<jupyter_start><jupyter_text>Datasets for projects and exploration<jupyter_code># Import Numpy and Datascience modules.
import numpy as np
from datascience import *

# Plotting modules
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import warnings
warnings.simplefilter('ignore', UserWarning)<jupyter_output><empty_output><jupyter_text>Quality of life by countrySee: https://worldpopulationreview.com/country-rankings/standard-of-living-by-country<jupyter_code>url = "https://raw.githubusercontent.com/DataScienceTempleFirst/code-cod/main/countries_quality_of_life_index.18-10-2021.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Inventory of Historic Religious Properties in PhiladelphiaSee: http://www.preservationalliance.com/inventory-historic-religious-properties-philadelphia<jupyter_code>url= "https://raw.githubusercontent.com/DataScienceTempleFirst/code-cod/main/Inventory%20of%20Historic%20Religious%20Properties%20in%20Philadelphia.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Philadelphia Open Data School Graduation RatesThis longitudinal open data file includes information about the graduation rates for schools broken out by: graduation rate type (four-year, five-year, or six-year), demographic category (EL status, IEP status, Economically Disadvantaged Status, Gender, or Ethnicity), and ninth-grade cohort. Students are attributed to the last school at which they actively attended in the respective graduation window, which ends on September 30 each year. Students are classified as EL, as having an IEP, and/or economically disadvantaged if they were designated as such at any point during their high school career.see: https://www.philasd.org/performance/programsservices/open-data/school-performance/school_graduation_rates<jupyter_code>url = "https://cdn.philasd.org/offices/performance/Open_Data/School_Performance/Graduation_Rates/SDP_Graduation_Rates_School_S.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Philadelphia vaccination rates by zip codeCOVID-19 VaccinationsShows distribution counts of first and second dose, as well as total dose information for all vaccinations performed by the health department. Also provides vaccinations by census tract, ZIP code, age, race, and sex. Vaccinations include residents and non-residents of Philadelphia. Updates daily.See: https://www.opendataphilly.org/dataset/covid-vaccinations/resource/87ac5b4e-8491-41e3-8cf0-5bfebba2e3a0<jupyter_code>url = "https://phl.carto.com/api/v2/sql?filename=covid_vaccines_by_zip&format=csv&skipfields=cartodb_id,the_geom,the_geom_webmercator&q=SELECT%20*%20FROM%20covid_vaccines_by_zip"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Pennsylvannia population by zip code<jupyter_code>url = "https://raw.githubusercontent.com/DataScienceTempleFirst/code-cod/main/PA_zip_pop.csv"
data = Table.read_table(url)
data.sort("pop",descending=True)
data.where('county','Philadelphia')<jupyter_output><empty_output><jupyter_text>COVID Vaccination data by country<jupyter_code>url = "https://raw.githubusercontent.com/DataScienceTempleFirst/code-cod/main/COVID_VAXDATA.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>City Employee Earnings (Philadelphia)This data does not necessarily represent current salaries of employees and is intended for informational purposes only. Formal requests to document salary details or other personnel information should be made through the City's Human Resources department.https://www.opendataphilly.org/dataset/employee-salaries-overtime<jupyter_code>url = "https://phl.carto.com/api/v2/sql?filename=employee_earnings&format=csv&q=SELECT%20*%20FROM%20employee_earnings"
data = Table.read_table(url)
data.sort('base_salary',descending=False)<jupyter_output><empty_output><jupyter_text>Farmers Markets LocationsLocations of farmers markets in Philadelphia.Trouble downloading or have questions about this City dataset? Visit the OpenDataPhilly Discussion Group<jupyter_code>url = "https://opendata.arcgis.com/datasets/0707c1f31e2446e881d680b0a5ee54bc_0.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Map Farmer's market dataSee Inferential Thinking: https://inferentialthinking.com/chapters/08/5/Bike_Sharing_in_the_Bay_Area.html?highlight=map<jupyter_code>data = data.relabeled('X','long').relabeled('Y','lat')
FM_Philly = data.select('lat', 'long', 'NAME').relabel('NAME', 'labels')
Circle.map_table(FM_Philly, color='green')<jupyter_output><empty_output><jupyter_text>Palmer PenguinsSee: https://allisonhorst.github.io/palmerpenguins/<jupyter_code>url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Jeopardysee: https://www.jeopardy.com<jupyter_code>contestant = "https://raw.githubusercontent.com/anuparna/jeopardy/master/dataset/contestants.csv"
locations =  "https://raw.githubusercontent.com/anuparna/jeopardy/master/dataset/locations.csv"
results =  "https://raw.githubusercontent.com/anuparna/jeopardy/master/dataset/final_results.csv"
loc = Table.read_table(locations)
contest = Table.read_table(contestant)
outcome = Table.read_table(results)
loc
contest
games = loc.join("player_id",contest)
games<jupyter_output><empty_output><jupyter_text>Exoplanets observed by Kepler telescope<jupyter_code>url = "https://raw.githubusercontent.com/DataScienceTempleFirst/code-cod/main/kepler.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output><jupyter_text>Molecular acid dissociation constant, pKa dataSee: https://github.com/samplchallenges/SAMPL7/tree/master/physical_property/pKaSMILES is a representaion of chemical structure.<jupyter_code>url = "https://raw.githubusercontent.com/robraddi/GP-SAMPL7/main/pKaDatabase/OChem/ochem0-2000.csv"
data = Table.read_table(url)
data<jupyter_output><empty_output>
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