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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 0. How is this data distributed?
The presence of extrame values is a common pattern in this dataframe. We need to be carefull while using cetered measures.
Step1:  1. In what year new yorkers died the most?
2007 was the year that more cases of death were reported. However, not a single year was very far away from the mean.
Step2:  2. Who is more likely to die, a male newyorker or a female new yorker?
Surprisingly, women are most likely to die than men in NYC.
Step3:  3. Is Sex (in)difference in the data the same for all the years available?
Diference is not very significants, but in all years mortality was more frequent in women in NYC.
Step4:  4. Which one are the most common deseases for men and woman? Is there a difference?
Mayor causes of death like heart deseases or Cancer seems to behave similar in the top 3 for men and women.
Step5:  5. What about in other deseases? Are there any sex differences?
Crebrovascular, diabetes and cronic lower respiratory deseases are more comen in women than men. On the other hand, Diabetes, HIV and deaths by accidents (note related to drugs) are most common in men.
Step6:  6. What is the leading cause of death?
NYC has a real heart problem
Step7:  7. What other deseases are frequent in newyorkers?
Top 10 deseases that causes death in NYC
Step8:  Hearth Deseases and Cancer  are by far the most common causes of death!
Step9:  8. Which ethnicity is overall more vulnerable to deseases in NYC?
According to the number of cases reported, non-hispanic white newyorkers have the highest mortality rate.
Step10:  9. Is distribution between ethnicities the same? What about white newyorkers?
Data on white population seems to be very similar to the overall outcome.
Step11:  10. Same for african americans?
Same distribution with the exception of an increase in deaths produced by diabetes type 2 and HIV
Step12:  11. What about spanish and asians?
Similar results for spanish population
Step13:  Asians are by far the most different population in relations to causes of death. It has the highest incidents of death by Cancer
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Python Code:
df['Count'].plot.box()
Explanation: 0. How is this data distributed?
The presence of extrame values is a common pattern in this dataframe. We need to be carefull while using cetered measures.
End of explanation
df.groupby('Year')['Count'].sum().sort_values(ascending=False)
df.groupby('Year')['Count'].sum().mean()
fig, ax = plt.subplots(figsize=(9, 6))
df.groupby('Year')['Count'].sum().plot.barh()
mean = df.groupby('Year')['Count'].sum().mean()
ax.plot([mean, mean], [0, 12], c='blue', linestyle="-", linewidth=0.5)
ax.annotate(s="Mean of death registered, 190,629.6", xy=(120000,0), color='Blue')
Explanation: 1. In what year new yorkers died the most?
2007 was the year that more cases of death were reported. However, not a single year was very far away from the mean.
End of explanation
df.groupby('Sex')['Count'].sum()
Explanation: 2. Who is more likely to die, a male newyorker or a female new yorker?
Surprisingly, women are most likely to die than men in NYC.
End of explanation
df.groupby(['Year', 'Sex'])['Count'].sum()
fig, ax = plt.subplots(figsize=(9, 7))
df.groupby(['Year', 'Sex'])['Count'].sum().plot(color=['darkred', 'blue'],kind='bar', title="deaths by gender over time")
ax.set_ylabel('total deaths')
ax.set_ylabel('Sex')
ax.set_ylim((0,110000))
Explanation: 3. Is Sex (in)difference in the data the same for all the years available?
Diference is not very significants, but in all years mortality was more frequent in women in NYC.
End of explanation
df.groupby(['Cause of Death', 'Sex'])['Count'].sum().sort_values(ascending=False).head(6)
Explanation: 4. Which one are the most common deseases for men and woman? Is there a difference?
Mayor causes of death like heart deseases or Cancer seems to behave similar in the top 3 for men and women.
End of explanation
df.groupby(['Cause of Death', 'Sex'])['Count'].sum().sort_values(ascending=False)
Explanation: 5. What about in other deseases? Are there any sex differences?
Crebrovascular, diabetes and cronic lower respiratory deseases are more comen in women than men. On the other hand, Diabetes, HIV and deaths by accidents (note related to drugs) are most common in men.
End of explanation
#Disease of heart is the number 1
df.groupby('Cause of Death')['Count'].sum().sort_values(ascending=False).head(1)
Explanation: 6. What is the leading cause of death?
NYC has a real heart problem
End of explanation
df.groupby('Cause of Death')['Count'].sum().sort_values(ascending=False).head(10)
Explanation: 7. What other deseases are frequent in newyorkers?
Top 10 deseases that causes death in NYC
End of explanation
fig, ax = plt.subplots(figsize=(9, 7))
df.groupby('Cause of Death')['Count'].sum().sort_values(ascending=True).plot.barh()
ax.set_xlim((0,400000))
Explanation: Hearth Deseases and Cancer  are by far the most common causes of death!
End of explanation
df.groupby('Ethnicity')['Count'].sum().sort_values(ascending=False)
df.groupby('Ethnicity')['Count'].sum().plot.barh(color=['Black', 'Black', 'Black', 'darkred'])
Explanation: 8. Which ethnicity is overall more vulnerable to deseases in NYC?
According to the number of cases reported, non-hispanic white newyorkers have the highest mortality rate.
End of explanation
fig, ax = plt.subplots(figsize=(9, 7))
only_whites = df[df['Ethnicity'] == 'NON-HISPANIC WHITE']
only_whites.groupby('Cause of Death')['Count'].sum().sort_values(ascending=True).plot.barh(color='blue')
Explanation: 9. Is distribution between ethnicities the same? What about white newyorkers?
Data on white population seems to be very similar to the overall outcome.
End of explanation
fig, ax = plt.subplots(figsize=(9, 7))
only_blacks = df[df['Ethnicity'] == 'NON-HISPANIC BLACK']
only_blacks.groupby('Cause of Death')['Count'].sum().sort_values(ascending=True).plot.barh(color='black')
Explanation: 10. Same for african americans?
Same distribution with the exception of an increase in deaths produced by diabetes type 2 and HIV
End of explanation
fig, ax = plt.subplots(figsize=(9, 7))
only_hispanic = df[df['Ethnicity'] == 'HISPANIC']
only_hispanic.groupby('Cause of Death')['Count'].sum().sort_values(ascending=True).plot.barh(color='brown')
Explanation: 11. What about spanish and asians?
Similar results for spanish population
End of explanation
only_asians = df[df['Ethnicity'] == 'ASIAN & PACIFIC ISLANDER']
fig, ax = plt.subplots(figsize=(9, 7))
only_asians.groupby('Cause of Death')['Count'].sum().sort_values(ascending=True).plot.barh(color='green')
Explanation: Asians are by far the most different population in relations to causes of death. It has the highest incidents of death by Cancer
End of explanation
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