Plot Stacked bar chart and Multiple bars chart from Pandas dataframe

Question

I have CSV data that is read into a Pandas dataframe such as:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("membership-applications.csv", delimiter=";")
df.sort_values(by=['year', 'quarter'], inplace=True, ascending=True)

quarter,year,type,status,total
Q1,2019,new,approved,10
Q1,2019,renewal,approved,30
Q2,2019,new,approved,10
Q2,2019,new,rejected,20
Q2,2019,renewal,0
Q3,2019,new,0
Q3,2019,renewal,0
Q4,2019,new,0
Q4,2019,renewal,0
Q1,2020,new,approved,10
Q1,2020,renewal,approved,50

How can I plot a stacked bar chart based on quarter, year and total (e.g. sum of each of 'new' or 'renewal' including all statuses)? For example,

                                  +------+ 
+------+                          |  10  |
|  10  | +------+                 +------+
+------+ |  10  |                 |      |
|      | +------+                 |  50  |
|  30  | |  20  |                 |      |
|      | |      |                 |      |
+------+ +------+                 +------+
Q1 2019  Q2 2019  Q3 2019 Q4 2019 Q1 2020

Also, based on the same dataframe, how can I a plot multiple-bar chart, for example for Q1 2019, the first bar is 'new' (which a total of 10) and 'renewal' as the next bar?

Something looks like this:

     +--+
     |  |
+--+ |30| 
|10| |  |
+--+ +--+
 Q1 2019

Thanks in advance for your help!!

Answer 1

you can try pivot_table to reshape the data:

fig = df.pivot_table(index = ['year','quarter'], columns = 'type', values = 'total', dropna=False , fill_value = 0).plot(kind ='bar', stacked = True)

OUTPUT:

To display bar side-by-side just remove the stack parameter:

fig = df.pivot_table(index = ['year','quarter'], columns = 'type', values = 'total', dropna=False , fill_value = 0).plot(kind ='bar')
plt.xticks(rotation = 30)

OUTPUT: