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Homework4

Usage

Jupyter lab, Library: request, pandas, json, numpy, seaborn

Description

Here we are going to see if there is a correlation between mortality of COVID-19 and number of hospital beds available in the U.S.. We are going to use API to access the data and join the two sets of data to plot.

Instruction

Here we are going to do some exercise on getting Covid-19 data by using API, and we will make a plot with seaborn function within Jupyter notebook at the end.

import requests
import pandas as pd
import json
import numpy as np

req=requests.get("https://api.covid19api.com/live/country/united-states").json()
covid=pd.DataFrame(req)
covid.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24480 entries, 0 to 24479
Data columns (total 13 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   ID           24480 non-null  object
 1   Country      24480 non-null  object
 2   CountryCode  24480 non-null  object
 3   Province     24480 non-null  object
 4   City         24480 non-null  object
 5   CityCode     24480 non-null  object
 6   Lat          24480 non-null  object
 7   Lon          24480 non-null  object
 8   Confirmed    24480 non-null  int64 
 9   Deaths       24480 non-null  int64 
 10  Recovered    24480 non-null  int64 
 11  Active       24480 non-null  int64 
 12  Date         24480 non-null  object
dtypes: int64(4), object(9)
memory usage: 2.4+ MB

covid.describe()
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Confirmed Deaths Recovered Active
count 2.448000e+04 24480.000000 24480.0 2.448000e+04
mean 1.169896e+06 15378.652002 0.0 1.154518e+06
std 1.522009e+06 18818.213672 0.0 1.504000e+06
min 0.000000e+00 0.000000 0.0 0.000000e+00
25% 2.508262e+05 2670.000000 0.0 2.485832e+05
50% 7.225890e+05 9371.000000 0.0 7.122190e+05
75% 1.468140e+06 19855.000000 0.0 1.450549e+06
max 1.116657e+07 95311.000000 0.0 1.107126e+07 
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
sns.relplot(data=covid,x="Lat", y="Lon", hue="Deaths", size="Confirmed", sizes=(40, 400))
<seaborn.axisgrid.FacetGrid at 0x7ff53588d670>

png

Here we have an issue with values on x- and y-axix so we are going to check the value first.

covid['Lat'][0]
'18.35'

Here we are going to make a change data type of "Lat" and "Lon".

covid['Lat'] = covid['Lat'].astype('float16')
covid['Lon'] = covid['Lon'].astype('float16')
covid.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24480 entries, 0 to 24479
Data columns (total 13 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   ID           24480 non-null  object 
 1   Country      24480 non-null  object 
 2   CountryCode  24480 non-null  object 
 3   Province     24480 non-null  object 
 4   City         24480 non-null  object 
 5   CityCode     24480 non-null  object 
 6   Lat          24480 non-null  float16
 7   Lon          24480 non-null  float16
 8   Confirmed    24480 non-null  int64  
 9   Deaths       24480 non-null  int64  
 10  Recovered    24480 non-null  int64  
 11  Active       24480 non-null  int64  
 12  Date         24480 non-null  object 
dtypes: float16(2), int64(4), object(7)
memory usage: 2.1+ MB

Now we can see that the data type of "Lat" and "Lon" has changed. Now we can make the plot correctly.

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
fig=sns.relplot(data=covid,x="Lon", y="Lat", hue="Deaths", size="Confirmed", sizes=(40, 400))

png

fig=sns.relplot(data=covid,x="Lon", y="Lat", hue="Deaths", size="Confirmed", sizes=(40, 400)).set(ylim=(25,50),xlim=(-150,-50))

png

Dataset COVID

Now we are going to get another data from different source. We are going to merge these two data by using pandas, and we are going to make a plot with the merged values in the merged data with seaborn

req=requests.get("https://www.communitybenefitinsight.org/api/get_hospitals.php").json()
hospitals=pd.DataFrame(req)
hospitals.head()
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hospital_id hospital_org_id ein name name_cr street_address city state zip_code fips_state_and_county_code hospital_bed_count chrch_affl_f urban_location_f children_hospital_f memb_counc_teach_hosps_f medicare_provider_number county hospital_bed_size updated_dt
0 1 1 630307951 Mizell Memorial Hospital Mizell Memorial Hospital 702 Main Street Opp AL 36462 01039 99 N N N N 010007 Covington County <100 beds June 15, 2022
1 2 2 630578923 St Vincents East St Vincents East 50 Medical Park Drive East Birmingham AL 35235 01073 362 N Y N Y 010011 Jefferson County >299 beds June 15, 2022
2 3 3 630312913 Shelby Baptist Medical Center Shelby Baptist Medical Center 1000 First Street North Alabaster AL 35007 01117 252 N Y N N 010016 Shelby County 100-299 beds June 15, 2022
3 4 4 630459034 Callahan Eye Foundation Hosp Callahan Eye Foundation Hosp 1720 University Boulevard Birmingham AL 35233 01073 106 N Y N Y 010018 Jefferson County 100-299 beds June 15, 2022
4 5 5 581973570 Cherokee Medical Center Cherokee Medical Center 400 Northwood Drive Centre AL 35960 01019 60 N N N N 010022 Cherokee County <100 beds June 15, 2022

