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Commit e244ff8c authored by 0c07d516632c138624818b1d249c5f44's avatar 0c07d516632c138624818b1d249c5f44
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module3/exo1/analyse-syndrome-grippal.ipynb
View file @ e244ff8c
......@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
......@@ -28,10 +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\""
......@@ -61,9 +59,976 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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" <td>88.0</td>\n",
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" <td>66784.0</td>\n",
" <td>89</td>\n",
" <td>78.0</td>\n",
" <td>100.0</td>\n",
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" <td>84531</td>\n",
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" <td>112.0</td>\n",
" <td>140.0</td>\n",
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" <th>7</th>\n",
" <td>202508</td>\n",
" <td>3</td>\n",
" <td>136020</td>\n",
" <td>124824.0</td>\n",
" <td>147216.0</td>\n",
" <td>203</td>\n",
" <td>186.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>202507</td>\n",
" <td>3</td>\n",
" <td>208952</td>\n",
" <td>195988.0</td>\n",
" <td>221916.0</td>\n",
" <td>312</td>\n",
" <td>293.0</td>\n",
" <td>331.0</td>\n",
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" <th>9</th>\n",
" <td>202506</td>\n",
" <td>3</td>\n",
" <td>273519</td>\n",
" <td>258159.0</td>\n",
" <td>288879.0</td>\n",
" <td>408</td>\n",
" <td>385.0</td>\n",
" <td>431.0</td>\n",
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" <td>334395</td>\n",
" <td>318416.0</td>\n",
" <td>350374.0</td>\n",
" <td>499</td>\n",
" <td>475.0</td>\n",
" <td>523.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>11</th>\n",
" <td>202504</td>\n",
" <td>3</td>\n",
" <td>350043</td>\n",
" <td>332885.0</td>\n",
" <td>367201.0</td>\n",
" <td>522</td>\n",
" <td>496.0</td>\n",
" <td>548.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>12</th>\n",
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" <td>266627.0</td>\n",
" <td>377</td>\n",
" <td>356.0</td>\n",
" <td>398.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>13</th>\n",
" <td>202502</td>\n",
" <td>3</td>\n",
" <td>257247</td>\n",
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" <td>271503.0</td>\n",
" <td>384</td>\n",
" <td>363.0</td>\n",
" <td>405.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>14</th>\n",
" <td>202501</td>\n",
" <td>3</td>\n",
" <td>231549</td>\n",
" <td>214627.0</td>\n",
" <td>248471.0</td>\n",
" <td>345</td>\n",
" <td>320.0</td>\n",
" <td>370.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>15</th>\n",
" <td>202452</td>\n",
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" <td>201726</td>\n",
" <td>185870.0</td>\n",
" <td>217582.0</td>\n",
" <td>302</td>\n",
" <td>278.0</td>\n",
" <td>326.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>16</th>\n",
" <td>202451</td>\n",
" <td>3</td>\n",
" <td>201697</td>\n",
" <td>187843.0</td>\n",
" <td>215551.0</td>\n",
" <td>302</td>\n",
" <td>281.0</td>\n",
" <td>323.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>202450</td>\n",
" <td>3</td>\n",
" <td>136694</td>\n",
" <td>126369.0</td>\n",
" <td>147019.0</td>\n",
" <td>205</td>\n",
" <td>190.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202449</td>\n",
" <td>3</td>\n",
" <td>108487</td>\n",
" <td>99037.0</td>\n",
" <td>117937.0</td>\n",
" <td>163</td>\n",
" <td>149.0</td>\n",
" <td>177.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202448</td>\n",
" <td>3</td>\n",
" <td>87381</td>\n",
" <td>78687.0</td>\n",
" <td>96075.0</td>\n",
" <td>131</td>\n",
" <td>118.0</td>\n",
" <td>144.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202447</td>\n",
" <td>3</td>\n",
" <td>76286</td>\n",
" <td>67626.0</td>\n",
" <td>84946.0</td>\n",
" <td>114</td>\n",
" <td>101.0</td>\n",
" <td>127.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202446</td>\n",
" <td>3</td>\n",
" <td>56399</td>\n",
" <td>49006.0</td>\n",
" <td>63792.0</td>\n",
" <td>85</td>\n",
" <td>74.0</td>\n",
" <td>96.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202445</td>\n",
" <td>3</td>\n",
" <td>47347</td>\n",
" <td>40843.0</td>\n",
" <td>53851.0</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>23</th>\n",
" <td>202444</td>\n",
" <td>3</td>\n",
" <td>36039</td>\n",
" <td>30122.0</td>\n",
" <td>41956.0</td>\n",
" <td>54</td>\n",
" <td>45.0</td>\n",
" <td>63.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202443</td>\n",
" <td>3</td>\n",
" <td>46572</td>\n",
" <td>39928.0</td>\n",
" <td>53216.0</td>\n",
" <td>70</td>\n",
" <td>60.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202442</td>\n",
" <td>3</td>\n",
" <td>67785</td>\n",
" <td>60009.0</td>\n",
" <td>75561.0</td>\n",
" <td>102</td>\n",
" <td>90.0</td>\n",
" <td>114.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202441</td>\n",
" <td>3</td>\n",
" <td>79435</td>\n",
" <td>71386.0</td>\n",
" <td>87484.0</td>\n",
" <td>119</td>\n",
" <td>107.0</td>\n",
" <td>131.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202440</td>\n",
" <td>3</td>\n",
" <td>84965</td>\n",
" <td>76555.0</td>\n",
" <td>93375.0</td>\n",
" <td>127</td>\n",
" <td>114.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202439</td>\n",
" <td>3</td>\n",
" <td>91660</td>\n",
" <td>82937.0</td>\n",
" <td>100383.0</td>\n",
" <td>137</td>\n",
" <td>124.0</td>\n",
" <td>150.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202438</td>\n",
" <td>3</td>\n",
" <td>91786</td>\n",
" <td>82903.0</td>\n",
" <td>100669.0</td>\n",
" <td>138</td>\n",
" <td>125.0</td>\n",
