{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
" \n",
"___\n",
"# Matplotlib Sub Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# COMMON MISTAKE!\n",
"# DON'T FORGET THE .PYPLOT part\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE: For users running .py scripts in an IDE like PyCharm or Sublime Text Editor. You will not see the plots in a notebook, instead if you are using another editor, you'll use: *plt.show()* at the end of all your plotting commands to have the figure pop up in another window.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"a = np.linspace(0,10,11)\n",
"b = a ** 4"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.000e+00, 1.000e+00, 1.600e+01, 8.100e+01, 2.560e+02, 6.250e+02,\n",
" 1.296e+03, 2.401e+03, 4.096e+03, 6.561e+03, 1.000e+04])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"x = np.arange(0,10)\n",
"y = 2 * x"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# plt.subplots()\n",
"\n",
"**NOTE: Make sure you put the commands all together in the same cell as we do in this notebook and video!**\n",
"\n",
"The plt.subplots() object will act as a more automatic axis manager. This makes it much easier to show multiple plots side by side.\n",
"\n",
"Note how we use tuple unpacking to grba both the Figure object and a numpy array of axes:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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