The count is not unique because there are same name all over the states in the U.S.. Therefore, we are going to create a new column called "state_county".

hospitals['state_county']=hospitals['state']+'_'+hospitals['county']
hospitals.head()
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hospital_id hospital_org_id ein name name_cr street_address city state zip_code fips_state_and_county_code hospital_bed_count chrch_affl_f urban_location_f children_hospital_f memb_counc_teach_hosps_f medicare_provider_number county hospital_bed_size updated_dt state_county
0 1 1 630307951 Mizell Memorial Hospital Mizell Memorial Hospital 702 Main Street Opp AL 36462 01039 99 N N N N 010007 Covington County <100 beds June 15, 2022 AL_Covington County
1 2 2 630578923 St Vincents East St Vincents East 50 Medical Park Drive East Birmingham AL 35235 01073 362 N Y N Y 010011 Jefferson County >299 beds June 15, 2022 AL_Jefferson County
2 3 3 630312913 Shelby Baptist Medical Center Shelby Baptist Medical Center 1000 First Street North Alabaster AL 35007 01117 252 N Y N N 010016 Shelby County 100-299 beds June 15, 2022 AL_Shelby County
3 4 4 630459034 Callahan Eye Foundation Hosp Callahan Eye Foundation Hosp 1720 University Boulevard Birmingham AL 35233 01073 106 N Y N Y 010018 Jefferson County 100-299 beds June 15, 2022 AL_Jefferson County
4 5 5 581973570 Cherokee Medical Center Cherokee Medical Center 400 Northwood Drive Centre AL 35960 01019 60 N N N N 010022 Cherokee County <100 beds June 15, 2022 AL_Cherokee County 
HospitalInfoPerCounty=hospitals.groupby(['state_county'], as_index=False)['hospital_bed_count'].agg('sum')
HospitalInfoPerCounty.head()
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state_county hospital_bed_count
0 AK_Anchorage Municipality 401
1 AK_Fairbanks North Star Borough 162
2 AK_Kenai Peninsula Borough 49622
3 AK_Ketchikan Gateway Borough 25
4 AK_Kodiak Island Borough 25

the number we have in AK_Kenai Peninsula Borough is too high comparing to the others. We need to check the data type of it.

hospitals.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3423 entries, 0 to 3422
Data columns (total 20 columns):
 #   Column                      Non-Null Count  Dtype 
---  ------                      --------------  ----- 
 0   hospital_id                 3423 non-null   object
 1   hospital_org_id             3423 non-null   object
 2   ein                         3423 non-null   object
 3   name                        3423 non-null   object
 4   name_cr                     3423 non-null   object
 5   street_address              3423 non-null   object
 6   city                        3423 non-null   object
 7   state                       3423 non-null   object
 8   zip_code                    3423 non-null   object
 9   fips_state_and_county_code  3423 non-null   object
 10  hospital_bed_count          3423 non-null   object
 11  chrch_affl_f                3423 non-null   object
 12  urban_location_f            3423 non-null   object
 13  children_hospital_f         3423 non-null   object
 14  memb_counc_teach_hosps_f    3423 non-null   object
 15  medicare_provider_number    3423 non-null   object
 16  county                      3422 non-null   object
 17  hospital_bed_size           3423 non-null   object
 18  updated_dt                  3423 non-null   object
 19  state_county                3422 non-null   object
dtypes: object(20)
memory usage: 535.0+ KB

Here we are going to make to change its data type to numbers.

