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Commit 65d9e95

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‎Lesson05/.ipynb_checkpoints/Exercise38-checkpoint.ipynb

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‎Lesson05/.ipynb_checkpoints/Exercise39-checkpoint.ipynb

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‎Lesson05/.ipynb_checkpoints/Exercise40-checkpoint.ipynb

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‎Lesson05/.ipynb_checkpoints/Exercise41-checkpoint.ipynb

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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import pandas library and read DataFrame from DATA_PATH \n",
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"\n",
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"import pandas as pd \n",
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"%matplotlib inline\n",
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"import numpy as np \n",
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"\n",
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"from pathlib import Path\n",
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"from bokeh.plotting import figure, show, output_file\n",
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"from bokeh.plotting import figure, output_notebook, show, ColumnDataSource\n",
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"\n",
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"DATA_PATH = Path('../datasets/chap5_data/')"
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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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"**set the output as notebook**"
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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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"output_notebook()"
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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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"**Read the data as Dataframe. Filter the rows according to countries(`UK` and `France`).\n",
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"Make it as `ColumnDataSource` so that bokeh can access it by columns names**"
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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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"uk_eu_population = pd.read_csv(DATA_PATH / \"uk_europe_population_2005_2019.csv\")\n",
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"uk_population = uk_eu_population[uk_eu_population.country == 'UK']\n",
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"source_uk = ColumnDataSource(dict(year=uk_population.year, change=uk_population.change))\n",
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"france_population = uk_eu_population[uk_eu_population.country == 'France']\n",
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"source_france = ColumnDataSource(dict(year=france_population.year, change=france_population.change))"
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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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"**plot both line on figure**"
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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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"\n",
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"TOOLTIPS = [\n",
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" (\"population:\", \"@change\")\n",
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"]\n",
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"\n",
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"r = figure(title=\"Line Plot comparing Population Change\", plot_height=450, tooltips=TOOLTIPS)\n",
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"\n",
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"r.line(x=\"year\", y=\"change\", source=source_uk, color='#1F78B4', legend='UK', line_color=\"red\", line_width=3)\n",
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"r.line(x=\"year\", y=\"change\", source=source_france, legend='France', line_color=\"black\", line_width=2)\n",
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"\n",
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"r.grid.grid_line_alpha=0.3\n",
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"\n",
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"show(r)"
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#step-1 download the sample data from library,\n",
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"# import bokeh.sampledata\n",
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"# bokeh.sampledata.download()"
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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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"from pathlib import Path\n",
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"DATA_PATH = Path('../datasets/chap5_data/')"
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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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"#step-2 import required libraries\n",
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"import pandas as pd\n",
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"from bokeh.plotting import figure, output_notebook, show, ColumnDataSource\n",
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"from bokeh.io import push_notebook, show, output_notebook\n",
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"from ipywidgets import interact\n",
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"output_notebook()"
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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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"# Step-3 initalize the figure\n",
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"TOOLTIPS = [\n",
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" (\"date\", \"@date\"),\n",
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" (\"value\", \"@close\")\n",
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"]\n",
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"p = figure(title=\"Interactive plot to change line width and color\", plot_width=900,\n",
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" plot_height=400, x_axis_type=\"datetime\", tooltips=TOOLTIPS)\n"
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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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"# step-4 helper function to return dataframes.\n",
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"def prepare_data():\n",
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" microsoft_stock = pd.read_csv(DATA_PATH / \"microsoft_stock_ex6.csv\")\n",
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" microsoft_stock[\"date\"] = pd.to_datetime(microsoft_stock[\"date\"])\n",
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" google_stock = pd.read_csv(DATA_PATH / \"google_stock_ex6.csv\")\n",
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" google_stock[\"date\"] = pd.to_datetime(google_stock[\"date\"])\n",
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" \n",
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" return microsoft_stock, google_stock\n"
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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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"# step-5 call the helper function to get the dataframes\n",
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"microsoft_stock, google_stock = prepare_data()"
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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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"# step-6 Add the lines for both dataframes\n",
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"microsoft_line=p.line(\"date\",\"close\", source=microsoft_stock, line_width=1.5, legend=\"microsoft_stock\")\n",
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"google_line = p.line(\"date\", \"close\", source=google_stock, line_width=1.5, legend=\"google_stock\")"
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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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"#custom function define how to interact for user event.\n",
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"def update(color, width=1):\n",
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" google_line.glyph.line_color = color\n",
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" google_line.glyph.line_width = width\n",
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" push_notebook()"
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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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"#step-7 plot the required libraries\n",
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"interact(update, color=[\"red\", \"blue\", \"gray\"], width=(1,5))\n",
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"show(p, notebook_handle=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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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