Example use of data, producing PM10 24 hour maximum and smoker rate against asthma

ovi.py

+import pandas as pandas
+import matplotlib.pyplot as plt
+
+# Retrieve the maximum daily average for 2016 PM10, and then index by the appropriate LGA code
+MAX_24HR = pandas.read_csv("PM10_MAX_24HR_READING.csv")
+PM10_to_LGA = pandas.read_csv("PM10_to_LGA.csv")
+data = MAX_24HR
+data["LGA_CODE_2011"] = PM10_to_LGA["LGA_CODE_2011"]
+data = data.set_index("LGA_CODE_2011")
+
+# Add the adult smoker rate to this data
+LGA_by_HEALTH = pandas.read_csv("LGA_by_HEALTH.csv")
+LGA_by_HEALTH = LGA_by_HEALTH.set_index("LGA_CODE_2011")
+data["smokers"] = LGA_by_HEALTH["smokers"]
+
+# Average out all SA2 data for each LGA
+SA2_by_DISEASES = pandas.read_csv("SA2_by_DISEASES.csv")
+SA2_to_LGA = pandas.read_csv("SA2_to_LGA.csv")
+LGA_by_DISEASES = SA2_by_DISEASES.join(SA2_to_LGA.set_index("SA2_MAINCODE_2011"), on="SA2_MAINCODE_2011")
+LGA_by_DISEASES = LGA_by_DISEASES.drop("SA2_MAINCODE_2011", axis = 1)
+LGA_by_DISEASES = LGA_by_DISEASES.groupby(["LGA_CODE_2011"]).mean()
+
+# Add asthma data
+data["asthma"] = LGA_by_DISEASES["asthma"]
+data = data.reset_index()
+data = data.sort_values(["asthma"])
+
+fig, ax = plt.subplots()
+
+# Maximum PM10 24 hour average against asthma incidence for local government area
+pl1 = ax.scatter(data["asthma"], data["value"], c = "b")
+ax.set_ylabel("Maximum PM10 24 hour reading (2016, μg/m³)", color = "blue")
+ax.set_xlabel("Asthma incidence (percentage)")
+fig.savefig("ovi_asthma_vs_pm1024hr.png", bbox_inches='tight')
+
+# Now add against smoker rate
+ax2 = ax.twinx()
+pl2 = ax2.scatter(data["asthma"], data["smokers"], c = "r")
+ax2.set_ylabel("Adult smokers rate (percentage)", color = "red")
+fig.savefig("ovi_asthma_vs_pm1024hr_smokers.png", bbox_inches='tight')
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