Tables to Idioms

Acknowledgement

Slides created by and for: Munzner, T. (2014). Visualization analysis and design. CRC press. [Mun14]

Used by permission of the author.

Image from: Wickham, H. (2010). A layered grammar of graphics. Journal of Computational and Graphical Statistics, 19(1), 3-28. [Wic10]

[Goo]

URL: https://colab.research.google.com/notebooks/io.ipynb.

[GG16]

B Granger and J Grout. Jupyterlab: building blocks for interactive computing. Slides of presentation made at SciPy, 2016.

[Mun14]

Tamara Munzner. Visualization analysis and design. CRC press, 2014.

[Wic10]

Hadley Wickham. A layered grammar of graphics. Journal of Computational and Graphical Statistics, 19(1):3–28, 2010.

First the Data

import matplotlib.pyplot as plt
import pandas as pd

url="https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/us_state_vaccinations.csv"
data = []
data=pd.read_csv(url)
data = data.dropna() # removed NAs with 0s

### Remove the United States entries

data = data[data.location != "United States"]

data.total_vaccinations = data.total_vaccinations/100000
data.total_distributed = data.total_distributed/100000
data.people_vaccinated = data.people_vaccinated/100000

data.head()

	date	location	total_vaccinations	total_distributed	people_vaccinated	people_fully_vaccinated_per_hundred	total_vaccinations_per_hundred	people_fully_vaccinated	people_vaccinated_per_hundred	distributed_per_hundred	daily_vaccinations_raw	daily_vaccinations	daily_vaccinations_per_million	share_doses_used	total_boosters	total_boosters_per_hundred
263	2021-10-02	Alabama	45.44355	67.3828	25.86950	42.73	92.68	2094920.0	52.76	137.43	9583.0	10431.0	2127.0	0.674	2985.0	0.06
264	2021-10-03	Alabama	45.65230	67.3665	25.92096	42.89	93.11	2103203.0	52.87	137.39	20875.0	11389.0	2323.0	0.678	10683.0	0.22
265	2021-10-04	Alabama	45.79477	67.3623	25.95947	43.02	93.40	2109349.0	52.94	137.38	14247.0	12178.0	2484.0	0.680	14974.0	0.31
266	2021-10-05	Alabama	45.84378	67.4281	25.97448	43.07	93.50	2111925.0	52.97	137.52	4901.0	10470.0	2135.0	0.680	15821.0	0.32
267	2021-10-06	Alabama	45.94736	67.5796	26.00070	43.15	93.71	2115732.0	53.03	137.83	10358.0	11500.0	2345.0	0.680	19856.0	0.40

Acknowledgement

Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina and Joe Hasell (2020) - “Coronavirus Pandemic (COVID-19)”. Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/coronavirus’ [Online Resource]

Original Link: owid/covid-19-data (Accessed 3/11/2021) Source Link: owid/covid-19-data

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. IEEE Annals of the History of Computing, 9(03), 90-95.

Today’s and Yesterday’s date and data

from datetime import datetime, timedelta

todaysDate = datetime.today()
yesterdaysDate = todaysDate  - timedelta(days=1)
yesterdaysDate = yesterdaysDate.strftime('%Y-%m-%d')
twoDays = todaysDate - timedelta(days=2)
twoDays = twoDays.strftime('%Y-%m-%d')

data[data.date==yesterdaysDate].head()

	date	location	total_vaccinations	total_distributed	people_vaccinated	people_fully_vaccinated_per_hundred	total_vaccinations_per_hundred	people_fully_vaccinated	people_vaccinated_per_hundred	distributed_per_hundred	daily_vaccinations_raw	daily_vaccinations	daily_vaccinations_per_million	share_doses_used	total_boosters	total_boosters_per_hundred

Scatter Plot — Matplotlib 3.3.4 Documentation. https://matplotlib.org/stable/gallery/shapes_and_collections/scatter.html. Accessed 14 Mar. 2021.

px = 1/plt.rcParams['figure.dpi']  # pixel in inches
fig, ax = plt.subplots(1,figsize=(600*px, 400*px))

ax.scatter(data.total_distributed, data.total_vaccinations)

# Add labels, a title and grid lines to the plot
ax.set_xlabel( 'Total Distributed (x100000)' )
ax.set_ylabel( 'Total Vaccinations (x100000)' )
plt.title( 'Distributed vs. Vaccinated per State' )
ax.grid()

# Show the figure (here in Jupyter)
plt.show( fig )
plt.close( fig )

Grouped Bar Chart with Labels — Matplotlib 3.3.4 Documentation. https://matplotlib.org/stable/gallery/lines_bars_and_markers/barchart.html. Accessed 14 Mar. 2021.

