",
1347 |        "      <th>166285</th>\n",
1348 |        "      <td>2019</td>\n",
1349 |        "      <td>12</td>\n",
1350 |        "      <td>21</td>\n",
1351 |        "      <td>23</td>\n",
1352 |        "      <td>13.17</td>\n",
1353 |        "      <td>7.75</td>\n",
1354 |        "      <td>81.75</td>\n",
1355 |        "      <td>2.94</td>\n",
1356 |        "      <td>310.37</td>\n",
1357 |        "    </tr>\n",
1358 |        "    <tr>\n",
1359 |        "      <th>166309</th>\n",
1360 |        "      <td>2019</td>\n",
1361 |        "      <td>12</td>\n",
1362 |        "      <td>22</td>\n",
1363 |        "      <td>23</td>\n",
1364 |        "      <td>9.63</td>\n",
1365 |        "      <td>5.98</td>\n",
1366 |        "      <td>79.88</td>\n",
1367 |        "      <td>3.22</td>\n",
1368 |        "      <td>312.74</td>\n",
1369 |        "    </tr>\n",
1370 |        "    <tr>\n",
1371 |        "      <th>166333</th>\n",
1372 |        "      <td>2019</td>\n",
1373 |        "      <td>12</td>\n",
1374 |        "      <td>23</td>\n",
1375 |        "      <td>23</td>\n",
1376 |        "      <td>10.87</td>\n",
1377 |        "      <td>5.31</td>\n",
1378 |        "      <td>65.88</td>\n",
1379 |        "      <td>1.95</td>\n",
1380 |        "      <td>140.37</td>\n",
1381 |        "    </tr>\n",
1382 |        "    <tr>\n",
1383 |        "      <th>166357</th>\n",
1384 |        "      <td>2019</td>\n",
1385 |        "      <td>12</td>\n",
1386 |        "      <td>24</td>\n",
1387 |        "      <td>23</td>\n",
1388 |        "      <td>9.28</td>\n",
1389 |        "      <td>5.86</td>\n",
1390 |        "      <td>80.62</td>\n",
1391 |        "      <td>2.39</td>\n",
1392 |        "      <td>315.27</td>\n",
1393 |        "    </tr>\n",
1394 |        "    <tr>\n",
1395 |        "      <th>166381</th>\n",
1396 |        "      <td>2019</td>\n",
1397 |        "      <td>12</td>\n",
1398 |        "      <td>25</td>\n",
1399 |        "      <td>23</td>\n",
1400 |        "      <td>10.56</td>\n",
1401 |        "      <td>4.64</td>\n",
1402 |        "      <td>58.44</td>\n",
1403 |        "      <td>1.72</td>\n",
1404 |        "      <td>266.10</td>\n",
1405 |        "    </tr>\n",
1406 |        "    <tr>\n",
1407 |        "      <th>166405</th>\n",
1408 |        "      <td>2019</td>\n",
1409 |        "      <td>12</td>\n",
1410 |        "      <td>26</td>\n",
1411 |        "      <td>23</td>\n",
1412 |        "      <td>7.34</td>\n",
1413 |        "      <td>4.15</td>\n",
1414 |        "      <td>65.44</td>\n",
1415 |        "      <td>2.20</td>\n",
1416 |        "      <td>295.84</td>\n",
1417 |        "    </tr>\n",
1418 |        "    <tr>\n",
1419 |        "      <th>166429</th>\n",
1420 |        "      <td>2019</td>\n",
1421 |        "      <td>12</td>\n",
1422 |        "      <td>27</td>\n",
1423 |        "      <td>23</td>\n",
1424 |        "      <td>6.79</td>\n",
1425 |        "      <td>4.27</td>\n",
1426 |        "      <td>69.62</td>\n",
1427 |        "      <td>1.84</td>\n",
1428 |        "      <td>252.41</td>\n",
1429 |        "    </tr>\n",
1430 |        "    <tr>\n",
1431 |        "      <th>166453</th>\n",
1432 |        "      <td>2019</td>\n",
1433 |        "      <td>12</td>\n",
1434 |        "      <td>28</td>\n",
1435 |        "      <td>23</td>\n",
1436 |        "      <td>6.52</td>\n",
1437 |        "      <td>4.76</td>\n",
1438 |        "      <td>78.75</td>\n",
1439 |        "      <td>1.22</td>\n",
