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notes.2023-07-12.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "a3d481af",
"metadata": {},
"source": [
"# How do I even get started with Data Visualization?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a94b3c78",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display\n",
"\n",
"display(\"Let's Get Started\")"
]
},
{
"cell_type": "markdown",
"id": "547ba3f5",
"metadata": {},
"source": [
"## Load Data Files\n",
"\n",
"*This is optional if you are **not** using Google Colab*\n",
"\n",
"Execute these cells to access the `data` for this workshop and install some additional packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c02aee51",
"metadata": {},
"outputs": [],
"source": [
"!git clone https://github.com/dutc-io/agu-data-viz.git"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b34f338e",
"metadata": {},
"outputs": [],
"source": [
"%cd agu-data-viz"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26274060",
"metadata": {},
"outputs": [],
"source": [
"!pip install ipyvizzu==0.15.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3f7b766",
"metadata": {},
"outputs": [],
"source": [
"! [[ -e data/anscombe.csv ]] && echo \"Data files loaded!\""
]
},
{
"cell_type": "markdown",
"id": "afed0baf",
"metadata": {},
"source": [
"## Types of Visualizations\n",
"- Exploratory\n",
"- Communicative\n",
"- Fun!\n",
"\n",
"## Types of Data Visualization Tools\n",
"- Non-programmatic\n",
" - low : hand/computer drawing\n",
" - high: GUI chart builders* (excel, tableau, ...)\n",
"- Programmatic\n",
" - low : programmatic drawing\n",
" - high: declarative & convenience"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "239cb123",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display\n",
"from pandas import read_csv\n",
"\n",
"anscombe = read_csv('data/anscombe.csv')\n",
"\n",
"display(\n",
" # anscombe.head(),\n",
" anscombe.groupby('id')[['x', 'y']].agg(['mean', 'std']),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "36660b4e",
"metadata": {},
"source": [
"## Common Data Visualization APIs\n",
"\n",
"### Drawing (non-declarative)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7627320f",
"metadata": {},
"outputs": [],
"source": [
"from pandas import read_csv\n",
"from matplotlib.pyplot import subplots, show\n",
"\n",
"anscombe = read_csv('data/anscombe.csv')\n",
"\n",
"fig, axes = subplots(nrows=2, ncols=2)\n",
"\n",
"for (label, group), ax in zip(anscombe.groupby('id'), axes.flat):\n",
" ax.scatter(group['x'], group['y'], s=24)\n",
" ax.set_title(label)\n",
"\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "a7ecdc22",
"metadata": {},
"source": [
"### High Level - Declarative"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bec53cfb",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.pyplot import show\n",
"from plotnine import ggplot, facet_wrap, geom_point, geom_smooth, labs, theme_minimal, aes\n",
"from pandas import read_csv\n",
"\n",
"anscombe = read_csv('data/anscombe.csv')\n",
"\n",
"(\n",
" ggplot(anscombe, aes(x='x', y='y'))\n",
" + facet_wrap('id', ncol=2)\n",
" + geom_point()\n",
" + geom_smooth(method='ols')\n",
" + labs(x='x variable', y='y variable', title='Anscombe’s Quartet')\n",
" + theme_minimal()\n",
").draw()\n",
"\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "c4d1a578",
"metadata": {},
"source": [
"### High Level - Convenience"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a67b81e",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.pyplot import show\n",
"from seaborn import lmplot\n",
"from pandas import read_csv\n",
