I want to find all unique values in one column and normalize the corresponding values in another column to its last value. I want to achieve this via the pandas module using python3 .
Example:
Original dataset
Fruit | Amount
Orange | 90
Orange | 80
Orange | 10
Apple | 100
Apple | 50
Orange | 20
Orange | 60 --> latest value of Orange. Use to normalize Orange
Apple | 75
Apple | 25
Apple | 40 --> latest value of Apple. Used to normalize Apple
Desired output
Ratio column with normalized values for unique values in the 'Fruit' column
Fruit | Amount | Ratio
Orange | 90 | 90/60 --> 150%
Orange | 80 | 80/60 --> 133.3%
Orange | 10 | 10/60 --> 16.7%
Apple | 100 | 100/40 --> 250%
Apple | 50 | 50/40 --> 125%
Orange | 20 | 20/60 --> 33.3%
Orange | 60 | 60/60 --> 100%
Apple | 75 | 75/40 --> 187.5%
Apple | 25 | 25/40 --> 62.5%
Apple | 40 | 40/40 --> 100%
Python code attempt
import pandas as pd
filename = r'C:\fruitdata.dat'
df = pd.read_csv(filename, delimiter='|')
print(df)
print(df.loc[df['Fruit '] == 'Orange '])
print(df[df['Fruit '] == 'Orange '].tail(1))
Python output (IPython)
In [1]: df
Fruit Amount
0 Orange 90
1 Orange 80
2 Orange 10
3 Apple 100
4 Apple 50
5 Orange 20
6 Orange 60
7 Apple 75
8 Apple 25
9 Apple 40
In [2]: df.loc[df['Fruit '] == 'Orange ']
Fruit Amount
0 Orange 90
1 Orange 80
2 Orange 10
5 Orange 20
6 Orange 60
In [3]: df[df['Fruit '] == 'Orange '].tail(1)
Out[3]:
Fruit Amount
6 Orange 60
Question
How can I loop through each unique item in 'Fruit' and normalize all values against its tail value?
You can using iloc
with groupby
df.groupby('Fruit').Amount.apply(lambda x: x/x.iloc[-1])
Out[38]:
0 1.500000
1 1.333333
2 0.166667
3 2.500000
4 1.250000
5 0.333333
6 1.000000
7 1.875000
8 0.625000
9 1.000000
Name: Amount, dtype: float64
After assign it back
df['New']=df.groupby('Fruit').Amount.apply(lambda x: x/x.iloc[-1])
df
Out[40]:
Fruit Amount New
0 Orange 90 1.500000
1 Orange 80 1.333333
2 Orange 10 0.166667
3 Apple 100 2.500000
4 Apple 50 1.250000
5 Orange 20 0.333333
6 Orange 60 1.000000
7 Apple 75 1.875000
8 Apple 25 0.625000
9 Apple 40 1.000000
Without using lambda
df.Amount/df.groupby('Fruit',sort=False).Amount.transform('last')
Out[46]:
0 1.500000
1 1.333333
2 0.166667
3 2.500000
4 1.250000
5 0.333333
6 1.000000
7 1.875000
8 0.625000
9 1.000000
Name: Amount, dtype: float64
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