diff --git a/module2/exo5/exo5_en.ipynb b/module2/exo5/exo5_en.ipynb index 6a0919d7ba4c9594341883f6d283db9ea050de72..c6e426cc5a3f07652dd44b207b4aaa7f66574f56 100644 --- a/module2/exo5/exo5_en.ipynb +++ b/module2/exo5/exo5_en.ipynb @@ -706,7 +706,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/influenza-like-illness-analysis.ipynb b/module3/exo1/influenza-like-illness-analysis.ipynb index 87092fc69cd90ff457ea56284b789cb0de199a41..0262d3a596ccf885129ca09262e7b38ed2e715b4 100644 --- a/module3/exo1/influenza-like-illness-analysis.ipynb +++ b/module3/exo1/influenza-like-illness-analysis.ipynb @@ -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": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18381989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "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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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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120243033690828974.044842.05543.067.0FRFrance
220242933956032592.046528.05949.069.0FRFrance
320242835434245781.062903.08168.094.0FRFrance
420242734736440234.054494.07160.082.0FRFrance
520242634421936956.051482.06655.077.0FRFrance
620242534720440300.054108.07161.081.0FRFrance
720242434111034671.047549.06252.072.0FRFrance
820242333587530610.041140.05446.062.0FRFrance
920242233377228274.039270.05143.059.0FRFrance
1020242132196317556.026370.03326.040.0FRFrance
1120242032005715780.024334.03024.036.0FRFrance
1220241931537511274.019476.02317.029.0FRFrance
1320241832240917653.027165.03427.041.0FRFrance
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1820241333509029607.040573.05345.061.0FRFrance
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2020241135026843331.057205.07565.085.0FRFrance
2120241036010752623.067591.09079.0101.0FRFrance
2220240937112162920.079322.010795.0119.0FRFrance
23202408310456694520.0114612.0157142.0172.0FRFrance
242024073138078127050.0149106.0207190.0224.0FRFrance
252024063190062177955.0202169.0285267.0303.0FRFrance
262024053216237203595.0228879.0324305.0343.0FRFrance
272024043213196200547.0225845.0320301.0339.0FRFrance
282024033163457152276.0174638.0245228.0262.0FRFrance
292024023129436119453.0139419.0194179.0209.0FRFrance
.................................
204519852132609619621.032571.04735.059.0FRFrance
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2074 rows × 10 columns

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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\u001b[0m in \u001b[0;36m\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, diff --git a/module3/exo3/exercice_en.ipynb b/module3/exo3/exercice_en.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..fb431eb5f4731728092bf0777bd668db8b7c0d32 100644 --- a/module3/exo3/exercice_en.ipynb +++ b/module3/exo3/exercice_en.ipynb @@ -1,5 +1,90 @@ { - "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 } -