"\n",
"anscombe = read_csv('data/anscombe.csv')\n",
"\n",
"lmplot(anscombe, x='x', y='y', col='id', col_wrap=2)\n",
"\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d7ee168",
"metadata": {},
"outputs": [],
"source": [
"from seaborn.objects import Plot, Dots, PolyFit, Line\n",
"from pandas import read_csv\n",
"\n",
"anscombe = read_csv('data/anscombe.csv')\n",
"\n",
"\n",
"(\n",
" Plot(anscombe, x='x', y='y')\n",
" .facet(col='id', wrap=2)\n",
" .add(Dots(color='black'))\n",
" .add(Line(), PolyFit(1))\n",
").show()"
]
},
{
"cell_type": "markdown",
"id": "3012bbe4",
"metadata": {},
"source": [
"### Is it worth it to learn multiple data visualization libraries/languages?\n",
"\n",
"## Let’s Make Some Viz!\n",
"\n",
"### Static Data Visualizations (explore, communicative, fun)\n",
"\n",
"**matplotlib**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15be4264",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.pyplot import figure, show\n",
"from matplotlib.patches import Circle, Rectangle\n",
"\n",
"fig = figure(figsize=(6,6))\n",
"\n",
"c = Circle((.5, .8), .1)\n",
"fig.add_artist(c)\n",
"\n",
"## uncomment below if running from Jupyter Notebook/Google Colab\n",
"# ax = fig.add_axes([0, 0, 0, 0])\n",
"# ax.set_visible(False)\n",
"\n",
"body_rect = Rectangle((.47, .75), .06, -.5)\n",
"fig.add_artist(body_rect)\n",
"\n",
"arms_rect = Rectangle((.3, .55), .4, .05)\n",
"fig.add_artist(arms_rect)\n",
"\n",
"lleg_rect = Rectangle((.5, .3), .3, .05, angle=225)\n",
"fig.add_artist(lleg_rect)\n",
"\n",
"rleg_rect = Rectangle((.47, .26), .3, .05, angle=-45)\n",
"fig.add_artist(rleg_rect)\n",
"\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "38a60299",
"metadata": {},
"source": [
"**Useful Things to draw for Data Viz**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82d8ea89",
"metadata": {},
"outputs": [],
"source": [
"from numpy import linspace, pi, sin, cos\n",
"from matplotlib.pyplot import figure, show, plot, rc\n",
"\n",
"rc('font', size=16)\n",
"\n",
"xs = linspace(0, 2 * pi)\n",
"\n",
"fig = figure()\n",
"ax = fig.add_axes([.3, .3, .5, .5])\n",
"\n",
"# ax.plot(xs, sin(xs))\n",
"# ax.plot(xs, cos(xs))\n",
"\n",
"# ax.set_ylabel('this is my y label')\n",
"# fig.supylabel('this my figure y label')\n",
"\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77e502c3",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display\n",
"\n",
"from numpy import linspace, pi, sin, cos\n",
"from matplotlib.pyplot import subplots, show\n",
"\n",
"# fig, ax = subplots()\n",
"\n",
"# fig, axes = subplots(nrows=1, ncols=2)\n",
"fig, axes = subplots(nrows=2, ncols=2)\n",
"# print(axes[:, 0])\n",
"\n",
"xs = linspace(0, 2 * pi)\n",
"\n",
"# display(axes)\n",
"# display(type(axes))\n",
"\n",
"axes[1, 0].plot(xs, sin(xs))\n",
"\n",
"# display(axes, type(axes), axes[0])\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "ae0d8aa0",
"metadata": {},
"source": [
"Matplotlib is object oriented\n",
"- Containers → Artists\n",
"- Figure → Axes →\n",
" - X/YAxis\n",
" - ticks\n",
" - ticklabels\n",
" - axis label\n",
" - Primitives\n",
" - Patches (Circle, Rectangle)\n",
" - Line2d\n",
" - Annotations/Text\n",
" - ...\n",
" - Legend\n",
" - Primitives\n",
" - Text (label & title)\n",
"\n",
"- Coordinate Spaces: values → ... → screen\n",
" - Proportional coordinate space (Figure & Axes)\n",
" - Data coordinate space (Axes)\n",
" - Identity/point space (Figure)\n",
"\n",
"**Applied to Star Trader Data - Tracking Ship Failures**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "994c936e",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"from matplotlib.pyplot import subplots, show\n",