" <td>151.0</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>2081</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>2082</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>2083</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>2084</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>2085</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>2086</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>2087</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>2088</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>2089</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>2090</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>2091</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>2092</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>2093</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>2094</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>2095</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>2096</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>2097</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>2098</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>2099</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>2100</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>2101</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>2102</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>2103</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>2104</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>2105</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>2106</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>2107</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>2108</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>2109</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>2110</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>2111 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202515 3 38813 31411.0 46215.0 58 47.0 \n",
"1 202514 3 37870 31403.0 44337.0 57 47.0 \n",
"2 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"3 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"4 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"5 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"6 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"7 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"8 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"9 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"10 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"11 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"12 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"13 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"14 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"15 202452 3 201726 185870.0 217582.0 302 278.0 \n",
"16 202451 3 201697 187843.0 215551.0 302 281.0 \n",
"17 202450 3 136694 126369.0 147019.0 205 190.0 \n",
"18 202449 3 108487 99037.0 117937.0 163 149.0 \n",
"19 202448 3 87381 78687.0 96075.0 131 118.0 \n",
"20 202447 3 76286 67626.0 84946.0 114 101.0 \n",
"21 202446 3 56399 49006.0 63792.0 85 74.0 \n",
"22 202445 3 47347 40843.0 53851.0 71 61.0 \n",
"23 202444 3 36039 30122.0 41956.0 54 45.0 \n",
"24 202443 3 46572 39928.0 53216.0 70 60.0 \n",
"25 202442 3 67785 60009.0 75561.0 102 90.0 \n",
"26 202441 3 79435 71386.0 87484.0 119 107.0 \n",
"27 202440 3 84965 76555.0 93375.0 127 114.0 \n",
"28 202439 3 91660 82937.0 100383.0 137 124.0 \n",
"29 202438 3 91786 82903.0 100669.0 138 125.0 \n",
"... ... ... ... ... ... ... ... \n",
"2081 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2082 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2083 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2084 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2085 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2086 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2087 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2088 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2089 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2090 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2091 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2092 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2093 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2094 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2095 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2096 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2097 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2098 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2099 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2100 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2101 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2102 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2103 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2104 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2105 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2106 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2107 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2108 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2109 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2110 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 69.0 FR France \n",
"1 67.0 FR France \n",
"2 68.0 FR France \n",
"3 88.0 FR France \n",
"4 100.0 FR France \n",
"5 101.0 FR France \n",
"6 140.0 FR France \n",
"7 220.0 FR France \n",
"8 331.0 FR France \n",
"9 431.0 FR France \n",
"10 523.0 FR France \n",
"11 548.0 FR France \n",
"12 398.0 FR France \n",
"13 405.0 FR France \n",
"14 370.0 FR France \n",
"15 326.0 FR France \n",
"16 323.0 FR France \n",
"17 220.0 FR France \n",
"18 177.0 FR France \n",
"19 144.0 FR France \n",
"20 127.0 FR France \n",
"21 96.0 FR France \n",
"22 81.0 FR France \n",
"23 63.0 FR France \n",
"24 80.0 FR France \n",
"25 114.0 FR France \n",
"26 131.0 FR France \n",
"27 140.0 FR France \n",
"28 150.0 FR France \n",
"29 151.0 FR France \n",
"... ... ... ... \n",
"2081 59.0 FR France \n",
"2082 64.0 FR France \n",
"2083 97.0 FR France \n",
