331 lines
616 KiB
Plaintext
331 lines
616 KiB
Plaintext
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{
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"cells": [
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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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"# Visualisation de Séries Temporelles"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"mcdon = pd.read_csv('mcdonalds.csv',index_col='Date',parse_dates=True)"
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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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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Adj. Close Adj. Volume\n",
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"Date \n",
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"1970-01-02 0.209761 2825604.0\n",
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"1970-01-05 0.213316 2210449.5\n",
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"1970-01-06 0.214501 1951168.5\n",
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"1970-01-07 0.213316 2728768.5\n",
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"1970-01-08 0.213316 2242404.0"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Adj. Close</th>\n <th>Adj. Volume</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1970-01-02</th>\n <td>0.209761</td>\n <td>2825604.0</td>\n </tr>\n <tr>\n <th>1970-01-05</th>\n <td>0.213316</td>\n <td>2210449.5</td>\n </tr>\n <tr>\n <th>1970-01-06</th>\n <td>0.214501</td>\n <td>1951168.5</td>\n </tr>\n <tr>\n <th>1970-01-07</th>\n <td>0.213316</td>\n <td>2728768.5</td>\n </tr>\n <tr>\n <th>1970-01-08</th>\n <td>0.213316</td>\n <td>2242404.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 3
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}
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],
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"source": [
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"mcdon.head()"
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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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"scrolled": true
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},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='Date'>"
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]
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},
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"metadata": {},
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"execution_count": 4
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"252.024627pt\" version=\"1.1\" viewBox=\"0 0 375.2875 252.024627\" width=\"375.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n <cc:Work>\r\n <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n <dc:date>2020-11-27T21:06:07.436654</dc:date>\r\n <dc:format>image/svg+xml</dc:format>\r\n <dc:creator>\r\n <cc:Agent>\r\n <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title>\r\n </cc:Agent>\r\n </dc:creator>\r\n </cc:Work>\r\n </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n <g id=\"patch_1\">\r\n <path d=\"M 0 252.024627 \r\nL 375.2875 252.024627 \r\nL 375.2875 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n </g>\r\n <g id=\"axes_1\">\r\n <g id=\"patch_2\">\r\n <path d=\"M 33.2875 203.04 \r\nL 368.0875 203.04 \r\nL 368.0875 7.2 \r\nL 33.2875 7.2 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n </g>\r\n <g id=\"matplotlib.axis_1\">\r\n <g id=\"xtick_1\">\r\n <g id=\"line2d_1\">\r\n <defs>\r\n <path d=\"M 0 0 \r\nL 0 3.5 \r\n\" id=\"m3dd6470934\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n </defs>\r\n <g>\r\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"48.488149\" xlink:href=\"#m3dd6470934\" y=\"203.04\"/>\r\n </g>\r\n </g>\r\n <g id=\"text_1\">\r\n <!-- 1970 -->\r\n <g transform=\"translate(25.407959 229.34544)rotate(-30)scale(0.1 -0.1)\">\r\n <defs>\r\n <path d=\"M 12.40625 8.296875 \r\nL 28.515625 8.296875 \r\nL 28.515625 63.921875 \r\nL 10.984375 60.40625 \r\nL 10.984375 69.390625 \r\nL 28.421875 72.90625 \r\nL 38.28125 72.90625 \r\nL 38.28125 8.296875 \r\nL 54.390625 8.296875 \r\nL 54.390625 0 \r\nL 12.40625 0 \r\nz\r\n\" id=\"DejaVuSans-49\"/>\r\n <path d=\"M 10.984375 1.515625 \r\nL 10.984375 10.5 \r\nQ 14.703125 8.734375 18.5 7.8125 \r\nQ 22.3125 6.890625 25.984375 6.890625 \r\nQ 35.75 6.890625 40.890625 13.453125 \r\nQ 46.046875 20.015625 46.78125 33.40625 \r\nQ 43.953125 29.203125 39.59375 26.953125 \r\nQ 35.25 24.703125 29.984375 24.703125 \r\nQ 19.046875 24.703125 12.671875 31.3125 \r\nQ 6.296875 37.9375 6.296875 49.421875 \r\nQ 6.296875 60.640625 12.9375 67.421875 \r\nQ 19.578125 74.21875 30.609375 74.21875 \r\nQ 43.265625 74.21875 49.921875 64.515625 \r\nQ 56.59375 54.828125 56.59375 36.375 \r\nQ 56.59375 19.140625 48.40625 8.859375 \r\nQ 40.234375 -1.421875 26.421875 -1.421875 \r\nQ 22.703125 -1.421875 18.890625 -0.6875 \r\nQ 15.09375 0.046875 10.984375 1.515625 \r\nz\r\nM 30.609375 32.421875 \r\nQ 37.25 32.421875 41.125 36.953125 \r\nQ 45.015625 41.5 45.015625 49.421875 \r\nQ 45.015625 57.28125 41.125 61.84375 \r\nQ 37.25 66.40625 30.609375 66.40625 \r\nQ 23.96875 66.40625 20.09375 61.84375 \r\nQ 16.21875 57.28125 16.21875 49.421875 \r\nQ 16.21875 41.5 20.09375 36.953125 \r\nQ 23.96875 32.421875 30.609375 32.421875 \r\nz\r\n\" id=\"DejaVuSans-57\"/>\r\n <path d=\"M 8.203125 72.90625 \r\nL 55.078125 72.90625 \r\nL 55.078125 68.703125 \r\nL 28.609375 0 \r\nL 18.3125 0 \r\nL 43.21875 64.59375 \r\nL 8.203125 64.59375 \r\nz\r\n\" id=\"DejaVuSans-55\"/>\r\n <path d=\"M 31.78125 66.40625 \r\nQ 24.171875 66.40625 20.328125 58.90625 \r\nQ 16.5 51.421875 16.5 36.375 \r\nQ 16.5 21.390625 20.328125 13.890625 \r\nQ 24.171875 6.390625 31.78125 6.390625 \r\nQ 39.453125 6.390625 43.28125 13.890625 \r\nQ 47.125 21.390625 47.125 36.375 \r\nQ 47.125 51.421875 43.28125 58.90625 \r\nQ 39.453125 66.40625 31.78125 66.40625 \r\nz\r\nM 31.78125 74.21875 \r\nQ 44.
