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Commit 0a06574a authored by f25bf2222ee7e546119ff38ac2d2fbf1's avatar f25bf2222ee7e546119ff38ac2d2fbf1
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module2/exo5/exo5_en.ipynb
View file @ 0a06574a
......@@ -706,7 +706,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo1/influenza-like-illness-analysis.ipynb
View file @ 0a06574a
......@@ -9,10 +9,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
......@@ -30,10 +28,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
......@@ -83,6 +79,81 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(type(raw_data))\n",
"raw_data['inc'] = raw_data['inc'].astype(int) \n",
"print(raw_data['inc'].idxmax())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1838</th>\n",
" <td>198919</td>\n",
" <td>3</td>\n",
" <td>-</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1838 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1838 FR France "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
......@@ -96,9 +167,976 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202431</td>\n",
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" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202430</td>\n",
" <td>3</td>\n",
" <td>36908</td>\n",
" <td>28974.0</td>\n",
" <td>44842.0</td>\n",
" <td>55</td>\n",
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" <td>67.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202429</td>\n",
" <td>3</td>\n",
" <td>39560</td>\n",
" <td>32592.0</td>\n",
" <td>46528.0</td>\n",
" <td>59</td>\n",
" <td>49.0</td>\n",
" <td>69.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202428</td>\n",
" <td>3</td>\n",
" <td>54342</td>\n",
" <td>45781.0</td>\n",
" <td>62903.0</td>\n",
" <td>81</td>\n",
" <td>68.0</td>\n",
" <td>94.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202427</td>\n",
" <td>3</td>\n",
" <td>47364</td>\n",
" <td>40234.0</td>\n",
" <td>54494.0</td>\n",
" <td>71</td>\n",
" <td>60.0</td>\n",
" <td>82.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202426</td>\n",
" <td>3</td>\n",
" <td>44219</td>\n",
" <td>36956.0</td>\n",
" <td>51482.0</td>\n",
" <td>66</td>\n",
" <td>55.0</td>\n",
" <td>77.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202425</td>\n",
" <td>3</td>\n",
" <td>47204</td>\n",
" <td>40300.0</td>\n",
" <td>54108.0</td>\n",
" <td>71</td>\n",
" <td>61.0</td>\n",
" <td>81.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202424</td>\n",
" <td>3</td>\n",
" <td>41110</td>\n",
" <td>34671.0</td>\n",
" <td>47549.0</td>\n",
" <td>62</td>\n",
" <td>52.0</td>\n",
" <td>72.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202423</td>\n",
" <td>3</td>\n",
" <td>35875</td>\n",
" <td>30610.0</td>\n",
" <td>41140.0</td>\n",
" <td>54</td>\n",
" <td>46.0</td>\n",
" <td>62.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202422</td>\n",
" <td>3</td>\n",
" <td>33772</td>\n",
" <td>28274.0</td>\n",
" <td>39270.0</td>\n",
" <td>51</td>\n",
" <td>43.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202421</td>\n",
" <td>3</td>\n",
" <td>21963</td>\n",
" <td>17556.0</td>\n",
" <td>26370.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202420</td>\n",
" <td>3</td>\n",
" <td>20057</td>\n",
" <td>15780.0</td>\n",
" <td>24334.0</td>\n",
" <td>30</td>\n",
" <td>24.0</td>\n",
" <td>36.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202419</td>\n",
" <td>3</td>\n",
" <td>15375</td>\n",
" <td>11274.0</td>\n",
" <td>19476.0</td>\n",
" <td>23</td>\n",
" <td>17.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202418</td>\n",
" <td>3</td>\n",
" <td>22409</td>\n",
" <td>17653.0</td>\n",
" <td>27165.0</td>\n",
" <td>34</td>\n",
" <td>27.0</td>\n",
" <td>41.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202417</td>\n",
" <td>3</td>\n",
" <td>27042</td>\n",
" <td>21410.0</td>\n",
" <td>32674.0</td>\n",
" <td>41</td>\n",
" <td>33.0</td>\n",
" <td>49.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202416</td>\n",
" <td>3</td>\n",
" <td>28882</td>\n",
" <td>23305.0</td>\n",
" <td>34459.0</td>\n",
" <td>43</td>\n",
" <td>35.0</td>\n",
" <td>51.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202415</td>\n",
" <td>3</td>\n",
" <td>30229</td>\n",
" <td>24648.0</td>\n",
" <td>35810.0</td>\n",
" <td>45</td>\n",
" <td>37.0</td>\n",
" <td>53.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202414</td>\n",
