CODEWITHSUNDEEP
pythonpandasdataframematplotlibplot
user40
user40

Reputation: 1396

Plot stacked bar chart from pandas data frame

I have dataframe:

payout_df.head(10)

enter image description here

What would be the easiest, smartest and fastest way to replicate the following excel plot?

enter image description here

I've tried different approaches, but couldn't get everything into place.

Thanks

Upvotes: 1

Views: 15052

Answers (2)

ALollz
ALollz

Reputation: 59579

If you just want a stacked bar chart, then one way is to use a loop to plot each column in the dataframe and just keep track of the cumulative sum, which you then pass as the bottom argument of pyplot.bar

import pandas as pd
import matplotlib.pyplot as plt

# If it's not already a datetime
payout_df['payout'] = pd.to_datetime(payout_df.payout)

cumval=0
fig = plt.figure(figsize=(12,8))
for col in payout_df.columns[~payout_df.columns.isin(['payout'])]:
    plt.bar(payout_df.payout, payout_df[col], bottom=cumval, label=col)
    cumval = cumval+payout_df[col]

_ = plt.xticks(rotation=30)
_ = plt.legend(fontsize=18)

enter image description here

Upvotes: 7

gmacro
gmacro

Reputation: 427

Besides the lack of data, I think the following code will produce the desired graph

import pandas as pd
import matplotlib.pyplot as plt

df.payout = pd.to_datetime(df.payout)

grouped = df.groupby(pd.Grouper(key='payout', freq='M')).sum()
grouped.plot(x=grouped.index.year, kind='bar', stacked=True)

plt.show()

I don't know how to reproduce this fancy x-axis style. Also, your payout column must be a datetime, otherwise pd.Grouper won't work (available frequencies).

Upvotes: 2

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