1440 |        "      <td>146.51</td>\n",
1441 |        "    </tr>\n",
1442 |        "    <tr>\n",
1443 |        "      <th>166477</th>\n",
1444 |        "      <td>2019</td>\n",
1445 |        "      <td>12</td>\n",
1446 |        "      <td>29</td>\n",
1447 |        "      <td>23</td>\n",
1448 |        "      <td>6.23</td>\n",
1449 |        "      <td>4.76</td>\n",
1450 |        "      <td>80.62</td>\n",
1451 |        "      <td>1.14</td>\n",
1452 |        "      <td>223.33</td>\n",
1453 |        "    </tr>\n",
1454 |        "    <tr>\n",
1455 |        "      <th>166501</th>\n",
1456 |        "      <td>2019</td>\n",
1457 |        "      <td>12</td>\n",
1458 |        "      <td>30</td>\n",
1459 |        "      <td>23</td>\n",
1460 |        "      <td>4.05</td>\n",
1461 |        "      <td>4.52</td>\n",
1462 |        "      <td>88.81</td>\n",
1463 |        "      <td>1.60</td>\n",
1464 |        "      <td>111.80</td>\n",
1465 |        "    </tr>\n",
1466 |        "    <tr>\n",
1467 |        "      <th>166525</th>\n",
1468 |        "      <td>2019</td>\n",
1469 |        "      <td>12</td>\n",
1470 |        "      <td>31</td>\n",
1471 |        "      <td>23</td>\n",
1472 |        "      <td>6.63</td>\n",
1473 |        "      <td>4.70</td>\n",
1474 |        "      <td>77.69</td>\n",
1475 |        "      <td>2.10</td>\n",
1476 |        "      <td>50.44</td>\n",
1477 |        "    </tr>\n",
1478 |        "  </tbody>\n",
1479 |        "</table>\n",
1480 |        "</div>"
1481 |       ],
1482 |       "text/plain": [
1483 |        "        YEAR  MO  DY  HR    T2M   QV2M    RH2M  WS10M   WD10M\n",
1484 |        "165805  2019  12   1  23  15.42   9.22   84.19   2.46  315.51\n",
1485 |        "165829  2019  12   2  23  12.74   7.14   77.56   2.69  285.01\n",
1486 |        "165853  2019  12   3  23  10.99   6.23   76.12   3.06  246.51\n",
1487 |        "165877  2019  12   4  23  13.18   6.53   69.19   1.71  187.35\n",
1488 |        "165901  2019  12   5  23  13.86   7.39   74.88   0.99  166.84\n",
1489 |        "165925  2019  12   6  23  13.95   8.06   81.44   1.25  170.66\n",
1490 |        "165949  2019  12   7  23  14.61   8.48   82.19   1.24  243.92\n",
1491 |        "165973  2019  12   8  23  14.39   9.09   89.38   1.57  220.17\n",
1492 |        "165997  2019  12   9  23  14.19   8.00   79.44   1.50  201.64\n",
1493 |        "166021  2019  12  10  23  13.12   7.63   80.75   2.03  220.31\n",
1494 |        "166045  2019  12  11  23  14.93   8.00   75.38   1.24  139.87\n",
1495 |        "166069  2019  12  12  23  15.83  10.56   93.38   6.88  128.04\n",
1496 |        "166093  2019  12  13  23  15.16  11.17  100.00   2.51  138.41\n",
1497 |        "166117  2019  12  14  23  15.06   9.58   90.12   1.96   69.18\n",
1498 |        "166141  2019  12  15  23  12.55   8.42   93.31   2.11  279.60\n",
1499 |        "166165  2019  12  16  23   9.40   6.35   86.50   3.08  291.24\n",
1500 |        "166189  2019  12  17  23   8.57   6.16   88.94   3.03  292.46\n",
1501 |        "166213  2019  12  18  23   9.61   5.68   76.62   1.34  227.84\n",
1502 |        "166237  2019  12  19  23   8.79   5.49   78.25   2.25  236.92\n",
1503 |        "166261  2019  12  20  23  10.94   5.62   69.19   1.42  265.58\n",
1504 |        "166285  2019  12  21  23  13.17   7.75   81.75   2.94  310.37\n",
1505 |        "166309  2019  12  22  23   9.63   5.98   79.88   3.22  312.74\n",
1506 |        "166333  2019  12  23  23  10.87   5.31   65.88   1.95  140.37\n",
