126 lines
2.5 KiB
Plaintext
126 lines
2.5 KiB
Plaintext
{
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualization with Matplotlib"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Importing Matplotlib\n",
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"\n",
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"Just as we use the `np` shorthand for NumPy and the `pd` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
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]
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},
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"metadata": {
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"tags": []
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},
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"source": [
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"The `plt` interface is what we will use most often, as you shall see throughout this part of the book."
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Setting Styles\n",
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"\n",
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"We will use the `plt.style` directive to choose appropriate aesthetic styles for our figures.\n",
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"Here we will set the `classic` style, which ensures that the plots we create use the classic Matplotlib style:"
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]
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},
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"tags": []
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"outputs": [],
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"source": [
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"plt.style.use('classic')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"x = np.linspace(0, 10, 100)\n",
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"\n",
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"fig = plt.figure()\n",
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"plt.plot(x, np.sin(x), '-')\n",
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"plt.plot(x, np.cos(x), '--');"
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]
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}
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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