"2084 93.0 FR France \n",
"2085 80.0 FR France \n",
"2086 116.0 FR France \n",
"2087 149.0 FR France \n",
"2088 281.0 FR France \n",
"2089 395.0 FR France \n",
"2090 485.0 FR France \n",
"2091 544.0 FR France \n",
"2092 689.0 FR France \n",
"2093 722.0 FR France \n",
"2094 762.0 FR France \n",
"2095 926.0 FR France \n",
"2096 1113.0 FR France \n",
"2097 1236.0 FR France \n",
"2098 832.0 FR France \n",
"2099 459.0 FR France \n",
"2100 207.0 FR France \n",
"2101 190.0 FR France \n",
"2102 198.0 FR France \n",
"2103 224.0 FR France \n",
"2104 266.0 FR France \n",
"2105 219.0 FR France \n",
"2106 176.0 FR France \n",
"2107 163.0 FR France \n",
"2108 195.0 FR France \n",
"2109 308.0 FR France \n",
"2110 213.0 FR France \n",
"\n",
"[2111 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data"
......@@ -78,9 +1043,73 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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"<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>1874</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",
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"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1874 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1874 FR France "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
......@@ -94,9 +1123,976 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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"</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>202515</td>\n",
" <td>3</td>\n",
" <td>38813</td>\n",
" <td>31411.0</td>\n",
" <td>46215.0</td>\n",
" <td>58</td>\n",
" <td>47.0</td>\n",
" <td>69.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202514</td>\n",
" <td>3</td>\n",
" <td>37870</td>\n",
" <td>31403.0</td>\n",
" <td>44337.0</td>\n",
" <td>57</td>\n",
" <td>47.0</td>\n",
" <td>67.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202513</td>\n",
" <td>3</td>\n",
" <td>39673</td>\n",
" <td>33686.0</td>\n",
" <td>45660.0</td>\n",
" <td>59</td>\n",
" <td>50.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202512</td>\n",
" <td>3</td>\n",
" <td>52543</td>\n",
" <td>45627.0</td>\n",
" <td>59459.0</td>\n",
" <td>78</td>\n",
" <td>68.0</td>\n",
" <td>88.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202511</td>\n",
" <td>3</td>\n",
" <td>59469</td>\n",
" <td>52154.0</td>\n",
" <td>66784.0</td>\n",
" <td>89</td>\n",
" <td>78.0</td>\n",
" <td>100.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202510</td>\n",
" <td>3</td>\n",
" <td>60334</td>\n",
" <td>53048.0</td>\n",
" <td>67620.0</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>6</th>\n",
" <td>202509</td>\n",
" <td>3</td>\n",
" <td>84531</td>\n",
" <td>74994.0</td>\n",
" <td>94068.0</td>\n",
" <td>126</td>\n",
" <td>112.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202508</td>\n",
" <td>3</td>\n",
" <td>136020</td>\n",
" <td>124824.0</td>\n",
" <td>147216.0</td>\n",
" <td>203</td>\n",
" <td>186.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202507</td>\n",
" <td>3</td>\n",
" <td>208952</td>\n",
" <td>195988.0</td>\n",
" <td>221916.0</td>\n",
" <td>312</td>\n",
" <td>293.0</td>\n",
" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202506</td>\n",
" <td>3</td>\n",
" <td>273519</td>\n",
" <td>258159.0</td>\n",
" <td>288879.0</td>\n",
" <td>408</td>\n",
" <td>385.0</td>\n",
" <td>431.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202505</td>\n",
" <td>3</td>\n",
" <td>334395</td>\n",
" <td>318416.0</td>\n",
" <td>350374.0</td>\n",
" <td>499</td>\n",
" <td>475.0</td>\n",
" <td>523.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202504</td>\n",
" <td>3</td>\n",
" <td>350043</td>\n",
" <td>332885.0</td>\n",
" <td>367201.0</td>\n",
" <td>522</td>\n",
" <td>496.0</td>\n",
" <td>548.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202503</td>\n",
" <td>3</td>\n",
" <td>252772</td>\n",
" <td>238917.0</td>\n",
" <td>266627.0</td>\n",
" <td>377</td>\n",
" <td>356.0</td>\n",
" <td>398.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202502</td>\n",
" <td>3</td>\n",
" <td>257247</td>\n",
" <td>242991.0</td>\n",
" <td>271503.0</td>\n",
" <td>384</td>\n",
" <td>363.0</td>\n",
" <td>405.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202501</td>\n",
" <td>3</td>\n",
" <td>231549</td>\n",
" <td>214627.0</td>\n",
" <td>248471.0</td>\n",
" <td>345</td>\n",
" <td>320.0</td>\n",
" <td>370.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202452</td>\n",
" <td>3</td>\n",
" <td>201726</td>\n",
" <td>185870.0</td>\n",
" <td>217582.0</td>\n",
" <td>302</td>\n",
" <td>278.0</td>\n",
" <td>326.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202451</td>\n",
" <td>3</td>\n",
" <td>201697</td>\n",
" <td>187843.0</td>\n",
" <td>215551.0</td>\n",
" <td>302</td>\n",
" <td>281.0</td>\n",
" <td>323.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202450</td>\n",
" <td>3</td>\n",
" <td>136694</td>\n",
" <td>126369.0</td>\n",
" <td>147019.0</td>\n",
" <td>205</td>\n",
" <td>190.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202449</td>\n",
" <td>3</td>\n",
" <td>108487</td>\n",
" <td>99037.0</td>\n",
" <td>117937.0</td>\n",
" <td>163</td>\n",
" <td>149.0</td>\n",
" <td>177.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202448</td>\n",
" <td>3</td>\n",
" <td>87381</td>\n",
" <td>78687.0</td>\n",
" <td>96075.0</td>\n",
" <td>131</td>\n",
" <td>118.0</td>\n",
" <td>144.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202447</td>\n",
" <td>3</td>\n",
" <td>76286</td>\n",
" <td>67626.0</td>\n",
" <td>84946.0</td>\n",
" <td>114</td>\n",
" <td>101.0</td>\n",
" <td>127.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202446</td>\n",
" <td>3</td>\n",
" <td>56399</td>\n",
" <td>49006.0</td>\n",
" <td>63792.0</td>\n",
" <td>85</td>\n",
" <td>74.0</td>\n",
" <td>96.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202445</td>\n",
" <td>3</td>\n",