1507 |        "166357  2019  12  24  23   9.28   5.86   80.62   2.39  315.27\n",
1508 |        "166381  2019  12  25  23  10.56   4.64   58.44   1.72  266.10\n",
1509 |        "166405  2019  12  26  23   7.34   4.15   65.44   2.20  295.84\n",
1510 |        "166429  2019  12  27  23   6.79   4.27   69.62   1.84  252.41\n",
1511 |        "166453  2019  12  28  23   6.52   4.76   78.75   1.22  146.51\n",
1512 |        "166477  2019  12  29  23   6.23   4.76   80.62   1.14  223.33\n",
1513 |        "166501  2019  12  30  23   4.05   4.52   88.81   1.60  111.80\n",
1514 |        "166525  2019  12  31  23   6.63   4.70   77.69   2.10   50.44"
1515 |       ]
1516 |      },
1517 |      "execution_count": 77,
1518 |      "metadata": {},
1519 |      "output_type": "execute_result"
1520 |     }
1521 |    ],
1522 |    "source": [
1523 |     "tempdf\n"
1524 |    ]
1525 |   },
1526 |   {
1527 |    "cell_type": "code",
1528 |    "execution_count": 78,
1529 |    "metadata": {},
1530 |    "outputs": [
1531 |     {
1532 |      "data": {
1533 |       "text/plain": [
1534 |        "count    31.000000\n",
1535 |        "mean     11.237419\n",
1536 |        "std       3.281225\n",
1537 |        "min       4.050000\n",
1538 |        "25%       9.035000\n",
1539 |        "50%      10.990000\n",
1540 |        "75%      14.070000\n",
1541 |        "max      15.830000\n",
1542 |        "Name: T2M, dtype: float64"
1543 |       ]
1544 |      },
1545 |      "execution_count": 78,
1546 |      "metadata": {},
1547 |      "output_type": "execute_result"
1548 |     }
1549 |    ],
1550 |    "source": [
1551 |     "tempdf['T2M'].describe()"
1552 |    ]
1553 |   },
1554 |   {
1555 |    "cell_type": "code",
1556 |    "execution_count": 79,
1557 |    "metadata": {},
1558 |    "outputs": [
1559 |     {
1560 |      "data": {
1561 |       "text/html": [
1562 |        "<div>\n",
1563 |        "<style scoped>\n",
1564 |        "    .dataframe tbody tr th:only-of-type {\n",
1565 |        "        vertical-align: middle;\n",
1566 |        "    }\n",
1567 |        "\n",
1568 |        "    .dataframe tbody tr th {\n",
1569 |        "        vertical-align: top;\n",
1570 |        "    }\n",
1571 |        "\n",
1572 |        "    .dataframe thead th {\n",
1573 |        "        text-align: right;\n",
1574 |        "    }\n",
1575 |        "</style>\n",
1576 |        "<table border=\"1\" class=\"dataframe\">\n",
1577 |        "  <thead>\n",
1578 |        "    <tr style=\"text-align: right;\">\n",
1579 |        "      <th></th>\n",
1580 |        "      <th>YEAR</th>\n",
1581 |        "      <th>MO</th>\n",
1582 |        "      <th>DY</th>\n",
1583 |        "      <th>HR</th>\n",
1584 |        "      <th>T2M</th>\n",
1585 |        "      <th>QV2M</th>\n",
1586 |        "      <th>RH2M</th>\n",
1587 |        "      <th>WS10M</th>\n",
1588 |        "      <th>WD10M</th>\n",
1589 |        "    </tr>\n",
1590 |        "  </thead>\n",
1591 |        "  <tbody>\n",
1592 |        "    <tr>\n",
1593 |        "      <th>count</th>\n",
1594 |        "      <td>31.0</td>\n",
1595 |        "      <td>31.0</td>\n",
1596 |        "      <td>31.000000</td>\n",
1597 |        "      <td>31.0</td>\n",
1598 |        "      <td>31.000000</td>\n",
1599 |        "      <td>31.000000</td>\n",
1600 |        "      <td>31.000000</td>\n",
1601 |        "      <td>31.000000</td>\n",
1602 |        "      <td>31.000000</td>\n",
1603 |        "    </tr>\n",
1604 |        "    <tr>\n",
1605 |        "      <th>mean</th>\n",