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"source": [
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"mcdon['Adj. Close'].plot()"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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||
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"output_type": "execute_result",
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"data": {
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"text/plain": [
|
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"<AxesSubplot:xlabel='Date'>"
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]
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},
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"metadata": {},
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||
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"execution_count": 5
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 864x288 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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}
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|
],
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||
|
|
"source": [
|
||
|
|
"mcdon['Adj. Volume'].plot(figsize=(12,4))"
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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,
|
||
|
|
"metadata": {},
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||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='Date'>"
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||
|
|
]
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},
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||
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|
"metadata": {},
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||
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"execution_count": 6
|
||
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},
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||
|
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{
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||
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|
"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
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||
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"metadata": {
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||
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"needs_background": "light"
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||
|
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}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mcdon['Adj. Close'].plot(xlim=['2007-01-01','2009-01-01'],ylim=(20,50))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='Date'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 7
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
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"data": {
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||
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"text/plain": "<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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|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mcdon['Adj. Close'].plot(xlim=['2007-01-01','2009-01-01'],ylim=(20,50),\n",
|
||
|
|
" ls='--',c='red')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.dates as dates"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
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||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='Date'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
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|
"execution_count": 9
|
||
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},
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{
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||
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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||
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},
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||
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|
"metadata": {
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||
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|
"needs_background": "light"
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||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mcdon['Adj. Close'].plot(xlim=['2007-01-01','2009-01-01'],ylim=(20,50))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"idx = mcdon.loc['2007-01-01':'2007-05-01'].index"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"stock = mcdon.loc['2007-01-01':'2007-05-01']['Adj. Close']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Date\n",
|
||
|
|
"2007-01-03 31.662754\n",
|
||
|
|
"2007-01-04 31.424580\n",
|
||
|
|
"2007-01-05 31.424580\n",
|
||
|
|
"2007-01-08 31.547276\n",
|
||
|
|
"2007-01-09 31.605015\n",
|
||
|
|
" ... \n",
|
||
|
|
"2007-04-25 35.098239\n",
|
||
|
|
"2007-04-26 35.531283\n",
|
||
|
|
"2007-04-27 35.329196\n",
|
||
|
|
"2007-04-30 34.845630\n",
|
||
|
|
"2007-05-01 35.466327\n",
|
||
|
|
"Name: Adj. Close, Length: 82, dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 12
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"stock"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig,ax = plt.subplots()\n",
|
||
|
|
"ax.plot_date(idx,stock,'-')\n",
|
||
|
|
"\n",
|
||
|
|
"# EMPLACEMENT\n",
|
||
|
|
"ax.xaxis.set_major_locator(dates.MonthLocator())\n",
|
||
|
|
"\n",
|
||
|
|
"# FORMATAGE\n",
|
||
|
|
"ax.xaxis.set_major_formatter(dates.DateFormatter('\\n\\n\\n\\n%b-%Y'))\n",
|
||
|
|
"\n",
|
||
|
|
"ax.xaxis.set_minor_locator(dates.WeekdayLocator(byweekday=0))\n",
|
||
|
|
"ax.xaxis.set_minor_formatter(dates.DateFormatter('%d'))\n",
|
||
|
|
"\n",
|
||
|
|
"fig.autofmt_xdate()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.7.9-final"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|