" <td>47347</td>\n",
" <td>40843.0</td>\n",
" <td>53851.0</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>23</th>\n",
" <td>202444</td>\n",
" <td>3</td>\n",
" <td>36039</td>\n",
" <td>30122.0</td>\n",
" <td>41956.0</td>\n",
" <td>54</td>\n",
" <td>45.0</td>\n",
" <td>63.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202443</td>\n",
" <td>3</td>\n",
" <td>46572</td>\n",
" <td>39928.0</td>\n",
" <td>53216.0</td>\n",
" <td>70</td>\n",
" <td>60.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202442</td>\n",
" <td>3</td>\n",
" <td>67785</td>\n",
" <td>60009.0</td>\n",
" <td>75561.0</td>\n",
" <td>102</td>\n",
" <td>90.0</td>\n",
" <td>114.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202441</td>\n",
" <td>3</td>\n",
" <td>79435</td>\n",
" <td>71386.0</td>\n",
" <td>87484.0</td>\n",
" <td>119</td>\n",
" <td>107.0</td>\n",
" <td>131.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202440</td>\n",
" <td>3</td>\n",
" <td>84965</td>\n",
" <td>76555.0</td>\n",
" <td>93375.0</td>\n",
" <td>127</td>\n",
" <td>114.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202439</td>\n",
" <td>3</td>\n",
" <td>91660</td>\n",
" <td>82937.0</td>\n",
" <td>100383.0</td>\n",
" <td>137</td>\n",
" <td>124.0</td>\n",
" <td>150.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202438</td>\n",
" <td>3</td>\n",
" <td>91786</td>\n",
" <td>82903.0</td>\n",
" <td>100669.0</td>\n",
" <td>138</td>\n",
" <td>125.0</td>\n",
" <td>151.0</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>2081</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>2082</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>2083</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>2084</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>2085</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>2086</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>2087</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>2088</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>2089</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>2090</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>2091</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>2092</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>2093</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>2094</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>2095</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>2096</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>2097</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>2098</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>2099</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>2100</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>2101</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>2102</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>2103</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>2104</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>2105</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>2106</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>2107</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>2108</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>2109</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>2110</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>2110 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202515 3 38813 31411.0 46215.0 58 47.0 \n",
"1 202514 3 37870 31403.0 44337.0 57 47.0 \n",
"2 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"3 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"4 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"5 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"6 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"7 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"8 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"9 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"10 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"11 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"12 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"13 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"14 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"15 202452 3 201726 185870.0 217582.0 302 278.0 \n",
"16 202451 3 201697 187843.0 215551.0 302 281.0 \n",
"17 202450 3 136694 126369.0 147019.0 205 190.0 \n",
"18 202449 3 108487 99037.0 117937.0 163 149.0 \n",
"19 202448 3 87381 78687.0 96075.0 131 118.0 \n",
"20 202447 3 76286 67626.0 84946.0 114 101.0 \n",
"21 202446 3 56399 49006.0 63792.0 85 74.0 \n",
"22 202445 3 47347 40843.0 53851.0 71 61.0 \n",
"23 202444 3 36039 30122.0 41956.0 54 45.0 \n",
"24 202443 3 46572 39928.0 53216.0 70 60.0 \n",
"25 202442 3 67785 60009.0 75561.0 102 90.0 \n",
"26 202441 3 79435 71386.0 87484.0 119 107.0 \n",
"27 202440 3 84965 76555.0 93375.0 127 114.0 \n",
"28 202439 3 91660 82937.0 100383.0 137 124.0 \n",
"29 202438 3 91786 82903.0 100669.0 138 125.0 \n",
"... ... ... ... ... ... ... ... \n",
"2081 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2082 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2083 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2084 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2085 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2086 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2087 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2088 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2089 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2090 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2091 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2092 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2093 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2094 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2095 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2096 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2097 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2098 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2099 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2100 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2101 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2102 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2103 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2104 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2105 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2106 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2107 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2108 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2109 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2110 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 69.0 FR France \n",