1606 |        "      <td>2019.0</td>\n",
1607 |        "      <td>12.0</td>\n",
1608 |        "      <td>16.000000</td>\n",
1609 |        "      <td>23.0</td>\n",
1610 |        "      <td>11.237419</td>\n",
1611 |        "      <td>6.822581</td>\n",
1612 |        "      <td>79.817097</td>\n",
1613 |        "      <td>2.149677</td>\n",
1614 |        "      <td>217.814194</td>\n",
1615 |        "    </tr>\n",
1616 |        "    <tr>\n",
1617 |        "      <th>std</th>\n",
1618 |        "      <td>0.0</td>\n",
1619 |        "      <td>0.0</td>\n",
1620 |        "      <td>9.092121</td>\n",
1621 |        "      <td>0.0</td>\n",
1622 |        "      <td>3.281225</td>\n",
1623 |        "      <td>1.907761</td>\n",
1624 |        "      <td>9.095952</td>\n",
1625 |        "      <td>1.091071</td>\n",
1626 |        "      <td>74.989962</td>\n",
1627 |        "    </tr>\n",
1628 |        "    <tr>\n",
1629 |        "      <th>min</th>\n",
1630 |        "      <td>2019.0</td>\n",
1631 |        "      <td>12.0</td>\n",
1632 |        "      <td>1.000000</td>\n",
1633 |        "      <td>23.0</td>\n",
1634 |        "      <td>4.050000</td>\n",
1635 |        "      <td>4.150000</td>\n",
1636 |        "      <td>58.440000</td>\n",
1637 |        "      <td>0.990000</td>\n",
1638 |        "      <td>50.440000</td>\n",
1639 |        "    </tr>\n",
1640 |        "    <tr>\n",
1641 |        "      <th>25%</th>\n",
1642 |        "      <td>2019.0</td>\n",
1643 |        "      <td>12.0</td>\n",
1644 |        "      <td>8.500000</td>\n",
1645 |        "      <td>23.0</td>\n",
1646 |        "      <td>9.035000</td>\n",
1647 |        "      <td>5.400000</td>\n",
1648 |        "      <td>75.750000</td>\n",
1649 |        "      <td>1.460000</td>\n",
1650 |        "      <td>156.675000</td>\n",
1651 |        "    </tr>\n",
1652 |        "    <tr>\n",
1653 |        "      <th>50%</th>\n",
1654 |        "      <td>2019.0</td>\n",
1655 |        "      <td>12.0</td>\n",
1656 |        "      <td>16.000000</td>\n",
1657 |        "      <td>23.0</td>\n",
1658 |        "      <td>10.990000</td>\n",
1659 |        "      <td>6.350000</td>\n",
1660 |        "      <td>79.880000</td>\n",
1661 |        "      <td>1.960000</td>\n",
1662 |        "      <td>227.840000</td>\n",
1663 |        "    </tr>\n",
1664 |        "    <tr>\n",
1665 |        "      <th>75%</th>\n",
1666 |        "      <td>2019.0</td>\n",
1667 |        "      <td>12.0</td>\n",
1668 |        "      <td>23.500000</td>\n",
1669 |        "      <td>23.0</td>\n",
1670 |        "      <td>14.070000</td>\n",
1671 |        "      <td>8.030000</td>\n",
1672 |        "      <td>85.345000</td>\n",
1673 |        "      <td>2.485000</td>\n",
1674 |        "      <td>282.305000</td>\n",
1675 |        "    </tr>\n",
1676 |        "    <tr>\n",
1677 |        "      <th>max</th>\n",
1678 |        "      <td>2019.0</td>\n",
1679 |        "      <td>12.0</td>\n",
1680 |        "      <td>31.000000</td>\n",
1681 |        "      <td>23.0</td>\n",
1682 |        "      <td>15.830000</td>\n",
1683 |        "      <td>11.170000</td>\n",
1684 |        "      <td>100.000000</td>\n",
1685 |        "      <td>6.880000</td>\n",
1686 |        "      <td>315.510000</td>\n",
1687 |        "    </tr>\n",
1688 |        "  </tbody>\n",
1689 |        "</table>\n",
1690 |        "</div>"
1691 |       ],
1692 |       "text/plain": [
1693 |        "         YEAR    MO         DY    HR        T2M       QV2M        RH2M  \\\n",
