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added style sheet discussion
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@bmtgoncalves
bmtgoncalves committed Dec 2, 2019
1 parent 17b4024 commit 1e6f126
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Showing 7 changed files with 1,903 additions and 1,202 deletions.
100 changes: 57 additions & 43 deletions Part I - Matplotlib.ipynb → 1. Matplotlib.ipynb
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"name": "stdout",
"output_type": "stream",
"text": [
"Wed Sep 25 2019 \n",
"watermark 1.8.1\n",
"matplotlib 3.1.0\n",
"numpy 1.16.2\n",
"pandas 0.24.2\n",
"Sun Dec 01 2019 \n",
"\n",
"CPython 3.7.3\n",
"IPython 6.2.1\n",
"\n",
"numpy 1.16.2\n",
"matplotlib 3.1.0\n",
"pandas 0.24.2\n",
"\n",
"compiler : Clang 4.0.1 (tags/RELEASE_401/final)\n",
"system : Darwin\n",
"release : 18.7.0\n",
"release : 19.0.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 8\n",
"interpreter: 64bit\n"
"interpreter: 64bit\n",
"Git hash : 17b4024f04091f8fdc1ee8a8e6705838976550c3\n"
]
}
],
"source": [
"%watermark -n -v -m -p numpy,matplotlib,pandas"
"%watermark -n -v -m -g -iv"
]
},
{
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{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11d7d7ba8>]"
"[<matplotlib.lines.Line2D at 0x11f38b0b8>]"
]
},
"execution_count": 5,
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{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11da92cf8>]"
"[<matplotlib.lines.Line2D at 0x11f64a128>]"
]
},
"execution_count": 6,
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{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11dae4780>"
"<matplotlib.legend.Legend at 0x11f690438>"
]
},
"execution_count": 7,
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{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11de03860>"
"<matplotlib.legend.Legend at 0x11f86ec88>"
]
},
"execution_count": 8,
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{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11fea4048>"
"<matplotlib.axes._subplots.AxesSubplot at 0x121a5a828>"
]
},
"execution_count": 13,
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{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x120217d68>"
"<matplotlib.legend.Legend at 0x121dbe390>"
]
},
"execution_count": 15,
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{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11da226d8>"
"<matplotlib.legend.Legend at 0x1220e4860>"
]
},
"execution_count": 17,
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{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x12085fc50>"
"<matplotlib.legend.Legend at 0x122416128>"
]
},
"execution_count": 18,
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"cell_type": "markdown",
"metadata": {},
"source": [
"We'll plot these three datasets using both bar and pie plots. "
"We'll plot these three datasets using both bar and pie plots. As we want to make sure we use the same colors for the corresponding bar/slice, we start by explicitly obtaining the color cycle"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can supply the right color sequence to use for the bars"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 25)"
]
},
"execution_count": 20,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
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}
],
"source": [
"prop_cycle = plt.rcParams['axes.prop_cycle']\n",
"colors = prop_cycle.by_key()['color']\n",
"\n",
"fig, axs = plt.subplots(nrows=2, ncols=3)\n",
"fig.set_size_inches(14,10)\n",
"\n",
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},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 22,
"metadata": {},
"outputs": [
{
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"<BarContainer object of 1 artists>"
]
},
"execution_count": 21,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
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},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 23,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 26,
"metadata": {},
"outputs": [
{
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"4 Burkina Faso 1217.032994 14326203 52.295 0"
]
},
"execution_count": 25,
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 27,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 29,
"metadata": {},
"outputs": [
{
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"4 40 - 50 28289 45107 -45107 4"
]
},
"execution_count": 28,
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 30,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x10b4cd668>"
"<matplotlib.image.AxesImage at 0x123637c18>"
]
},
"execution_count": 31,
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
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},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 33,
"metadata": {},
"outputs": [
{
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},
{
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"execution_count": 33,
"execution_count": 34,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 35,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 36,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 38,
"metadata": {},
"outputs": [
{
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