191 lines
94 KiB
Plaintext
191 lines
94 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 69,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sb\n",
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"%matplotlib inline"
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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": 70,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = np.linspace(0,100)\n",
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"Y = X**2"
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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": 71,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f9f519afd60>]"
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]
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},
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"execution_count": 71,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.plot(X,Y)"
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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": 72,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f9f51935250>]"
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]
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},
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"execution_count": 72,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
|
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|
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.subplot(3,1,(1,2))\n",
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"plt.plot(X,Y,'red')\n",
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"plt.subplot(3,1,(3,3))\n",
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"plt.plot(X,Y,'blue')"
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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": 73,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f9f517ddbb0>]"
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]
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},
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x300 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig_1 = plt.figure(figsize=(6,3))\n",
|
||
|
|
"axes_1 = fig_1.add_axes([0,0.6,0.5,1])\n",
|
||
|
|
"axes_1.plot(X,Y)\n",
|
||
|
|
"axes_2 = fig_1.add_axes([0,0,0.5,0.5])\n",
|
||
|
|
"axes_2.plot(Y,X)\n",
|
||
|
|
"# axes_1 = fig_2.add"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.PairGrid at 0x7f9f51c93820>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 250x250 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df = pd.DataFrame(X,Y)\n",
|
||
|
|
"sb."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.10"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|