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Commit 16519685 authored by cb7068ff783828641c551b8f395e2fc0's avatar cb7068ff783828641c551b8f395e2fc0
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Adding Playfair's original graph

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module3/exo3/exercice.ipynb
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...@@ -484,44 +484,48 @@ ...@@ -484,44 +484,48 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We sort by increasing years and verify that the gap between two points is not more than 5 years:" "We can replace the index by the column year and sort by increasing years. We verify that the gap between two points is not more than 5 years:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 40,
"metadata": { "metadata": {
"hidePrompt": true "hidePrompt": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"sorted_data = data.set_index('period').sort_index()" "sorted_data = data.set_index('Year').sort_index()\n",
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
" assert (p2-p1)<=5 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Eventually plotting Playfair's graph:**"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 63,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "ValueError", "data": {
"evalue": "`bins` must increase monotonically, when an array", "text/plain": [
"output_type": "error", "<BarContainer object of 53 artists>"
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-34-2eb44a81e25a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Wages'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtwinx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Wheat'\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/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 3135\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3136\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3137\u001b[0;31m stacked=stacked, normed=normed, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 3138\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3139\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1865\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1869\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 6637\u001b[0m \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6638\u001b[0m \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6639\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6640\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6641\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\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/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ravel_and_check_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mbin_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniform_bins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_bin_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;31m# Histogram is an integer or a float array depending on the weights.\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/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_bin_edges\u001b[0;34m(a, bins, range, weights)\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbin_edges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mbin_edges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\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[1;32m 360\u001b[0m raise ValueError(\n\u001b[0;32m--> 361\u001b[0;31m '`bins` must increase monotonically, when an array')\n\u001b[0m\u001b[1;32m 362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: `bins` must increase monotonically, when an array"
] ]
}, },
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{ {
"data": { "data": {
"image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 2 Axes>" "<Figure size 432x288 with 2 Axes>"
] ]
...@@ -535,9 +539,15 @@ ...@@ -535,9 +539,15 @@
"source": [ "source": [
"plt.figure()\n", "plt.figure()\n",
"ax1 = plt.gca()\n", "ax1 = plt.gca()\n",
"plt.plot(data['Year'], data['Wages'])\n", "ax1.set_xlabel('Time (year)')\n",
"ax1.set_ylabel('Wages (Shillings/week)')\n",
"ax1.plot(sorted_data['Wages'], linewidth=3)\n",
"ax1.fill_between(sorted_data.index, 0, sorted_data['Wages'],\n",
" color='red')\n",
"ax2 = ax1.twinx()\n", "ax2 = ax1.twinx()\n",
"plt.hist(data['Year'], data['Wheat'])\n" "ax2.set_ylabel('Price of wheat (Shillings/quarter)')\n",
"ax1.bar(sorted_data.index, sorted_data['Wheat'], width=5,\n",
" color='grey', zorder=0)"
] ]
}, },
{ {
......
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