"from pandas import read_csv, to_datetime\n",
"\n",
"df = (\n",
" read_csv(\n",
" Path('data') / 'failures.csv',\n",
" index_col=['date', 'player','ship'],\n",
" parse_dates=['date'],\n",
" )\n",
" .sort_index()\n",
")\n",
"\n",
"plot_data = (\n",
" df.pivot_table(index='date', columns='player', values='faults', aggfunc='sum')\n",
" .rolling('90D').mean()\n",
")\n",
"\n",
"ax = plot_data.plot(legend=False)\n",
"for line in ax.lines:\n",
" x, y = line.get_data()\n",
" ax.annotate(\n",
" line.get_label(), xy=(x[-1], y[-1]),\n",
" xytext=(5, 0), textcoords='offset points',\n",
" color=line.get_color()\n",
" )\n",
"\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "e898e431",
"metadata": {},
"source": [
"### Interactive Data Visualizations (explore, fun)\n",
"\n",
"- System/function Exploration\n",
"- Data Exploration\n",
"- System Observability\n",
"\n",
"*Limited Communicatve Ability unless STRONGLY guided*\n",
"\n",
"**bokeh & panel** - a powerful way to share your data on the web!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc30971b",
"metadata": {},
"outputs": [],
"source": [
"from panel import Column, extension\n",
"from bokeh.plotting import figure\n",
"from bokeh.models import ColumnDataSource\n",
"\n",
"from numpy import linspace, zeros\n",
"from scipy.stats import skewnorm\n",
"\n",
"# Connect `panel` application to notebook runtime\n",
"extension()\n",
"\n",
"loc = 5\n",
"scale = 1\n",
"skew = 0\n",
"\n",
"cds = ColumnDataSource({\n",
" 'x': linspace(-10, 10, 500),\n",
" 'y1': zeros(shape=500),\n",
"})\n",
"cds.data['y2'] = skewnorm(loc=loc, scale=scale, a=skew).pdf(cds.data['x'])\n",
"\n",
"def update_plot(loc, scale, skew):\n",
" cds.data['y2'] = skewnorm.pdf(x=cds.data['x'], a=skew, loc=loc, scale=scale)\n",
"\n",
"p = figure(y_range=(0, .5), width=500, height=300)\n",
"p.varea(x='x', y1='y1', y2='y2', source=cds, alpha=.3)\n",
"p.line(x='x', y='y2', source=cds, line_width=4)\n",
"p.yaxis.major_label_text_font_size = \"20pt\"\n",
"p.xaxis.major_label_text_font_size = \"20pt\"\n",
"\n",
"Column(p).servable()"
]
},
{
"cell_type": "markdown",
"id": "b2a6b3d8",
"metadata": {},
"source": [
"adding interactivity"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e69af49d",
"metadata": {},
"outputs": [],
"source": [
"from panel import Column, bind, extension\n",
"from panel.widgets import FloatSlider\n",
"from bokeh.plotting import figure\n",
"from bokeh.models import ColumnDataSource\n",
"\n",
"from numpy import linspace, zeros\n",
"from scipy.stats import skewnorm\n",
"\n",
"# Increase font size of widgets\n",
"css = '''\n",
".bk-root .bk, .bk-root .bk:before, .bk-root .bk:after {\n",
" font-size: 110%;\n",
" }\n",
"'''\n",
"extension(raw_css=[css])\n",
"\n",
"loc = FloatSlider(name='mean', value=0, start=-10, end=10)\n",
"scale = FloatSlider(name='std. dev', value=1, start=.1, end=10)\n",
"skew = FloatSlider(name='skew', value=0, start=-6, end=6)\n",
"\n",
"cds = ColumnDataSource({\n",
" 'x': linspace(-10, 10, 500),\n",
" 'y1': zeros(shape=500),\n",
" 'y2': zeros(shape=500),\n",
"})\n",
"\n",
"def update_plot(loc, scale, skew):\n",
" cds.data['y2'] = skewnorm.pdf(x=cds.data['x'], a=skew, loc=loc, scale=scale)\n",
"\n",
"p = figure(y_range=(0, .5), width=500, height=300)\n",
"p.varea(x='x', y1='y1', y2='y2', source=cds, alpha=.3)\n",
"p.line(x='x', y='y2', source=cds, line_width=4)\n",
"p.yaxis.major_label_text_font_size = \"20pt\"\n",
"p.xaxis.major_label_text_font_size = \"20pt\"\n",
"\n",
"Column(\n",
" Column(loc, scale, skew),\n",
" p,\n",
" bind(update_plot, skew=skew, loc=loc, scale=scale)\n",