hospitals['hospital_bed_count'] = hospitals['hospital_bed_count'].astype('int64')
hospitals.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3423 entries, 0 to 3422
Data columns (total 20 columns):
 #   Column                      Non-Null Count  Dtype 
---  ------                      --------------  ----- 
 0   hospital_id                 3423 non-null   object
 1   hospital_org_id             3423 non-null   object
 2   ein                         3423 non-null   object
 3   name                        3423 non-null   object
 4   name_cr                     3423 non-null   object
 5   street_address              3423 non-null   object
 6   city                        3423 non-null   object
 7   state                       3423 non-null   object
 8   zip_code                    3423 non-null   object
 9   fips_state_and_county_code  3423 non-null   object
 10  hospital_bed_count          3423 non-null   int64 
 11  chrch_affl_f                3423 non-null   object
 12  urban_location_f            3423 non-null   object
 13  children_hospital_f         3423 non-null   object
 14  memb_counc_teach_hosps_f    3423 non-null   object
 15  medicare_provider_number    3423 non-null   object
 16  county                      3422 non-null   object
 17  hospital_bed_size           3423 non-null   object
 18  updated_dt                  3423 non-null   object
 19  state_county                3422 non-null   object
dtypes: int64(1), object(19)
memory usage: 535.0+ KB

We are going to create a data frame that is unique by "state_county", and we are going to sum across entries on "hospital_bed_count".

HospitalInfoPerCounty=hospitals.groupby(['state_county'], as_index=False)['hospital_bed_count'].agg('sum')
HospitalInfoPerCounty.head()
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state_county hospital_bed_count
0 AK_Anchorage Municipality 401
1 AK_Fairbanks North Star Borough 162
2 AK_Kenai Peninsula Borough 77
3 AK_Ketchikan Gateway Borough 25
4 AK_Kodiak Island Borough 25 
HospitalInfoPerCounty['state_county']=HospitalInfoPerCounty['state_county'].str.replace(' County','')
HospitalInfoPerCounty.head(5)
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state_county hospital_bed_count
0 AK_Anchorage Municipality 401
1 AK_Fairbanks North Star Borough 162
2 AK_Kenai Peninsula Borough 77
3 AK_Ketchikan Gateway Borough 25
4 AK_Kodiak Island Borough 25

COVID DATAFRAME

Here we are going to work with Covid data in order to compare with "HospitalInfoPerCounty"

import requests
import pandas as pd
import json
import numpy as np
req=requests.get("https://webhooks.mongodb-stitch.com/api/client/v2.0/app/covid-19-qppza/service/REST-API/incoming_webhook/us_only?min_date=2021-01-15T00:00:00.000Z&max_date=2021-01-15T00:00:00.000Z").json()
covid=pd.DataFrame(req)
covid.head()
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_id uid country_iso2 country_iso3 country_code fips county state country combined_name population loc date confirmed deaths confirmed_daily deaths_daily
0 6335197fb2fc4a05475fedec 84001001 US USA 840 1001.0 Autauga Alabama US Autauga, Alabama, US 55869.0 {'type': 'Point', 'coordinates': [-86.6441, 32... 2021-01-15T00:00:00.000Z 5103 55 28 0
1 6335197fb2fc4a05475ff1c0 84001003 US USA 840 1003.0 Baldwin Alabama US Baldwin, Alabama, US 223234.0 {'type': 'Point', 'coordinates': [-87.7221, 30... 2021-01-15T00:00:00.000Z 16002 179 161 2
2 6335197fb2fc4a05475ff594 84001005 US USA 840 1005.0 Barbour Alabama US Barbour, Alabama, US 24686.0 {'type': 'Point', 'coordinates': [-85.3871, 31... 2021-01-15T00:00:00.000Z 1712 36 16 0
3 6335197fb2fc4a05475ff968 84001007 US USA 840 1007.0 Bibb Alabama US Bibb, Alabama, US 22394.0 {'type': 'Point', 'coordinates': [-87.1251, 32... 2021-01-15T00:00:00.000Z 2130 47 17 0
4 6335197fb2fc4a05475ffd3c 84001009 US USA 840 1009.0 Blount Alabama US Blount, Alabama, US 57826.0 {'type': 'Point', 'coordinates': [-86.5679, 33... 2021-01-15T00:00:00.000Z 5264 83 45 3

We are going to convert full states name under "state" column to two letter code. In order to do that, we are going to make a library and replace states name by using the library. Any state name matches with its key in the library replace to values in the library.

