My Body, My Choice: Abortions in New York City
VISUALIZATION EXPLAINED:
The information on the right shows the number of insurances that are connected to each named abortion center/clinic and have Medicaid as one of the insurances listed. The information displays that H+H Jacobi Options Service has the most insurances that are offered, along with Medicaid, and is located in the Bronx. Also, the data shows that the most insurances along with Medicaid are in the Bronx and Brooklyn, while Queens, Manhattan, and fake clinic (listed below) seem to lack having Medicaid as one of their providers. Therefore, people with Medicaid that reside in the Bronx and Brooklyn seem to have more availability in which abortion clinics/centers they can have consultations and procedures at while people in Manhattan and Queens are only limited to private insurances and higher qualified insurances. This also shows that lower-incomed individuals reside more in areas of Brooklyn and Bronx due to the type of insurance (Medicaid) that is available than in other boroughs.
TECHNIQUE:
CODE:
First, I read the csv into a dataframe. I created one data frame that started with the column of names. I moved on to using the .loc function for pandas to make another data frame that stored the next column to have the number of insurances grouped by the condition that the column Medicaid has to be true ('yes'). Then, I concatenated the two data frames that were created separately with an axis of 1 into one data frame.
result2 = df['Name']
result= df.loc[(df['Medicaid']=="yes") & (df['Num_of_insurance']), ['Medicaid','Num_of_insurance']]
resulty = pd.concat([result2, result], axis=1)
print(resulty)
The data on the left shows number of fake clinics that are ordered by its matching borough, number and name. This information is given to show who has the most amount of fake clinics present today and their essential information to avoid locations that can perform illegal abortion services that aren't certified and sway decisions based on bias and personal belief. The most amount of fake clinics prove to be in Queens, Brooklyn, and Manhattan and the least is the Bronx. Therefore, this indicates that fake clinics heavily affect most regions in NYC in terms of boroughs and has no correlation to a specific community. Also, the information further researched shows that most religiously motivated areas that sway women and girls from getting abortions are present mostly in Manhattan and Brooklyn while Queens and Bronx has more medically suspicious performances being done.
VISUALIZATION EXPLAINED:
CODE:
result= df.loc[(df['Fake_clinic']=="yes") & (df['Borough']) & (df['Number']) & (df['Name']), ['Fake_clinic','Borough','Number','Name']]
print(result)
result.to_csv(csvOutput, index=False)
VISUALIZATION EXPLAINED:
The information on the right shows the number of non-empty values, the average value, standard deviation, minimum value, the 25%,50%, and 75% percentile, and maximum value for the number of insurances, surgical durations and medication durations for procedures that are available universally throughout all boroughs. The information reveals that NYC has a location that offers abortion services with a maximum of 5 insurances and minimum number of insurances at 1. Also, this shows that the minimum time to get a medication and surgical abortion procedures are 8 weeks and 9 weeks respectively and the maximum time to get a surgical and medication abortion is 25 and 24 weeks. On average, the number of insurances offered at one clinic or center would be around 3 and the duration for surgical and medication abortions are 20 and 10 weeks respectively.
TECHNIQUE:
Similarly to the first data analysis, I used .loc function from pandas to serve as a group by function to give four columns based on conditions. I made one data frame without concatenating by setting both columns 'Borough' and 'Number' to the condition that the first column, 'Fake_clinic', must be set to yes. Then, outside of those two columns following the condition, it should include the matching name of the clinic.
TECHNIQUE:
CODE:
s = df.squeeze(axis=1)
reading = s.describe()
print(reading)
reading.to_csv(csvOutput, index=False)
The technique for this output is using the describe() function for python package pandas. Firstly, I used the function squeeze() to remove single-dimensional entries from the shape of the data frame. Then, I used the describe function to all the columns in the data frame, as the function automatically finds columns that have values that are numerical ('Num_of_insurance','Surgical_duration','Medication_duration').