").servable()"
]
},
{
"cell_type": "markdown",
"id": "8612871a",
"metadata": {},
"source": [
"**Applied to our Star Trader Data - Planetary Weather**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad9e3dd7",
"metadata": {},
"outputs": [],
"source": [
"from pandas import read_csv\n",
"\n",
"df = (\n",
" read_csv(\n",
" 'data/weather_york.csv',\n",
" usecols=['date', 'temperature_max', 'temperature_min'],\n",
" parse_dates=['date'],\n",
" index_col='date'\n",
" )\n",
").loc['1990':'2000']\n",
"\n",
"# Long timeseries Zoom\n",
"from bokeh.plotting import figure, ColumnDataSource\n",
"from panel import Column\n",
"from pandas import to_datetime, DateOffset\n",
"\n",
"cds = ColumnDataSource(df)\n",
"p = figure(\n",
" width=1000, height=250, x_axis_type='datetime', y_range=[0, 110],\n",
" x_range=[df.index.min(), df.index.min() + DateOffset(years=1, days=-1)],\n",
")\n",
"p.vbar(x='date', bottom='temperature_min', top='temperature_max', source=cds, width=(24 * 60 * 60 * 1000))\n",
"\n",
"range_p = figure(\n",
" width=1000, height=p.height // 4, x_axis_type='datetime', y_range=[0, 110],\n",
" x_range=[df.index.min(), df.index.max()],\n",
")\n",
"range_p.vbar(x='date', bottom='temperature_min', top='temperature_max', source=cds, width=(24 * 60 * 60 * 1000))\n",
"\n",
"from bokeh.models import RangeTool\n",
"\n",
"rangetool = RangeTool(x_range=p.x_range)\n",
"range_p.add_tools(rangetool)\n",
"\n",
"Column(p, range_p).servable()"
]
},
{
"cell_type": "markdown",
"id": "a4cf5939",
"metadata": {},
"source": [
"### Animated Data Visualizations (communicative, fun)\n",
"\n",
"**ipyvizzu**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29df05f7",
"metadata": {},
"outputs": [],
"source": [
"from panel.pane import Markdown, HTML\n",
"from pandas import read_csv, MultiIndex\n",
"from ipyvizzu import Chart, Data, Config, Style, DisplayTarget\n",
"\n",
"countries = (\n",
" read_csv('data/dictionary.csv')\n",
" .set_index('Code')['Country']\n",
")\n",
"countries['URS'] = 'Soviet Union'\n",
"\n",
"medals_count = (\n",
" read_csv('data/summer.csv')\n",
" .drop_duplicates(['Year', 'Country', 'Event', 'Medal'])\n",
" .groupby(['Year', 'Country']).size()\n",
" .rename('Total Medals')\n",
")\n",
"\n",
"medals_count = (\n",
" medals_count.reindex(\n",
" MultiIndex.from_product(\n",
" medals_count.index.levels),\n",
" fill_value=0\n",
" )\n",
" .reset_index()\n",
" .astype({'Year': str})\n",
" .assign(Country=lambda d: d['Country'].map(countries))\n",
")\n",
"\n",
"medals_count['Cumulative Medals'] = medals_count.groupby(['Country'])['Total Medals'].cumsum()\n",
"\n",
"countries_min = (\n",
" medals_count.groupby('Country')\n",
" ['Total Medals'].sum()\n",
" .gt(80)\n",
" .loc[lambda s: s].index\n",
")\n",
"\n",
"data = Data()\n",
"data.add_data_frame(medals_count)\n",
"\n",
"config = {\n",
"\t'y': 'Country',\n",
"\t'x': 'Total Medals',\n",
" 'sort': 'byValue'\n",
"}\n",
"\n",
"style = Style(\n",
" {'plot': {'paddingTop': 40, 'paddingLeft': 150}}\n",
")\n",
"\n",
"chart = Chart(\n",
" width=\"800px\", height=\"600px\",\n",
" display=DisplayTarget.MANUAL\n",
")\n",
"chart.on('logo-draw', 'event.preventDefault();')\n",
"chart.animate(\n",
" data,\n",
" style,\n",
" Config(config | {'title': 'United States Leads Summer Olympic Medals'}),\n",
")\n",
"\n",
"filt = '||'.join(\n",
" f\"record.Country == '{c}'\"\n",
" for c in countries_min\n",
")\n",
"chart.animate(\n",
" Config({\n",
" 'title': 'Countries Winning > 80 Summer Olympic Medals',\n",
" }),\n",
" Data.filter(filt),\n",
" delay=2,\n",
"\tduration=4,\n",
")\n",
"\n",
"for i, (year, group) in enumerate(medals_count.groupby('Year')):\n",