# creating the bar plot
yData = data[data.date==yesterdaysDate]
px = 1/plt.rcParams['figure.dpi']  # pixel in inches
fig, ax = plt.subplots(1,figsize=(1000*px, 300*px))
ax.bar(yData.location, yData.total_vaccinations, color ='maroon', width = 0.4)

# Add labels, a title and grid lines to the plot
ax.set_xlabel( 'Locations' )
ax.set_ylabel( 'Total Vaccinations (x100000)' )
plt.title( 'Vaccinations Yesterday per Location' )
# Show the figure (here in Jupyter)
plt.show( fig )
plt.close( fig )

# creating the bar plot
top10 = ["California","Texas","Florida","New York State","Illinois","Pennsylvania","Ohio","Georgia","North Carolina","Michigan"]
yData = data[data.date==yesterdaysDate]

top10Yest = yData[yData.location.isin(top10)]
px = 1/plt.rcParams['figure.dpi']  # pixel in inches
fig, ax = plt.subplots(1,figsize=(1000*px, 300*px))
ax.bar(top10Yest.location, top10Yest.total_vaccinations, color ='maroon', width = 0.4)

# Add labels, a title and grid lines to the plot
ax.set_xlabel( 'Locations' )
ax.set_ylabel( 'Total Vaccinations (x100000)' )
plt.title( 'Vaccinations Yesterday per Location' )
# Show the figure (here in Jupyter)
plt.show( fig )
plt.close( fig )

# creating the bar plot
top10 = ["California","Texas","Florida","New York State","Illinois","Pennsylvania","Ohio","Georgia","North Carolina","Michigan"]
yData = data[data.date==yesterdaysDate]

sortedData = yData.sort_values(by=['total_vaccinations'])
top10Yest = sortedData[sortedData.location.isin(top10)]
px = 1/plt.rcParams['figure.dpi']  # pixel in inches
fig, ax = plt.subplots(1,figsize=(1000*px, 300*px))
ax.bar(top10Yest.location, top10Yest.total_vaccinations, color ='maroon', width = 0.4)

# Add labels, a title and grid lines to the plot
ax.set_xlabel( 'Locations' )
ax.set_ylabel( 'Total Vaccinations (x100000)' )
plt.title( 'Vaccinations Yesterday per Location' )
# Show the figure (here in Jupyter)
plt.show( fig )
plt.close( fig )

Paired Bar Chart

import numpy as np
# creating the bar plot
top10 = ["California","Texas","Florida","New York State","Illinois","Pennsylvania","Ohio","Georgia","North Carolina","Michigan"]
yData = data[data.date==yesterdaysDate]
sortedData = yData.sort_values(by=['total_vaccinations'])
top10Yest = sortedData[sortedData.location.isin(top10)]

twoData = data[data.date==twoDays]
sortedData = twoData.sort_values(by=['total_vaccinations'])
top102days = sortedData[sortedData.location.isin(top10)]

x = np.arange(len(sortedData[sortedData.location.isin(top10)].location))  # the label locations
width = 0.35  # the width of the bars

px = 1/plt.rcParams['figure.dpi']  # pixel in inches
fig, ax = plt.subplots(1,figsize=(1000*px, 300*px))
ax.bar(x - width/2, top102days.total_vaccinations, color ='steelblue', width = 0.4, label="Yesterday")
ax.bar(x + width/2, top10Yest.total_vaccinations, color ='maroon', width = 0.4, label="Two Days")
# Add labels, a title and grid lines to the plot
ax.set_xlabel( 'Locations' )
ax.set_ylabel( 'Total Vaccinations (x100000)' )
ax.set_xticks(x)
ax.set_xticklabels(sortedData[sortedData.location.isin(top10)].location)

ax.legend()

plt.title( 'Vaccinations Yesterday per Location' )
# Show the figure (here in Jupyter)
plt.show( fig )
plt.close( fig )

import numpy as np
# creating the bar plot
top10 = ["California","Texas","Florida","New York State","Illinois","Pennsylvania","Ohio","Georgia","North Carolina","Michigan"]
yData = data[data.date==yesterdaysDate]
sortedData = yData.sort_values(by=['total_vaccinations'])
top10Yest = sortedData[sortedData.location.isin(top10)]

twoData = data[data.date==twoDays]
sortedData = twoData.sort_values(by=['total_vaccinations'])
top102days = sortedData[sortedData.location.isin(top10)]

x = np.arange(len(sortedData[sortedData.location.isin(top10)].location))  # the label locations
width = 0.35  # the width of the bars

px = 1/plt.rcParams['figure.dpi']  # pixel in inches
fig, ax = plt.subplots(1,figsize=(1000*px, 300*px))
rects1 = ax.bar(x - width/2, top102days.total_vaccinations, color ='steelblue', width = 0.4, label="Two Days")
rects2 = ax.bar(x + width/2, top10Yest.total_vaccinations, color ='maroon', width = 0.4, label="Yesterday")
# Add labels, a title and grid lines to the plot
ax.set_xlabel( 'Locations' )
ax.set_ylabel( 'Total Vaccinations' )
ax.set_xticks(x)
ax.set_xticklabels(sortedData[sortedData.location.isin(top10)].location)

ax.legend()

def autolabel(rects):
    """Attach a text label above each bar in *rects*, displaying its height."""
    for rect in rects:
        height = rect.get_height()
        ax.annotate('{:.1f}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')


autolabel(rects1)
autolabel(rects2)


plt.title( 'Vaccinations Yesterday per Location' )
# Show the figure (here in Jupyter)
plt.show( fig )
plt.close( fig )