1694 |        "count    31.0  31.0  31.000000  31.0  31.000000  31.000000   31.000000   \n",
1695 |        "mean   2019.0  12.0  16.000000  23.0  11.237419   6.822581   79.817097   \n",
1696 |        "std       0.0   0.0   9.092121   0.0   3.281225   1.907761    9.095952   \n",
1697 |        "min    2019.0  12.0   1.000000  23.0   4.050000   4.150000   58.440000   \n",
1698 |        "25%    2019.0  12.0   8.500000  23.0   9.035000   5.400000   75.750000   \n",
1699 |        "50%    2019.0  12.0  16.000000  23.0  10.990000   6.350000   79.880000   \n",
1700 |        "75%    2019.0  12.0  23.500000  23.0  14.070000   8.030000   85.345000   \n",
1701 |        "max    2019.0  12.0  31.000000  23.0  15.830000  11.170000  100.000000   \n",
1702 |        "\n",
1703 |        "           WS10M       WD10M  \n",
1704 |        "count  31.000000   31.000000  \n",
1705 |        "mean    2.149677  217.814194  \n",
1706 |        "std     1.091071   74.989962  \n",
1707 |        "min     0.990000   50.440000  \n",
1708 |        "25%     1.460000  156.675000  \n",
1709 |        "50%     1.960000  227.840000  \n",
1710 |        "75%     2.485000  282.305000  \n",
1711 |        "max     6.880000  315.510000  "
1712 |       ]
1713 |      },
1714 |      "execution_count": 79,
1715 |      "metadata": {},
1716 |      "output_type": "execute_result"
1717 |     }
1718 |    ],
1719 |    "source": [
1720 |     "tempdf.describe()"
1721 |    ]
1722 |   },
1723 |   {
1724 |    "cell_type": "code",
1725 |    "execution_count": 84,
1726 |    "metadata": {},
1727 |    "outputs": [],
1728 |    "source": [
1729 |     "import matplotlib.pyplot as plt\n",
1730 |     "import seaborn as sns\n",
1731 |     "from sklearn.model_selection import train_test_split"
1732 |    ]
1733 |   },
1734 |   {
1735 |    "cell_type": "code",
1736 |    "execution_count": 86,
1737 |    "metadata": {},
1738 |    "outputs": [
1739 |     {
1740 |      "data": {
1741 |       "text/plain": [
1742 |        "4452"
1743 |       ]
1744 |      },
1745 |      "execution_count": 86,
1746 |      "metadata": {},
1747 |      "output_type": "execute_result"
1748 |     }
1749 |    ],
1750 |    "source": [
1751 |     "df['T2M'].nunique()"
1752 |    ]
1753 |   },
1754 |   {
1755 |    "cell_type": "code",
1756 |    "execution_count": null,
1757 |    "metadata": {},
1758 |    "outputs": [],
1759 |    "source": [
1760 |     "plt.figure(figsize=(18,6))\n",
1761 |     "sns.pairplot(df.drop('MO',axis=1),hue='T2M')\n",
1762 |     "plt.show()"
1763 |    ]
1764 |   },
1765 |   {
1766 |    "cell_type": "code",
1767 |    "execution_count": null,
1768 |    "metadata": {},
1769 |    "outputs": [],
1770 |    "source": []
1771 |   }
1772 |  ],
1773 |  "metadata": {
1774 |   "kernelspec": {
1775 |    "display_name": "Python 3",
1776 |    "language": "python",
1777 |    "name": "python3"
1778 |   },
1779 |   "language_info": {
1780 |    "codemirror_mode": {
1781 |     "name": "ipython",
1782 |     "version": 3
1783 |    },
1784 |    "file_extension": ".py",
1785 |    "mimetype": "text/x-python",
1786 |    "name": "python",
1787 |    "nbconvert_exporter": "python",
1788 |    "pygments_lexer": "ipython3",
1789 |    "version": "3.8.3"
1790 |   },
1791 |   "vscode": {
1792 |    "interpreter": {
1793 |     "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
1794 |    }
1795 |   }
1796 |  },
1797 |  "nbformat": 4,
1798 |  "nbformat_minor": 4
1799 | }
1800 | 


--------------------------------------------------------------------------------