Python: How to iterate over rows and calculate value based on previous row

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I have sales data till Jul-2020 and want to predict the next 3 months using a recovery rate. This is the dataframe:

test = pd.DataFrame({'Country':['USA','USA','USA','USA','USA'],
             'Month':[6,7,8,9,10],
              'Sales':[100,200,0,0,0],
              'Recovery':[0,1,1.5,2.5,3]
             })

This is how it looks:

enter image description here

Now, I want to add a "Predicted" column resulting into this dataframe:

enter image description here

The first value 300 at row 3, is basically (200 * 1.5/1). This will be our base value going ahead, so next value i.e. 500 is basically (300 * 2.5/1.5) and so on. How do I iterate over row every row, starting from row 3 onwards? I tried using shift() but couldn't iterate over the rows.

pandas python
2 Answers

You could do it like this:

import pandas as pd
test = pd.DataFrame({'Country':['USA','USA','USA','USA','USA'],
             'Month':[6,7,8,9,10],
              'Sales':[100,200,0,0,0],
              'Recovery':[0,1,1.5,2.5,3]
             })

test['Prediction'] = test['Sales']
for i in range(1, len(test)):
    #prevent division by zero
    if test.loc[i-1, 'Recovery'] != 0:
        test.loc[i, 'Prediction'] = test.loc[i-1, 'Prediction'] * test.loc[i, 'Recovery'] / test.loc[i-1, 'Recovery']
user_119717850

The sequence you have is straight up just Recovery * base level (Sales = 200)

You can compute that sequence like this:

valid_sales = test.Sales > 0
prediction = (test.Recovery * test.Sales[valid_sales].iloc[-1]).rename("Predicted")

And then combine by index, insert column or concat:

pd.concat([test, prediction], axis=1)
user_79675880
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