How calculate IRR on dateframe(df). I have dateframe:
| ID | Amount_paid | Rate1 | Rate2 | Rate3 |
|---|---|---|---|---|
| 1 | -3000 | 78,40 | 78,40 | 2500 |
| 2 | -200 | 28,60 | 28,60 | 28,60 |
| 3 | -4000 | 635,90 | 635,90 | 3600 |
| 4 | -1000 | 635,90 | 635,90 | 3600 |
| 5 | -800 | 635,90 | 635,90 | 3600 |
df['IRR']=npf.irr(df.iloc[:,1:])
ValueError: Input must be a rank-1 array.
df.iloc[:,1:] selects all three columns: Rate1, Rate2 and Rate3. But numpy.irr requires a single array as an input. It fails when you pass it three columns.
Can you let me know what your desired output is?
This works but there may be better ways to iterate through the dataframe. I don't use pandas often.
import pandas as pd
import numpy_financial as npf
df = pd.DataFrame()
df[ 'Amount_paid' ] = [ -3000, -200, -4000, -1000, -800 ]
df[ 'Rate1'] = [ 78.4, 28.6, 635.9, 635.9, 635.9 ]
df[ 'Rate2'] = [ 78.4, 28.6, 635.9, 635.9, 635.9 ]
df[ 'Rate3'] = [ 2500, 28.6, 3600, 3600, 3600 ]
result = []
for ix in range( len(df)):
arr = df.iloc[ ix ]
result.append( npf.irr(arr) )
result
# [-0.028570231670919766,
# -0.3277960041116903,
# 0.07913479979420979,
# 0.930852297472391,
# 1.1442753876634035]
df[ 'IRR'] = result
df
# Amount_paid Rate1 Rate2 Rate3 IRR
# 0 -3000 78.4 78.4 2500.0 -0.040830
# 1 -200 28.6 28.6 28.6 -0.327796
# 2 -4000 635.9 635.9 3600.0 0.079135
# 3 -1000 635.9 635.9 3600.0 0.930852
# 4 -800 635.9 635.9 3600.0 1.144275
A version using df.apply
df[ 'IRR' ] = 0 # Reset 'IRR' to ensure apply does change it.
df[ 'IRR' ] = df.apply( npf.irr, axis = 1 )
df
# Amount_paid Rate1 Rate2 Rate3 IRR
# 0 -3000 78.4 78.4 2500.0 -0.040830
# 1 -200 28.6 28.6 28.6 -0.327796
# 2 -4000 635.9 635.9 3600.0 0.079135
# 3 -1000 635.9 635.9 3600.0 0.930852
# 4 -800 635.9 635.9 3600.0 1.144275
