Can Someone tell how can I Find Monthly Revenue, sort it and Visualize it.
Month&Year | Monthly Revenue
0 2016-11 | 261.9600
1 2016-11 | 731.9400
2 2016-06 | 14.6200
3 2015-10 | 957.5775
4 2015-10 | 22.3680
9989 2014-01 | 25.2480
9990 2017-02 | 91.9600
9991 2017-02 | 258.5760
9992 2017-02 | 29.6000
9993 2017-05 | 243.1600
How can I Display the sum of revenue on basis of individual months of different year
pandas.to_datetime.dt accessor
pandas.DataFrame.groupby
pandas.DataFrame.plot.barhimport pandas as pd
import matplotlib.pyplot as plt
# setup dataframe
data = {'Month&Year': ['2016-11', '2016-11', '2016-06', '2015-10', '2015-10', '2014-01', '2017-02', '2017-02', '2017-02', '2017-05'],
'Monthly Revenue': [261.96, 731.94, 14.62, 957.5775, 22.368, 25.248, 91.96, 258.576, 29.6, 243.16]}
df = pd.DataFrame(data)
# convert the Month&Year column to a datetime column
df['Month&Year'] = pd.to_datetime(df['Month&Year'], format='%Y-%m')
# use the .dt accessor to groupby year and month and sum Monthly Revenue
dfg = df.groupby([df['Month&Year'].dt.year, df['Month&Year'].dt.month]).agg({'Monthly Revenue': sum})
# rename the index columns
dfg.index = dfg.index.set_names(['year', 'month'])
# display(dfg)
Monthly Revenue
year month
2014 1 25.2480
2015 10 979.9455
2016 6 14.6200
11 993.9000
2017 2 380.1360
5 243.1600
# plot
dfg.plot.barh(figsize=(8, 5), legend=False)
plt.xlabel('Revenue')
plt.xscale('log')
plt.show()
year and month, groupby date.# groupby
dfg = df.groupby(df['Month&Year'].dt.date).agg({'Monthly Revenue': sum})
# plot
dfg.plot.barh(figsize=(8, 5), legend=False)
plt.xlabel('Revenue')
plt.ylabel('Date')
plt.xscale('log')
plt.show()
