331 lines
44 KiB
Plaintext
331 lines
44 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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\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": "code",
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"execution_count": 3,
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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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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.arange(0,10)"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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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": 12,
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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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"Text(0, 0.5, 'Y label')"
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]
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},
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"execution_count": 12,
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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 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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,'r--')\n",
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"plt.xlim(0,4)\n",
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"plt.ylim(0,10)\n",
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"plt.title('TITLE')\n",
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"plt.xlabel('X label')\n",
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"plt.ylabel('Y label')"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"mat = np.arange(0,100).reshape(10,10)"
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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": 14,
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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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"array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
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" [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
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" [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
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" [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
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" [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
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" [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
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" [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
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" [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
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" [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
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" [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
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]
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},
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"execution_count": 14,
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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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"source": [
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"mat"
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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": 15,
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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.image.AxesImage at 0x10ec20668>"
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]
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},
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"execution_count": 15,
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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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"text/plain": [
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||
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"<Figure size 432x288 with 1 Axes>"
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]
|
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|
|
},
|
||
|
|
"metadata": {
|
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"needs_background": "light"
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},
|
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"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(mat)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
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||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"mat = np.random.randint(0,1000,(10,10))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
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"cell_type": "code",
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||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
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"text/plain": [
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"<matplotlib.colorbar.Colorbar at 0x1103a5a20>"
|
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|
|
]
|
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|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"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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|
||
|
|
"text/plain": [
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||
|
|
"<Figure size 432x288 with 2 Axes>"
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||
|
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]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(mat)\n",
|
||
|
|
"plt.colorbar()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"df = pd.read_csv('./Tensorflow-Bootcamp-master/00-Crash-Course-Basics/salaries.csv')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
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|
|
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|
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|
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|
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|
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|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
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|
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|
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"\n",
|
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|
|
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|
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|
|
" text-align: right;\n",
|
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|
|
" }\n",
|
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|
|
"</style>\n",
|
||
|
|
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|
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|
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|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Name</th>\n",
|
||
|
|
" <th>Salary</th>\n",
|
||
|
|
" <th>Age</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>John</td>\n",
|
||
|
|
" <td>50000</td>\n",
|
||
|
|
" <td>34</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>Sally</td>\n",
|
||
|
|
" <td>120000</td>\n",
|
||
|
|
" <td>45</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>Alyssa</td>\n",
|
||
|
|
" <td>80000</td>\n",
|
||
|
|
" <td>27</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Name Salary Age\n",
|
||
|
|
"0 John 50000 34\n",
|
||
|
|
"1 Sally 120000 45\n",
|
||
|
|
"2 Alyssa 80000 27"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x10fe6b860>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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": "3.7.3"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|