CODEWITHSUNDEEP
pandasdataframematplotlibbar-chartmultiple-columns
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Reputation: 31

How to chart two different pandas data frames into one chart on matplotlib?

I have two separate sets of data using pandas:

>>> suicides_sex = suicides_russia.groupby("sex")["suicides_no"].sum()
>>> suicides_sex
sex
female    214330
male      995412

&

>>> suicides_age = suicides_russia.groupby("age") 
>>> ["suicides_no"].sum().sort_values()
>>> suicides_age
age
5-14 years       8840
75+ years       74211
15-24 years    148611
25-34 years    231187
55-74 years    267753
35-54 years    479140

I want to learn how to create either a double bar chart using matplotlib or two separate bar charts where I can separate each age group by gender.

How can I combine both sets of data to create either a single bar chart with double columns or two separate bar charts for each gender?

Upvotes: 0

Views: 77

Answers (1)

roman_ka
roman_ka

Reputation: 598

You can use boolean masks to separate the data and then group by age as you did.

import matplotlib.pyplot as plt

suicide_male = suicide_russia.loc[suicide_russia['sex']=='male', :]
# now you basically have the same dataframe but for male only

suicide_male_age = suicides_male.groupby("age")["suicides_no"].sum()

plt.bar(height=suicide_male_age.values, x=np.arange(suicide_male_age.index))
plt.xticks(labels=suicide_male_age.index)
plt.show()

Then you can repeat the same for female. That is probably not the most efficient way of doing it, but it works.

Also, I assumed the 'age' column values are strings, so I put np.arange as x positions of the bars and the values themselves as xticks.

Hope it helps!

Upvotes: 1

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