Create new columns from unique row values in a pandas dataframe

user_5102599 0
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I have a pandas dataframe like :

    yearPassed  policyType  count
0       1990        1        2000
1       1990        2        1400
2       1990        3        1200
3       1991        3        70
4       1992        2        1000
5       1992        3        800

I want to make a bar chart, color-coded by policyType column, and showing Year on X-Axis and count on Y-axis.

I tried doing this:

policy_vs_year.plot(x="yearPassed", y=["count", "policyType"], kind="bar")
plt.show()

but this gives a very bad plot.

So I decided to transform my dataframe into something like this (maybe it is easier to plot this way):

    yearPassed       1       2       3
0       1990       2000    1400     1200
1       1991        0        0       70
2       1992        0      1000     800

My question is whether it is possible with elementary functions in pandas to achieve this ? (or there are simpler alternatives to plot the dataframe in its original format - without having to reformat it ?)

pandas dataframe matplotlib plot python
2 Answers

This is easily done using df.pivot_table:

df = df.pivot_table(index=['yearPassed'], 
            columns=['policyType'], values='count').fillna(0)
df

policyType       1       2       3
yearPassed                        
1990        2000.0  1400.0  1200.0
1991           0.0     0.0    70.0
1992           0.0  1000.0   800.0

Furthermore, a stacked bar plot can be made using df.plot:

import matplotlib.pyplot as plt
df.plot(kind='bar', stacked=True)
plt.show()

enter image description here

user_49090870

Just using pandas 

df.set_index(['yearPassed','policyType']).unstack(-1).fillna(0).plot.bar(stacked=True)

enter image description here

user_79645270
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