This page contains visualizations of available open datasets with information from Delaware.

Births in Delaware from 2009 to 2014

Using the following dataset we can analyze the births in Delaware from 2009 to 2014. I will be using the requests library to do so.

import requests
import matplotlib.pyplot as plt
url = 'https://data.delaware.gov/resource/y8fa-dqxh.json?$limit=100000'
response = requests.get(url)
if response.status_code == 200:
    data = response.json()

We can now use our response object to analyze the data. Delaware is a small state, let's find out how many births, of the total 72456, were in each county. Delaware has three counties: New Castle, Kent, Sussex.

We see the totals for each county.

kent, sussex, ncc = 0, 0, 0
for item in data:
    if item['county_of_residence'] == 'Sussex':
        sussex += 1
    elif item['county_of_residence'] == 'New Castle':
        ncc += 1
    else:
        kent += 1
print(ncc, kent, sussex)

New Castle	Kent	Sussex
39,827	19,154	13,475

We can plot that data to show the following:

labels = 'NCC', 'Kent', 'Sussex'
sizes = [ncc, kent, sussex]
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=False, startangle=90)
ax1.axis('equal')
plt.show()

We can also use the same method as above to take a look at the age of the mothers at the time they gave birth.Unforuntaely our dataset is limited to three different age ranges but we will make due with what we have. Again we can iterate through each of the items in the dataset and count each occurence in each range:

twenty, thirtyfive, less_than_twenty = 0, 0, 0
for item in data:
    if item['mom_s_age'] == '20-34':
        twenty += 1
    elif item['mom_s_age'] == '35+':
        thirtyfive += 1
    else:
        less_than_twenty += 1
print(less_than_twenty, twenty, thirtyfive)

Under Twenty	Twenty to Thirty-four	Thirty-Five Plus
5,271	56,402	10,783

Doing so we see that there were 5,271 births from mothers aged less than 20, 56,402 from mothers aged 20-34, and 10,783 aged 35+. Now let's filter the data by county and age. We can do that for each county using the following:

Sussex

    sussex_twenty, sussex_thirtyfive, sussex_less_than_twenty = 0, 0, 0
    for item in data:
        if item['county_of_residence'] == 'Sussex':
            if item['mom_s_age'] == '20-34':
                sussex_twenty += 1
            elif item['mom_s_age'] == '35+':
                sussex_thirtyfive += 1
            else:
                sussex_less_than_twenty += 1
    print(sussex_less_than_twenty, sussex_twenty, sussex_thirtyfive)

Under Twenty	Twenty to Thirty-four	Thirty-Five Plus
1,268	10,603	1,604

Kent

    kent_twenty, kent_thirtyfive, kent_less_than_twenty = 0, 0, 0
    for item in data:
        if item['county_of_residence'] == 'Kent':
            if item['mom_s_age'] == '20-34':
                kent_twenty += 1
            elif item['mom_s_age'] == '35+':
                kent_thirtyfive += 1
            else:
                kent_less_than_twenty += 1
    print(kent_less_than_twenty, kent_twenty, kent_thirtyfive)

Under Twenty	Twenty to Thirty-four	Thirty-Five Plus
1,015	10,869	1,420

New Castle

    ncc_twenty, ncc_thirtyfive, ncc_less_than_twenty = 0, 0, 0
    for item in data:
        if item['county_of_residence'] == 'New Castle':
            if item['mom_s_age'] == '20-34':
                ncc_twenty += 1
            elif item['mom_s_age'] == '35+':
                ncc_thirtyfive += 1
            else:
                ncc_less_than_twenty += 1
    print(ncc_less_than_twenty, ncc_twenty, ncc_thirtyfive)

Under Twenty	Twenty to Thirty-four	Thirty-Five Plus
2,814	30,579	6,434