" title = 'Summer Olympic Medals 1896'\n",
" if year != '1896':\n",
" title += f' - {year}'\n",
" chart.animate(\n",
" Data.filter(\n",
" f'record.Year == {year} && ({filt})'\n",
" ),\n",
" Config(\n",
" config |\n",
" {'title': title, 'x': 'Cumulative Medals'}\n",
" ),\n",
"\t\tdelay=4 if i == 0 else 0,\n",
" duration=1,\n",
" x={\"easing\": \"linear\", \"delay\": 0},\n",
" y={\"delay\": 0},\n",
" show={\"delay\": 0},\n",
" hide={\"delay\": 0},\n",
" title={\"duration\": 0, \"delay\": 0},\n",
" )\n",
"\n",
"# # Zoom Out\n",
"chart.animate(\n",
" Data.filter(None),\n",
" Config({\n",
" 'title': 'Summer Olympic Medals up to 2012',\n",
" 'x': 'Total Medals',\n",
" }),\n",
" duration=3\n",
")\n",
"\n",
"chart.animate(\n",
" Data.filter('''\n",
" record.Country == 'United States'\n",
" || record.Country == 'United Kingdom'\n",
" || record.Country == 'France'\n",
" || record.Country == 'Italy'\n",
" '''),\n",
" Config({'title': 'Select Countries'}),\n",
")\n",
"\n",
"HTML(chart).servable()"
]
},
{
"cell_type": "markdown",
"id": "5be53380",
"metadata": {},
"source": [
"**Applied to Star Trader Data - **"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43cfbc85",
"metadata": {},
"outputs": [],
"source": [
"from panel.pane import Markdown, HTML\n",
"from pandas import read_csv\n",
"from ipyvizzu import Chart, Data, Config, Style, DisplayTarget\n",
"\n",
"df = (\n",
" read_csv(\n",
" 'data/weather_york.csv',\n",
" usecols=['date', 'temperature_max', 'temperature_min'],\n",
" parse_dates=['date'],\n",
" index_col='date'\n",
" )\n",
" .assign(\n",
" year=lambda d: d.index.year,\n",
" doy=lambda d: d.index.dayofyear.astype(str),\n",
" )\n",
" .sort_index()\n",
").loc['1990':'2000']\n",
"\n",
"# Animate the annual weather curve\n",
"\n",
"HTML(chart).servable()"
]
},
{
"cell_type": "markdown",
"id": "06f02b7c",
"metadata": {},
"source": [
"**Animation as Automated Interactivity**\n",
"- Drill Down : provides context\n",
"- Transformation: insight to dynamic metrics\n",
"- Movement : pre-attentive cues to draw eyes\n",
"\n",
"## Your Turn…\n",
"- Take any of the tools we have discussed today and make 1 static, 1 interactive (web), or 1 animated\n",
"- Remember before you start, think about what you want to create? Something exploratory, communicative?\n",
"\n",
"**Suggested Starting Points**\n",
"1. *static* Using all `data/weather_*.csv` datasets\n",
"\t- Visualize the average yearly `temperature_max` and `temperature_min` for a single planet (york, sol, kirk, …).\n",
"\t- Visualize the average yearly `temperature_max` and `temperature_min` for ALL planet (york, sol, kirk, …)\n",
"\t\t- Prioritize the comparison of the stars within a given year.\n",
"\t\t- Think: should the values be overlaid onto a single chart? Or spread across multiple?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9abb7baf",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from pandas import read_csv\n",
"\n",
"# data_path = Path('data')\n",
"# for p in data_path.glob('weather*'):\n",
"# print(p)\n",
"\n",
"df = (\n",
" read_csv(\n",
" 'data/weather_york.csv',\n",
" usecols=['date', 'temperature_max', 'temperature_min'],\n",
" parse_dates=['date'],\n",
" index_col='date'\n",
" )\n",
").loc['2000']\n",
"\n",
"plot_df = (\n",
" df\n",
" # df.resample('Y').mean()\n",
")\n",
"\n",
"from matplotlib.pyplot import subplots, show\n",
"\n",
"fig, ax = subplots()\n",
"# ax.plot(plot_df.index.year, plot_df['temperature_max'])\n",
"# ax.plot(plot_df.index.year, plot_df['temperature_min'])\n",
"ax.bar(\n",
" plot_df.index,\n",
" bottom=plot_df['temperature_min'],\n",