"1 67.0 FR France \n",
"2 68.0 FR France \n",
"3 88.0 FR France \n",
"4 100.0 FR France \n",
"5 101.0 FR France \n",
"6 140.0 FR France \n",
"7 220.0 FR France \n",
"8 331.0 FR France \n",
"9 431.0 FR France \n",
"10 523.0 FR France \n",
"11 548.0 FR France \n",
"12 398.0 FR France \n",
"13 405.0 FR France \n",
"14 370.0 FR France \n",
"15 326.0 FR France \n",
"16 323.0 FR France \n",
"17 220.0 FR France \n",
"18 177.0 FR France \n",
"19 144.0 FR France \n",
"20 127.0 FR France \n",
"21 96.0 FR France \n",
"22 81.0 FR France \n",
"23 63.0 FR France \n",
"24 80.0 FR France \n",
"25 114.0 FR France \n",
"26 131.0 FR France \n",
"27 140.0 FR France \n",
"28 150.0 FR France \n",
"29 151.0 FR France \n",
"... ... ... ... \n",
"2081 59.0 FR France \n",
"2082 64.0 FR France \n",
"2083 97.0 FR France \n",
"2084 93.0 FR France \n",
"2085 80.0 FR France \n",
"2086 116.0 FR France \n",
"2087 149.0 FR France \n",
"2088 281.0 FR France \n",
"2089 395.0 FR France \n",
"2090 485.0 FR France \n",
"2091 544.0 FR France \n",
"2092 689.0 FR France \n",
"2093 722.0 FR France \n",
"2094 762.0 FR France \n",
"2095 926.0 FR France \n",
"2096 1113.0 FR France \n",
"2097 1236.0 FR France \n",
"2098 832.0 FR France \n",
"2099 459.0 FR France \n",
"2100 207.0 FR France \n",
"2101 190.0 FR France \n",
"2102 198.0 FR France \n",
"2103 224.0 FR France \n",
"2104 266.0 FR France \n",
"2105 219.0 FR France \n",
"2106 176.0 FR France \n",
"2107 163.0 FR France \n",
"2108 195.0 FR France \n",
"2109 308.0 FR France \n",
"2110 213.0 FR France \n",
"\n",
"[2110 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
......@@ -122,7 +2118,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
......@@ -152,10 +2148,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
......@@ -179,9 +2173,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"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",
......@@ -203,6 +2205,8 @@
"metadata": {},
"outputs": [],
"source": [
"\n",
"sorted_data['inc'] = sorted_data['inc'].astype(int)\n",
"sorted_data['inc'].plot()"
]
},
......@@ -252,10 +2256,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
......@@ -274,7 +2276,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -298,9 +2300,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"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-13-81ad72216830>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\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": [
"yearly_incidence.plot(style='*')"
]
......@@ -314,9 +2333,59 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"1999 1214713082934385324913033696810837170752023524...\n",
"2023 1325712222758674989283141202139528781399024192...\n",
"1998 1386208617993226184251314542323110189132921693...\n",
"2000 1478264071499757760033348191754312430285532418...\n",
"2024 1528614641191442666331695382474908563218720036...\n",
"1986 1695874882261140389302152798622219276053981949...\n",
"2007 1773001572217011071786253289451195013920166732...\n",
"2002 1899900299043994663802597362540718113202411896...\n",
"1996 1946700905188724963659489513123184991624514980...\n",
"2008 1965132418933531177110014494769159851351712987...\n",
"2019 2048183919621368150632154915734971747409804877...\n",
"2001 2159140516905188559029902243741719338272374002...\n",
"1987 2247102731155762105879182159982661528665335163...\n",
"2014 2270470947401889129146395027932453719794123911...\n",
"2020 2337159215931672101022953172489770918505712277...\n",
"2012 2409259024213312417846068365140071702114053748...\n",
"1992 2464625165420992372238024297460143732674028185...\n",
"2013 2696638164923858827763847775147152025717873186...\n",
"1990 2721711479406039466147691202218296246223145343...\n",
"2016 2845159415377602291922881784028595131001358518...\n",
"2005 2949349616974501440877539103174832268025639158...\n",
"1997 3010145354012731674112229259470837710765991811...\n",
"2010 3025523151244352498531660460241013321379241212...\n",
"2021 3144207319003098396053641402823252186071819424...\n",
"2018 3256266724062497279428159716114631494912725124...\n",
"2017 3691210015672233342023724065596910598133791390...\n",
"2006 3767157228155281244870821034721881239942664726...\n",
"2009 3972437872292552190125122429367921957869992590...\n",
"2011 3989273267129504926427949545318893611978183311...\n",
"1994 4055416337553217298455894361702310492147831380...\n",
"2015 4143322923582367226850187714105678583165262248...\n",
"1993 4233496340462953638351557480983713535175932409...\n",
"1995 5027478642723459455628065949153661587815962140...\n",
"2004 5164161915042194150410951147351481126592308622...\n",
"2003 5455108726702843212616791240970671598981371103...\n",
"1988 8233721564046631614979061120816782323282464142...\n",
"2022 8567106658699673786038055781795301141915052179...\n",
"1989 8758703379374950779775391553735243437303988246...\n",
"1991 8811399020193425424051111461561820433197231301...\n",
"dtype: object"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
......