us_state_to_abbrev = {
"Alabama": "AL","Alaska": "AK","Arizona": "AZ","Arkansas": "AR", "California": "CA",
"Colorado": "CO","Connecticut": "CT","Delaware": "DE","Florida": "FL","Georgia": "GA",
"Hawaii": "HI","Idaho": "ID","Illinois": "IL","Indiana": "IN","Iowa": "IA","Kansas": "KS",
"Kentucky": "KY","Louisiana": "LA","Maine": "ME","Maryland": "MD","Massachusetts": "MA",
"Michigan": "MI","Minnesota": "MN","Mississippi": "MS","Missouri": "MO","Montana": "MT",
"Nebraska": "NE","Nevada": "NV","New Hampshire": "NH","New Jersey": "NJ","New Mexico": "NM",
"New York": "NY","North Carolina": "NC","North Dakota": "ND","Ohio": "OH",
"Oklahoma": "OK","Oregon": "OR","Pennsylvania": "PA","Rhode Island": "RI",
"South Carolina": "SC","South Dakota": "SD","Tennessee": "TN","Texas": "TX",
"Utah": "UT","Vermont": "VT","Virginia": "VA","Washington": "WA",
"West Virginia": "WV","Wisconsin": "WI","Wyoming": "WY",
"District of Columbia": "DC","American Samoa": "AS","Guam": "GU", "Northern Mariana Islands": "MP",
"Puerto Rico": "PR","United States Minor Outlying Islands": "UM","U.S. Virgin Islands": "VI",
}
covid['state'].replace(us_state_to_abbrev, inplace=True)
covid.head(5)
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_id uid country_iso2 country_iso3 country_code fips county state country combined_name population loc date confirmed deaths confirmed_daily deaths_daily
0 6335197fb2fc4a05475fedec 84001001 US USA 840 1001.0 Autauga AL US Autauga, Alabama, US 55869.0 {'type': 'Point', 'coordinates': [-86.6441, 32... 2021-01-15T00:00:00.000Z 5103 55 28 0
1 6335197fb2fc4a05475ff1c0 84001003 US USA 840 1003.0 Baldwin AL US Baldwin, Alabama, US 223234.0 {'type': 'Point', 'coordinates': [-87.7221, 30... 2021-01-15T00:00:00.000Z 16002 179 161 2
2 6335197fb2fc4a05475ff594 84001005 US USA 840 1005.0 Barbour AL US Barbour, Alabama, US 24686.0 {'type': 'Point', 'coordinates': [-85.3871, 31... 2021-01-15T00:00:00.000Z 1712 36 16 0
3 6335197fb2fc4a05475ff968 84001007 US USA 840 1007.0 Bibb AL US Bibb, Alabama, US 22394.0 {'type': 'Point', 'coordinates': [-87.1251, 32... 2021-01-15T00:00:00.000Z 2130 47 17 0
4 6335197fb2fc4a05475ffd3c 84001009 US USA 840 1009.0 Blount AL US Blount, Alabama, US 57826.0 {'type': 'Point', 'coordinates': [-86.5679, 33... 2021-01-15T00:00:00.000Z 5264 83 45 3

Here we are going to make a unique value like last time we did with hospital data.

covid['state_county']=covid['state']+'_'+covid['county']
covid.head()
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_id uid country_iso2 country_iso3 country_code fips county state country combined_name population loc date confirmed deaths confirmed_daily deaths_daily state_county
0 6335197fb2fc4a05475fedec 84001001 US USA 840 1001.0 Autauga AL US Autauga, Alabama, US 55869.0 {'type': 'Point', 'coordinates': [-86.6441, 32... 2021-01-15T00:00:00.000Z 5103 55 28 0 AL_Autauga
1 6335197fb2fc4a05475ff1c0 84001003 US USA 840 1003.0 Baldwin AL US Baldwin, Alabama, US 223234.0 {'type': 'Point', 'coordinates': [-87.7221, 30... 2021-01-15T00:00:00.000Z 16002 179 161 2 AL_Baldwin
2 6335197fb2fc4a05475ff594 84001005 US USA 840 1005.0 Barbour AL US Barbour, Alabama, US 24686.0 {'type': 'Point', 'coordinates': [-85.3871, 31... 2021-01-15T00:00:00.000Z 1712 36 16 0 AL_Barbour
3 6335197fb2fc4a05475ff968 84001007 US USA 840 1007.0 Bibb AL US Bibb, Alabama, US 22394.0 {'type': 'Point', 'coordinates': [-87.1251, 32... 2021-01-15T00:00:00.000Z 2130 47 17 0 AL_Bibb
4 6335197fb2fc4a05475ffd3c 84001009 US USA 840 1009.0 Blount AL US Blount, Alabama, US 57826.0 {'type': 'Point', 'coordinates': [-86.5679, 33... 2021-01-15T00:00:00.000Z 5264 83 45 3 AL_Blount

We will create another data frame with columns that we desire to be within it and we are going to sum across entries on those columns.