" <td>3</td>\n",
" <td>31813</td>\n",
" <td>26529.0</td>\n",
" <td>37097.0</td>\n",
" <td>48</td>\n",
" <td>40.0</td>\n",
" <td>56.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202413</td>\n",
" <td>3</td>\n",
" <td>35090</td>\n",
" <td>29607.0</td>\n",
" <td>40573.0</td>\n",
" <td>53</td>\n",
" <td>45.0</td>\n",
" <td>61.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202412</td>\n",
" <td>3</td>\n",
" <td>40639</td>\n",
" <td>34582.0</td>\n",
" <td>46696.0</td>\n",
" <td>61</td>\n",
" <td>52.0</td>\n",
" <td>70.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202411</td>\n",
" <td>3</td>\n",
" <td>50268</td>\n",
" <td>43331.0</td>\n",
" <td>57205.0</td>\n",
" <td>75</td>\n",
" <td>65.0</td>\n",
" <td>85.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202410</td>\n",
" <td>3</td>\n",
" <td>60107</td>\n",
" <td>52623.0</td>\n",
" <td>67591.0</td>\n",
" <td>90</td>\n",
" <td>79.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202409</td>\n",
" <td>3</td>\n",
" <td>71121</td>\n",
" <td>62920.0</td>\n",
" <td>79322.0</td>\n",
" <td>107</td>\n",
" <td>95.0</td>\n",
" <td>119.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202408</td>\n",
" <td>3</td>\n",
" <td>104566</td>\n",
" <td>94520.0</td>\n",
" <td>114612.0</td>\n",
" <td>157</td>\n",
" <td>142.0</td>\n",
" <td>172.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202407</td>\n",
" <td>3</td>\n",
" <td>138078</td>\n",
" <td>127050.0</td>\n",
" <td>149106.0</td>\n",
" <td>207</td>\n",
" <td>190.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202406</td>\n",
" <td>3</td>\n",
" <td>190062</td>\n",
" <td>177955.0</td>\n",
" <td>202169.0</td>\n",
" <td>285</td>\n",
" <td>267.0</td>\n",
" <td>303.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202405</td>\n",
" <td>3</td>\n",
" <td>216237</td>\n",
" <td>203595.0</td>\n",
" <td>228879.0</td>\n",
" <td>324</td>\n",
" <td>305.0</td>\n",
" <td>343.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202404</td>\n",
" <td>3</td>\n",
" <td>213196</td>\n",
" <td>200547.0</td>\n",
" <td>225845.0</td>\n",
" <td>320</td>\n",
" <td>301.0</td>\n",
" <td>339.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202403</td>\n",
" <td>3</td>\n",
" <td>163457</td>\n",
" <td>152276.0</td>\n",
" <td>174638.0</td>\n",
" <td>245</td>\n",
" <td>228.0</td>\n",
" <td>262.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202402</td>\n",
" <td>3</td>\n",
" <td>129436</td>\n",
" <td>119453.0</td>\n",
" <td>139419.0</td>\n",
" <td>194</td>\n",
" <td>179.0</td>\n",
" <td>209.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2045</th>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
" <td>26096</td>\n",
" <td>19621.0</td>\n",
" <td>32571.0</td>\n",
" <td>47</td>\n",
" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2046</th>\n",
" <td>198520</td>\n",
" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2047</th>\n",
" <td>198519</td>\n",
" <td>3</td>\n",
" <td>43154</td>\n",
" <td>32821.0</td>\n",
" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2048</th>\n",
" <td>198518</td>\n",
" <td>3</td>\n",
" <td>40555</td>\n",
" <td>29935.0</td>\n",
" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2049</th>\n",
" <td>198517</td>\n",
" <td>3</td>\n",
" <td>34053</td>\n",
" <td>24366.0</td>\n",
" <td>43740.0</td>\n",
" <td>62</td>\n",
" <td>44.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2050</th>\n",
" <td>198516</td>\n",
" <td>3</td>\n",
" <td>50362</td>\n",
" <td>36451.0</td>\n",
" <td>64273.0</td>\n",
" <td>91</td>\n",
" <td>66.0</td>\n",
" <td>116.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2051</th>\n",
" <td>198515</td>\n",
" <td>3</td>\n",
" <td>63881</td>\n",
" <td>45538.0</td>\n",
" <td>82224.0</td>\n",
" <td>116</td>\n",
" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2052</th>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
" <td>154690.0</td>\n",
" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2053</th>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2054</th>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2055</th>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2056</th>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2057</th>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2063</th>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2064</th>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2065</th>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2066</th>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2067</th>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2068</th>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2069</th>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2070</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2071</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2072</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2073</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2074</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2074 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202431 3 30726 23171.0 38281.0 46 35.0 \n",
"1 202430 3 36908 28974.0 44842.0 55 43.0 \n",
"2 202429 3 39560 32592.0 46528.0 59 49.0 \n",
"3 202428 3 54342 45781.0 62903.0 81 68.0 \n",
"4 202427 3 47364 40234.0 54494.0 71 60.0 \n",
"5 202426 3 44219 36956.0 51482.0 66 55.0 \n",
"6 202425 3 47204 40300.0 54108.0 71 61.0 \n",
"7 202424 3 41110 34671.0 47549.0 62 52.0 \n",
"8 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"9 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"10 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"11 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"12 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"13 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"14 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"15 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"16 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"17 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"18 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"19 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"20 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"21 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"22 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"23 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"24 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"25 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"26 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"27 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"28 202403 3 163457 152276.0 174638.0 245 228.0 \n",
"29 202402 3 129436 119453.0 139419.0 194 179.0 \n",
"... ... ... ... ... ... ... ... \n",
"2045 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2046 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2047 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2048 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2049 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2050 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2051 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2052 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2053 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2054 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2055 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2056 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2057 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2058 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2059 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2060 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2061 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2062 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2063 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2064 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2065 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2066 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2067 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2068 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2069 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2070 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2071 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2072 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2073 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2074 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 57.0 FR France \n",
"1 67.0 FR France \n",
"2 69.0 FR France \n",
"3 94.0 FR France \n",
"4 82.0 FR France \n",
"5 77.0 FR France \n",
"6 81.0 FR France \n",
"7 72.0 FR France \n",
"8 62.0 FR France \n",