module3/exo3/Cholera_Deaths.prj 0 → 100644
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PROJCS["OSGB 1936 / British National Grid",GEOGCS["OSGB 1936",DATUM["OSGB 1936",SPHEROID["Airy 1830",6377563.396,299.3249646]],PRIMEM["Greenwich",0.0],UNIT["degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["false_easting",400000.0],PARAMETER["false_northing",-100000.0],PARAMETER["central_meridian",-2.0],PARAMETER["scale_factor",0.9996012717],PARAMETER["latitude_of_origin",49.0],UNIT["m",1.0]]
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</Metadata>
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PROJCS["OSGB 1936 / British National Grid",GEOGCS["OSGB 1936",DATUM["OSGB 1936",SPHEROID["Airy 1830",6377563.396,299.3249646]],PRIMEM["Greenwich",0.0],UNIT["degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["false_easting",400000.0],PARAMETER["false_northing",-100000.0],PARAMETER["central_meridian",-2.0],PARAMETER["scale_factor",0.9996012717],PARAMETER["latitude_of_origin",49.0],UNIT["m",1.0]]
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README file for Snow GIS data
-----------------------------
This zip file contains a number of GIS layers relating to John Snow's 1854 investigation of a
Cholera outbreak in London - considered by many to be the first use of geographical analysis
in an epidemiological study. More details on the history are available at
http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak
This file contains a number of GIS layers created from Snow's original map which allow analyses to be
conducted on the data in modern GIS systems. For example, clustering of cases can be analysed and the
effect of spatial aggregation in modern anonymised health data releases. Of course, it's also just
interesting to look at the area, and how little it has changed since 1854.
Files included:
(Many of the items in the list consist of many actual files (for example .shp, .dbf etc)
* OSMap Raster Modern OS map of the area of the outbreak (from OS Open Data - contains Ordnance Survey data © Crown copyright and database right 2013)
* OSMap_Greyscale Raster Same as above, but in greyscale for easier visualisation (altered by conversion to greyscale, from OS Open Data - contains Ordnance Survey data © Crown copyright and database right 2013)
* SnowMap Raster Snow's original map, georeferenced and warped so that it accurately overlays the OS map
* CholeraDeaths Vector Points for each location of one or more deaths. Attribute value gives number of deaths at that location
* Pumps Vector Points for each location of a pump
Created and compiled by Robin Wilson (robin@rtwilson.com, www.rtwilson.com/academic) - Jan 2011.
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
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"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Some explanations
This is an org-mode document with code examples in R. Once opened in
Emacs, this document can easily be exported to HTML, PDF, and Office
formats. For more information on org-mode, see
https://orgmode.org/guide/.
When you type the shortcut =C-c C-e h o=, this document will be
exported as HTML. All the code in it will be re-executed, and the
results will be retrieved and included into the exported document. If
you do not want to re-execute all code each time, you can delete the #
and the space before ~#+PROPERTY:~ in the header of this document.
Like we showed in the video, R code is included as follows (and is
exxecuted by typing ~C-c C-c~):
#+begin_src R :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: [1] "Hello world!"
And now the same but in an R session. This is the most frequent
situation, because R is really an interactive language. With a
session, R's state, i.e. the values of all the variables, remains
persistent from one code block to the next. The code is still executed
using ~C-c C-c~.
#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src
#+RESULTS:
: speed dist
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
: Median :15.0 Median : 36.00
: Mean :15.4 Mean : 42.98
: 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00
Finally, an example for graphical output:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:./cars.png]]
Note the parameter ~:exports results~, which indicates that the code
will not appear in the exported document. We recommend that in the
context of this MOOC, you always leave this parameter setting as
~:exports both~, because we want your analyses to be perfectly
transparent and reproducible.
Watch out: the figure generated by the code block is /not/ stored in
the org document. It's a plain file, here named ~cars.png~. You have
to commit it explicitly if you want your analysis to be legible and
understandable on GitLab.