" height=plot_df['temperature_max'] - plot_df['temperature_min'],\n",
" width=1\n",
")\n",
"\n",
"# ax.set_title('Yearly Average in Sol', size='xx-large', loc='left')\n",
"ax.set_title('2000s Temperature in Sol')\n",
"ax.set_ylabel('Temperature (°F)', size='large')\n",
"ax.spines[['top', 'right']].set_visible(False)\n",
"\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "252a7dba",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from pandas import read_csv\n",
"from matplotlib.pyplot import subplots, show\n",
"\n",
"data_path = Path('data')\n",
"dfs = {}\n",
"for p in data_path.glob('weather*'):\n",
" dfs[p.stem] = (\n",
" read_csv(\n",
" 'data/weather_york.csv',\n",
" usecols=['date', 'temperature_max', 'temperature_min'],\n",
" parse_dates=['date'],\n",
" index_col='date'\n",
" )\n",
" ).loc['2000']\n",
"\n",
"fig, axes = subplots(2, 2, sharex=True, sharey=True)\n",
"\n",
"for (fname, df), ax in zip(dfs.items(), axes.flat):\n",
" planet_name = fname.split('_')[1]\n",
" ax.bar(\n",
" df.index,\n",
" bottom=df['temperature_min'],\n",
" height=df['temperature_max'] - df['temperature_min'],\n",
" width=1\n",
" )\n",
" ax.set_title(planet_name.title(), loc='left')\n",
"\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "f13007c4",
"metadata": {},
"source": [
"2. *interactive* Using the `data/failures.csv` dataset,\n",
"\t- Plot the total failures for each 'player' for each day.\n",
"\t\t- Apply a smoothing factor (`rolling average`) of 90 days prior to plotting the data.\n",
"\t- Create a slider widget that control the number of days involved in the smoothing.\n",
"\t\t- e.g. this slider should allow me to apply 0 days of smoothing all the way up to 90 days of smoothing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d8bdc12",
"metadata": {},
"outputs": [],
"source": [
"# …"
]
},
{
"cell_type": "markdown",
"id": "82d72557",
"metadata": {},
"source": [
"3. *animated* Using the `data/weather_*.csv` datasets\n",
"\t- Plot ALL of the 'temperature_max' data for EACH of the planets.\n",
"\t- Animate: drill-down to the planet with the HIGHEST average temperature (across all datapoints)\n",
"\t- Animate: reintroduce the other planet’s 'temperature_max' to the chart while maintaining the year 2010 zoom."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "521e65b3",
"metadata": {},
"outputs": [],
"source": [
"# …"
]
},
{
"cell_type": "markdown",
"id": "6696cac8",
"metadata": {},
"source": [
"**Can’t Get Started With The Above?** - try recreating examples from the documentation for these tools\n",
"(see **Useful Links** below), or follow along with a tutorial for a tool of your choice!\n",
"\n",
"## Useful Links\n",
"\n",
"**Matplotlib**\n",
"- Tutorial: https://matplotlib.org/stable/tutorials/index.html\n",
"- Cheatsheets: https://matplotlib.org/cheatsheets/\n",
"- Examples: https://matplotlib.org/stable/gallery/index.html\n",
"\n",
"**Plotnine**\n",
"- Tutorial: http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html (note that plotnine does not have official tutorials, so please refer to ggplot2)\n",
"- Examples: https://plotnine.readthedocs.io/en/stable/gallery.html#\n",
"\n",
"**Bokeh**\n",
"- Tutorial: https://docs.bokeh.org/en/latest/docs/first_steps.html#first-steps\n",
"- Examples: https://docs.bokeh.org/en/latest/docs/gallery.html#gallery\n",
"\n",
"**IPyvizzu**\n",
"- Tutoriall: https://ipyvizzu.vizzuhq.com/latest/tutorial/\n",
"- Examples: https://ipyvizzu.vizzuhq.com/latest/examples/analytical_operations/\n",
"\n"
]
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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