Calculating Percentile in Python Pandas Dataframe

Question

I'm trying to calculate the percentile of each number within a dataframe and add it to a new column called 'percentile'.

This is my attempt:

import pandas as pd
from scipy import stats

data = {'symbol':'FB','date':['2012-05-18','2012-05-21','2012-05-22','2012-05-23'],'close':[38.23,34.03,31.00,32.00]}

df = pd.DataFrame(data)

close = df['close']

for i in df:
    df['percentile'] = stats.percentileofscore(close,df['close'])

The column is not being filled and results in 'NaN'. This should be fairly easy, but I'm not sure where I'm going wrong.

Thanks in advance for the help.

Solution

df.close.apply(lambda x: stats.percentileofscore(df.close.sort_values(),x))

or

df.close.rank(pct=True)

Output:

0    1.00
1    0.75
2    0.25
3    0.50
Name: close, dtype: float64

Comments

[Max Power]

no need for looping through for i in df. see this answer stackoverflow.com/a/44607827/1870832

[danche]

You should know broadcast in Pandas. see this broadcast.

[mattblack]

very simple answer, thanks @scott-boston

[Brad Solomon]

Use .rank -- should be significantly faster

[Mate Hegedus]

.rank is 100% what you should use. That lambda function while correct will be MUCH slower