"9 59.0 FR France \n",
"10 40.0 FR France \n",
"11 36.0 FR France \n",
"12 29.0 FR France \n",
"13 41.0 FR France \n",
"14 49.0 FR France \n",
"15 51.0 FR France \n",
"16 53.0 FR France \n",
"17 56.0 FR France \n",
"18 61.0 FR France \n",
"19 70.0 FR France \n",
"20 85.0 FR France \n",
"21 101.0 FR France \n",
"22 119.0 FR France \n",
"23 172.0 FR France \n",
"24 224.0 FR France \n",
"25 303.0 FR France \n",
"26 343.0 FR France \n",
"27 339.0 FR France \n",
"28 262.0 FR France \n",
"29 209.0 FR France \n",
"... ... ... ... \n",
"2045 59.0 FR France \n",
"2046 64.0 FR France \n",
"2047 97.0 FR France \n",
"2048 93.0 FR France \n",
"2049 80.0 FR France \n",
"2050 116.0 FR France \n",
"2051 149.0 FR France \n",
"2052 281.0 FR France \n",
"2053 395.0 FR France \n",
"2054 485.0 FR France \n",
"2055 544.0 FR France \n",
"2056 689.0 FR France \n",
"2057 722.0 FR France \n",
"2058 762.0 FR France \n",
"2059 926.0 FR France \n",
"2060 1113.0 FR France \n",
"2061 1236.0 FR France \n",
"2062 832.0 FR France \n",
"2063 459.0 FR France \n",
"2064 207.0 FR France \n",
"2065 190.0 FR France \n",
"2066 198.0 FR France \n",
"2067 224.0 FR France \n",
"2068 266.0 FR France \n",
"2069 219.0 FR France \n",
"2070 176.0 FR France \n",
"2071 163.0 FR France \n",
"2072 195.0 FR France \n",
"2073 308.0 FR France \n",
"2074 213.0 FR France \n",
"\n",
"[2074 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
......@@ -123,10 +1161,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def convert_week(year_and_week_int):\n",
......@@ -154,10 +1190,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
......@@ -180,9 +1214,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
......@@ -200,9 +1242,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-0966cd984262>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [
"sorted_data['inc'].plot()"
]
......@@ -365,7 +1424,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo3/exercice_en.ipynb
View file @ 0a06574a
{
"cells": [],
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CO2 Data Analysis at Mauna Loa Observatory\n",
"\n",
"This notebook provides an analysis of atmospheric CO2 concentrations measured at the Mauna Loa Observatory, Hawaii. The dataset includes monthly CO2 measurements, both raw and adjusted, from various years.\n",
"\n",
"## Dataset Information\n",
"\n",
"- **Year**: Year of measurement.\n",
"- **Month**: Month of measurement.\n",
"- **Day_Count**: Identifier related to the day count (potentially not relevant for this analysis).\n",
"- **Decimal_Year**: Year in decimal format.\n",
"- **CO2**: Raw CO2 measurement (ppm).\n",
"- **Seasonally_Adjusted_CO2**: CO2 measurement with seasonal adjustment.\n",
"- **Smoothed_CO2**: Smoothed CO2 measurement.\n",
"- **Smoothed_Seasonally_Adjusted_CO2**: Smoothed and seasonally adjusted CO2 measurement.\n",
"- **Interpolated_CO2**: CO2 measurement with missing values filled in.\n",
"- **Interpolated_Seasonally_Adjusted_CO2**: Seasonally adjusted CO2 with missing values filled in.\n",
"- **Station_ID**: Station identifier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Load the dataset\n",
"file_path = 'monthly_in_situ_co2_mlo.csv'\n",
"co2_data = pd.read_csv(file_path, skiprows=100)\n",
"\n",
"# Adjust the column names for clarity\n",
"co2_data.columns = [\n",
" 'Year', 'Month', 'Day_Count', 'Decimal_Year', 'CO2', \n",
" 'Seasonally_Adjusted_CO2', 'Smoothed_CO2', 'Smoothed_Seasonally_Adjusted_CO2',\n",
" 'Interpolated_CO2', 'Interpolated_Seasonally_Adjusted_CO2', 'Station_ID'\n",
"]\n",
"\n",
"co2_data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Explore basic statistics of the dataset\n",
"co2_data.describe()\n",
"\n",
"# Check for missing values\n",
"co2_data.isnull().sum()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot CO2 over time\n",
"plt.figure(figsize=(14, 8))\n",
"sns.lineplot(x='Decimal_Year', y='CO2', data=co2_data, label='Raw CO2')\n",
"sns.lineplot(x='Decimal_Year', y='Seasonally_Adjusted_CO2', data=co2_data, label='Seasonally Adjusted CO2')\n",
"plt.title('CO2 Concentrations Over Time at Mauna Loa Observatory')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('CO2 (ppm)')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +101,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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