Finally, don't forget that we provide in the resource section of this
MOOC a configuration with a few keyboard shortcuts that allow you to
quickly create code blocks in R by typing ~<r~ or ~<R~ followed by
~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
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#+TITLE: Votre titre
#+AUTHOR: Votre nom
#+DATE: La date du jour
#+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Quelques explications
Ceci est un document org-mode avec quelques exemples de code
R. Une fois ouvert dans emacs, ce document peut aisément être
exporté au format HTML, PDF, et Office. Pour plus de détails sur
org-mode vous pouvez consulter https://orgmode.org/guide/.
Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera
compilé en html. Tout le code contenu sera ré-exécuté, les résultats
récupérés et inclus dans un document final. Si vous ne souhaitez pas
ré-exécuter tout le code à chaque fois, il vous suffit de supprimer
le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce
document.
Comme nous vous l'avons montré dans la vidéo, on inclut du code
R de la façon suivante (et on l'exécute en faisant ~C-c C-c~):
#+begin_src R :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: [1] "Hello world!"
Voici la même chose, mais avec une session R (c'est le cas le
plus courant, R étant vraiment un langage interactif), donc une
persistance d'un bloc à l'autre (et on l'exécute toujours en faisant
~C-c C-c~).
#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src
#+RESULTS:
: speed dist
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
: Median :15.0 Median : 36.00
: Mean :15.4 Mean : 42.98
: 3rd Qu.:19.0 3rd Qu.: 56.00
: Max. :25.0 Max. :120.00
Et enfin, voici un exemple de sortie graphique:
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
plot(cars)
#+end_src
#+RESULTS:
[[file:./cars.png]]
Vous remarquerez le paramètre ~:exports results~ qui indique que le code
ne doit pas apparaître dans la version finale du document. Nous vous
recommandons dans le cadre de ce MOOC de ne pas changer ce paramètre
(indiquer ~both~) car l'objectif est que vos analyses de données soient
parfaitement transparentes pour être reproductibles.
Attention, la figure ainsi générée n'est pas stockée dans le document
org. C'est un fichier ordinaire, ici nommé ~cars.png~. N'oubliez pas
de le committer si vous voulez que votre analyse soit lisible et
compréhensible sur GitLab.
Enfin, pour les prochains exercices, nous ne vous fournirons pas
forcément de fichier de départ, ça sera à vous de le créer, par
exemple en repartant de ce document et de le commiter vers
gitlab. N'oubliez pas que nous vous fournissons dans les ressources de
ce MOOC une configuration avec un certain nombre de raccourcis
claviers permettant de créer rapidement les blocs de code R (en
faisant ~<r~ ou ~<R~ suivi de ~Tab~).
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
informations et les remplacer par votre document computationnel.
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---
title: "Your title"
author: "Your name"
date: "Today's date"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some explanations
This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
```{r cars}
summary(cars)
```
It is also straightforward to include figures. For example:
```{r pressure, echo=FALSE}
plot(pressure)
```
Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
Now it's your turn! You can delete all this information and replace it by your computational document.
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{
"cells": [],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"file_extension": ".py",
"mimetype": "text/x-python",
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"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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---
title: "Votre titre"
author: "Votre nom"
date: "La date du jour"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Quelques explications
Ceci est un document R markdown que vous pouvez aisément exporter au format HTML, PDF, et MS Word. Pour plus de détails sur R Markdown consultez <http://rmarkdown.rstudio.com>.
Lorsque vous cliquerez sur le bouton **Knit** ce document sera compilé afin de ré-exécuter le code R et d'inclure les résultats dans un document final. Comme nous vous l'avons montré dans la vidéo, on inclue du code R de la façon suivante:
```{r cars}
summary(cars)
```
Et on peut aussi aisément inclure des figures. Par exemple:
```{r pressure, echo=FALSE}
plot(pressure)
```
Vous remarquerez le paramètre `echo = FALSE` qui indique que le code ne doit pas apparaître dans la version finale du document. Nous vous recommandons dans le cadre de ce MOOC de ne pas utiliser ce paramètre car l'objectif est que vos analyses de données soient parfaitement transparentes pour être reproductibles.
Comme les résultats ne sont pas stockés dans les fichiers Rmd, pour faciliter la relecture de vos analyses par d'autres personnes, vous aurez donc intérêt à générer un HTML ou un PDF et à le commiter.
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces informations et les remplacer par votre document computationnel.
module3/exo3/exercice_fr.ipynb deleted 100644 → 0
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"metadata": {
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"display_name": "Python 3",
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"name": "python3"
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"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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#+TITLE: Your title
#+AUTHOR: Your name
#+DATE: Today's date
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Some explanations
This is an org-mode document with code examples in R. Once opened in
Emacs, this document can easily be exported to HTML, PDF, and Office
formats. For more information on org-mode, see
https://orgmode.org/guide/.
