I want normalize data by dividing every row by the price on the first row-CodePudding

I have a dataframe in pandas. I want to normalize my Close column by dividing every row by the price on the first row. This is my attempt:

import pandas as pd
import yfinance as yf

bova=yf.download('BOVA11.SA',
                 start='2014-01-01',
                 end='2021-12-31',
                 progress=False,
                 )
bova

output:

              Open          High     Low          Close     Adj Close   Volume
Date                    
2014-01-02  50.220001   50.270000   49.060001   49.080002   49.080002   1001210
2014-01-03  49.099998   49.470001   49.049999   49.259998   49.259998   1227270
2014-01-06  49.259998   49.990002   49.200001   49.840000   49.840000   702060
2014-01-07  49.549999   50.290001   49.230000   49.230000   49.230000   1304100
2014-01-08  49.540001   49.590000   49.209999   49.279999   49.279999   951950
... ... ... ... ... ... ...
2021-12-23  101.599998  101.599998  100.709999  100.849998  100.849998  5047637
2021-12-27  101.400002  101.800003  100.949997  101.599998  101.599998  5274352
2021-12-28  101.830002  101.830002  100.589996  101.059998  101.059998  7167734
2021-12-29  101.239998  101.349998  99.940002   100.250000  100.250000  7343838
2021-12-30  100.809998  101.449997  100.290001  100.800003  100.800003  3404622

CodePudding user response：

So something like this?

for c in df.columns:
    if c != 'Date':
        df[c] = df[c]/df[c][0]

This will iterate every column of the dataframe except for Date, and will divide it by the value of the corresponding column of the first row.

CodePudding user response：

Use:

bova['Norm Close'] = bova['Close'] / bova['Close'][0]
print(bova[['Close', 'Norm Close']])

# Output
                 Close  Norm Close
Date                              
2014-01-02   49.080002    1.000000
2014-01-03   49.259998    1.003667
2014-01-06   49.840000    1.015485
2014-01-07   49.230000    1.003056
2014-01-08   49.279999    1.004075
...                ...         ...
2021-12-23  100.849998    2.054808
2021-12-27  101.599998    2.070090
2021-12-28  101.059998    2.059087
2021-12-29  100.250000    2.042583
2021-12-30  100.800003    2.053790

[1986 rows x 2 columns]