To find the percentile of a value relative to an array (or in your case a dataframe column), use the scipy function stats.percentileofscore().

For example, if we have a value x (the other numerical value not in the dataframe), and a reference array, arr (the column from the dataframe), we can find the percentile of x by:

from scipy import stats
percentile = stats.percentileofscore(arr, x)

Note that there is a third parameter to the stats.percentileofscore() function that has a significant impact on the resulting value of the percentile, viz. kind. You can choose from rank, weak, strict, and mean. See the docs for more information.

For an example of the difference:

>>> df
   a
0  1
1  2
2  3
3  4
4  5

>>> stats.percentileofscore(df['a'], 4, kind='rank')
80.0

>>> stats.percentileofscore(df['a'], 4, kind='weak')
80.0

>>> stats.percentileofscore(df['a'], 4, kind='strict')
60.0

>>> stats.percentileofscore(df['a'], 4, kind='mean')
70.0

As a final note, if you have a value that is greater than 80% of the other values in the column, it would be in the 80th percentile (see the example above for how the kind method affects this final score somewhat) not the 20th percentile. See this Wikipedia article for more information.

• You can use the pandas.DataFrame.quantile() function.
  • If you look at the API for quantile(), you will see it takes an argument for how to do interpolation. If you want a quantile that falls between two positions in your data:
    • 'linear', 'lower', 'higher', 'midpoint', or 'nearest'.
    • By default, it performs linear interpolation.
    • These interpolation methods are discussed in the Wikipedia article for percentile

import pandas as pd
import numpy as np

# sample data 
np.random.seed(2023)  # for reproducibility
data = {'Category': np.random.choice(['hot', 'cold'], size=(10,)),
        'field_A': np.random.randint(0, 100, size=(10,)),
        'field_B': np.random.randint(0, 100, size=(10,))}
df = pd.DataFrame(data)

df.field_A.mean()  # Same as df['field_A'].mean()
# 51.1

df.field_A.median() 
# 50.0

# You can call `quantile(i)` to get the i'th quantile,
# where `i` should be a fractional number.

df.field_A.quantile(0.1)  # 10th percentile
# 15.6

df.field_A.quantile(0.5)  # same as median
# 50.0

df.field_A.quantile(0.9)  # 90th percentile
# 88.8

df.groupby('Category').field_A.quantile(0.1)
#Category
#cold    28.8
#hot      8.6
#Name: field_A, dtype: float64

df

  Category  field_A  field_B
0     cold       96       58
1     cold       22       28
2      hot       17       81
3     cold       53       71
4     cold       47       63
5      hot       77       48
6     cold       39       32
7      hot       69       29
8      hot       88       49
9      hot        3       49

TL; DR

Use

sz = temp['INCOME'].size-1
temp['PCNT_LIN'] = temp['INCOME'].rank(method='max').apply(lambda x: 100.0*(x-1)/sz)

   INCOME    PCNT_LIN
0      78   44.444444
1      38   11.111111
2      42   22.222222
3      48   33.333333
4      31    0.000000
5      89   55.555556
6      94   66.666667
7     102   77.777778
8     122  100.000000
9     122  100.000000

Answer

It is actually very simple, once your understand the mechanics. When you are looking for percentile of a score, you already have the scores in each row. The only step left is understanding that you need percentile of numbers that are less or equal to the selected value. This is exactly what parameters kind='weak' of scipy.stats.percentileofscore() and method='average' of DataFrame.rank() do. In order to invert it, run Series.quantile() with interpolation='lower'.

So, the behavior of the scipy.stats.percentileofscore(), Series.rank() and Series.quantile() is consistent, see below:

In[]:
temp = pd.DataFrame([  78, 38, 42, 48, 31, 89, 94, 102, 122, 122], columns=['INCOME'])
temp['PCNT_RANK']=temp['INCOME'].rank(method='max', pct=True)
temp['POF']  = temp['INCOME'].apply(lambda x: scipy.stats.percentileofscore(temp['INCOME'], x, kind='weak'))
temp['QUANTILE_VALUE'] = temp['PCNT_RANK'].apply(lambda x: temp['INCOME'].quantile(x, 'lower'))
temp['RANK']=temp['INCOME'].rank(method='max')
sz = temp['RANK'].size - 1 
temp['PCNT_LIN'] = temp['RANK'].apply(lambda x: (x-1)/sz)
temp['CHK'] = temp['PCNT_LIN'].apply(lambda x: temp['INCOME'].quantile(x))

temp

Out[]:
   INCOME  PCNT_RANK    POF  QUANTILE_VALUE  RANK  PCNT_LIN    CHK
0      78        0.5   50.0              78   5.0  0.444444   78.0
1      38        0.2   20.0              38   2.0  0.111111   38.0
2      42        0.3   30.0              42   3.0  0.222222   42.0
3      48        0.4   40.0              48   4.0  0.333333   48.0
4      31        0.1   10.0              31   1.0  0.000000   31.0
5      89        0.6   60.0              89   6.0  0.555556   89.0
6      94        0.7   70.0              94   7.0  0.666667   94.0
7     102        0.8   80.0             102   8.0  0.777778  102.0
8     122        1.0  100.0             122  10.0  1.000000  122.0
9     122        1.0  100.0             122  10.0  1.000000  122.0

Now in a column PCNT_RANK you get ratio of values that are smaller or equal to the one in a column INCOME. But if you want the "interpolated" ratio, it is in column PCNT_LIN. And as you use Series.rank() for calculations, it is pretty fast and will crunch you 255M numbers in seconds.

Here I will explain how you get the value from using quantile() with linear interpolation:

temp['INCOME'].quantile(0.11)
37.93

Our data temp['INCOME'] has only ten values. According to the formula from your link to Wiki the rank of 11th percentile is

rank = 11*(10-1)/100 + 1 = 1.99

The truncated part of the rank is 1, which corresponds to the value 31, and the value with the rank 2 (i.e. next bin) is 38. The value of fraction is the fractional part of the rank. This leads to the result:

 31 + (38-31)*(0.99) = 37.93

For the values themselves, the fraction part have to be zero, so it is very easy to do the inverse calculation to get percentile:

p = (rank - 1)*100/(10 - 1)

I hope I made it more clear.