CovidPerCounty=covid.groupby(['state_county'], as_index=False).agg({'deaths':'sum','confirmed':'sum','population':'sum','confirmed_daily':'mean','deaths_daily':'mean'})
CovidPerCounty.head(15)
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state_county deaths confirmed population confirmed_daily deaths_daily
0 AK_Aleutians East 0 40 3337.0 1.0 0.0
1 AK_Aleutians West 0 242 5634.0 3.0 0.0
2 AK_Anchorage 129 24530 288000.0 92.0 0.0
3 AK_Bethel 12 2911 18386.0 26.0 0.0
4 AK_Bristol Bay 0 0 836.0 0.0 0.0
5 AK_Bristol Bay plus Lake and Peninsula 0 163 2428.0 1.0 0.0
6 AK_Chugach 3 241 6751.0 0.0 0.0
7 AK_Copper River 0 175 2699.0 1.0 0.0
8 AK_Denali 0 63 2097.0 0.0 0.0
9 AK_Dillingham 1 150 4916.0 1.0 0.0
10 AK_Fairbanks North Star 23 5576 96849.0 46.0 1.0
11 AK_Haines 0 23 2530.0 0.0 0.0
12 AK_Hoonah-Angoon 0 0 2148.0 0.0 0.0
13 AK_Juneau 5 1098 31974.0 4.0 0.0
14 AK_Kenai Peninsula 19 3821 58708.0 11.0 0.0

We are going to create a new column "mortality" in CovidPerCounty data frame. We will plot this value onto our final plot to compare with people per bed count value for each of counties.

CovidPerCounty['mortality']=CovidPerCounty['deaths']/CovidPerCounty['confirmed']
CovidPerCounty.head(5)
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state_county deaths confirmed population confirmed_daily deaths_daily mortality
0 AK_Aleutians East 0 40 3337.0 1.0 0.0 0.000000
1 AK_Aleutians West 0 242 5634.0 3.0 0.0 0.000000
2 AK_Anchorage 129 24530 288000.0 92.0 0.0 0.005259
3 AK_Bethel 12 2911 18386.0 26.0 0.0 0.004122
4 AK_Bristol Bay 0 0 836.0 0.0 0.0 NaN

Here we are going to merge two data frame and take out the values which can make interference with statistics.

Hospital_Covid_PerCounty=pd.merge(HospitalInfoPerCounty, CovidPerCounty, on='state_county')
Hospital_Covid_PerCounty=Hospital_Covid_PerCounty[Hospital_Covid_PerCounty['population']>50000]
Hospital_Covid_PerCounty=Hospital_Covid_PerCounty[Hospital_Covid_PerCounty['population']<300000]
Hospital_Covid_PerCounty=Hospital_Covid_PerCounty[Hospital_Covid_PerCounty['confirmed']>10]
Hospital_Covid_PerCounty.head()
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state_county hospital_bed_count deaths confirmed population confirmed_daily deaths_daily mortality
0 AL_Baldwin 240 179 16002 223234.0 161.0 2.0 0.011186
1 AL_Blount 65 83 5264 57826.0 45.0 3.0 0.015767
2 AL_Calhoun 552 200 10982 113605.0 119.0 3.0 0.018212
8 AL_Cullman 115 104 7960 83768.0 38.0 10.0 0.013065
9 AL_Elmore 69 101 7723 81209.0 53.0 0.0 0.013078

In merged data, we are going to create a new column "people_per_bed" in order to compare with mortality.

Hospital_Covid_PerCounty['people_per_bed']=Hospital_Covid_PerCounty['population']/Hospital_Covid_PerCounty['hospital_bed_count']
Hospital_Covid_PerCounty.tail(5)
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state_county hospital_bed_count deaths confirmed population confirmed_daily deaths_daily mortality people_per_bed
1652 WV_Monongalia 720 52 6311 105612.0 67.0 1.0 0.008240 146.683333
1657 WV_Putnam 70 63 3444 56450.0 37.0 2.0 0.018293 806.428571
1658 WV_Raleigh 173 37 3505 73361.0 79.0 1.0 0.010556 424.052023
1665 WV_Wood 343 76 5993 83518.0 54.0 6.0 0.012681 243.492711
1669 WY_Natrona 217 103 7039 79858.0 18.0 0.0 0.014633 368.009217

We are going to make the final plot by using seaborn as we did last time

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
plot=sns.relplot(data=Hospital_Covid_PerCounty,x="people_per_bed", y="mortality", hue="confirmed", size="population",
                 sizes=(40, 400)).set(ylim=(0,0.06),xlim=(0,6000))

png

We do not see any correlation between mortality and people per bed count

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