When you type the shortcut =C-c C-e h o=, this document will be
exported as HTML. All the code in it will be re-executed, and the
results will be retrieved and included into the exported document. If
you do not want to re-execute all code each time, you can delete the #
and the space before ~#+PROPERTY:~ in the header of this document.
Like we showed in the video, Python code is included as follows (and
is exxecuted by typing ~C-c C-c~):
#+begin_src python :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: Hello world!
And now the same but in an Python session. With a session, Python's
state, i.e. the values of all the variables, remains persistent from
one code block to the next. The code is still executed using ~C-c
C-c~.
#+begin_src python :results output :session :exports both
import numpy
x=numpy.linspace(-15,15)
print(x)
#+end_src
#+RESULTS:
#+begin_example
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245
0.30612245 0.91836735 1.53061224 2.14285714 2.75510204
3.36734694 3.97959184 4.59183673 5.20408163 5.81632653
6.42857143 7.04081633 7.65306122 8.26530612 8.87755102
9.48979592 10.10204082 10.71428571 11.32653061 11.93877551
12.55102041 13.16326531 13.7755102 14.3877551 15. ]
#+end_example
Finally, an example for graphical output:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:./cosxsx.png]]
Note the parameter ~:exports results~, which indicates that the code
will not appear in the exported document. We recommend that in the
context of this MOOC, you always leave this parameter setting as
~:exports both~, because we want your analyses to be perfectly
transparent and reproducible.
Watch out: the figure generated by the code block is /not/ stored in
the org document. It's a plain file, here named ~cosxsx.png~. You have
to commit it explicitly if you want your analysis to be legible and
understandable on GitLab.
Finally, don't forget that we provide in the resource section of this
MOOC a configuration with a few keyboard shortcuts that allow you to
quickly create code blocks in Python by typing ~<p~, ~<P~ or ~<PP~
followed by ~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
module3/exo3/exercice_python_fr.org deleted 100644 → 0
View file @ 777d6092
#+TITLE: Votre titre
#+AUTHOR: Votre nom
#+DATE: La date du jour
#+LANGUAGE: fr
# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Quelques explications
Ceci est un document org-mode avec quelques exemples de code
python. Une fois ouvert dans emacs, ce document peut aisément être
exporté au format HTML, PDF, et Office. Pour plus de détails sur
org-mode vous pouvez consulter https://orgmode.org/guide/.
Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera
compilé en html. Tout le code contenu sera ré-exécuté, les résultats
récupérés et inclus dans un document final. Si vous ne souhaitez pas
ré-exécuter tout le code à chaque fois, il vous suffit de supprimer
le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce
document.
Comme nous vous l'avons montré dans la vidéo, on inclue du code
python de la façon suivante (et on l'exécute en faisant ~C-c C-c~):
#+begin_src python :results output :exports both
print("Hello world!")
#+end_src
#+RESULTS:
: Hello world!
Voici la même chose, mais avec une session python, donc une
persistance d'un bloc à l'autre (et on l'exécute toujours en faisant
~C-c C-c~).
#+begin_src python :results output :session :exports both
import numpy
x=numpy.linspace(-15,15)
print(x)
#+end_src
#+RESULTS:
#+begin_example
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245
0.30612245 0.91836735 1.53061224 2.14285714 2.75510204
3.36734694 3.97959184 4.59183673 5.20408163 5.81632653
6.42857143 7.04081633 7.65306122 8.26530612 8.87755102
9.48979592 10.10204082 10.71428571 11.32653061 11.93877551
12.55102041 13.16326531 13.7755102 14.3877551 15. ]
#+end_example
Et enfin, voici un exemple de sortie graphique:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:./cosxsx.png]]
Vous remarquerez le paramètre ~:exports results~ qui indique que le code
ne doit pas apparaître dans la version finale du document. Nous vous
recommandons dans le cadre de ce MOOC de ne pas changer ce paramètre
(indiquer ~both~) car l'objectif est que vos analyses de données soient
parfaitement transparentes pour être reproductibles.
Attention, la figure ainsi générée n'est pas stockée dans le document
org. C'est un fichier ordinaire, ici nommé ~cosxsx.png~. N'oubliez pas
de le committer si vous voulez que votre analyse soit lisible et
compréhensible sur GitLab.
Enfin, n'oubliez pas que nous vous fournissons dans les ressources de
ce MOOC une configuration avec un certain nombre de raccourcis
claviers permettant de créer rapidement les blocs de code python (en
faisant ~<p~, ~<P~ ou ~<PP~ suivi de ~Tab~).
Maintenant, à vous de jouer! Vous pouvez effacer toutes ces
informations et les remplacer par votre document computationnel.
module3/exo3/requirements.txt 0 → 100644
View file @ e244ff8c
geopandas==0.9.0
folium==0.12.1
branca==0.4.2
rasterio==1.2.10
pandas==1.1.5
shapely==1.7.1
fiona==1.8.20
pyproj==2.6
numpy==1.19.5
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