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c0feccc3c7bc1e3949b9453794470f16
mooc-rr
Commits
f6c42b41
Commit
f6c42b41
authored
Jul 26, 2023
by
c0feccc3c7bc1e3949b9453794470f16
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final_version
parent
1bea1cf7
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SARS-CoV-2_analysis.ipynb
module3/exo3/SARS-CoV-2_analysis.ipynb
+79
-14
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module3/exo3/SARS-CoV-2_analysis.ipynb
View file @
f6c42b41
...
...
@@ -2523,21 +2523,30 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#
#
Visualization of different countries the cumulative number of people with SARS-CoV-2 disease since the beginning of the pandemic"
"# Visualization of different countries the cumulative number of people with SARS-CoV-2 disease since the beginning of the pandemic"
]
},
{
"cell_type": "code",
"execution_count": 37,
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot graph with a linear scale"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f421
662d4a
8>"
"<matplotlib.legend.Legend at 0x7f421
5a38c8
8>"
]
},
"execution_count":
37
,
"execution_count":
49
,
"metadata": {},
"output_type": "execute_result"
},
...
...
@@ -2555,24 +2564,80 @@
}
],
"source": [
"x = range(len(belgium_cases.T))\n",
"ax = plt.gca()\n",
"ax.get_xaxis().set_visible(False)\n",
"\n",
"plt.plot(belgium_cases.T, label='Belgium')\n",
"plt.plot(china_cases.T, label='China')\n",
"plt.plot(france_cases.T, label='France')\n",
"plt.plot(germany_cases.T, label='Germany')\n",
"plt.plot(italy_cases.T, label='Italy')\n",
"plt.plot(japan_cases.T, label='Japan')\n",
"plt.plot(netherlands_cases.T, label='Netherlands')\n",
"plt.plot(spain_cases.T, label='Spain')\n",
"plt.plot(UK_cases.T, label='United Kingdom')\n",
"plt.plot(
x,
belgium_cases.T, label='Belgium')\n",
"plt.plot(
x,
china_cases.T, label='China')\n",
"plt.plot(
x,
france_cases.T, label='France')\n",
"plt.plot(
x,
germany_cases.T, label='Germany')\n",
"plt.plot(
x,
italy_cases.T, label='Italy')\n",
"plt.plot(
x,
japan_cases.T, label='Japan')\n",
"plt.plot(
x,
netherlands_cases.T, label='Netherlands')\n",
"plt.plot(
x,
spain_cases.T, label='Spain')\n",
"plt.plot(
x,
UK_cases.T, label='United Kingdom')\n",
"\n",
"# Add labels and legend\n",
"plt.ylabel('Cumulative cases')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot graph with a logarithmic scale"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f42155e0748>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = plt.gca()\n",
"ax.get_xaxis().set_visible(False)\n",
"\n",
"plt.plot(x, belgium_cases.T, label='Belgium')\n",
"plt.plot(x, china_cases.T, label='China')\n",
"plt.plot(x, france_cases.T, label='France')\n",
"plt.plot(x, germany_cases.T, label='Germany')\n",
"plt.plot(x, italy_cases.T, label='Italy')\n",
"plt.plot(x, japan_cases.T, label='Japan')\n",
"plt.plot(x, netherlands_cases.T, label='Netherlands')\n",
"plt.plot(x, spain_cases.T, label='Spain')\n",
"plt.plot(x, UK_cases.T, label='United Kingdom')\n",
"\n",
"plt.yscale('log')\n",
"# Add labels and legend\n",
"plt.ylabel('Cumulative cases (log scale)')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
...
...
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