Confidence Interval in Python dataframe

Question

I am trying to calculate the mean and confidence interval(95%) of a column "Force" in a large dataset. I need the result by using the groupby function by grouping different "Classes".

When I calculate the mean and put it in the new dataframe, it gives me NaN values for all rows. I'm not sure if I'm going the correct way. Is there any easier way to do this?

This is the sample dataframe:

df=pd.DataFrame({ 'Class': ['A1','A1','A1','A2','A3','A3'], 
                  'Force': [50,150,100,120,140,160] },
                   columns=['Class', 'Force'])

To calculate the confidence interval, the first step I did was to calculate the mean. This is what I used:

F1_Mean = df.groupby(['Class'])['Force'].mean()

This gave me NaN values for all rows.

Answer 1

Update on 25-Oct-2021: @a-donda pointed out, 95% shall be based on 1.96 X standard deviations of the mean.

import pandas as pd
import numpy as np
import math

df=pd.DataFrame({'Class': ['A1','A1','A1','A2','A3','A3'], 
                 'Force': [50,150,100,120,140,160] },
                 columns=['Class', 'Force'])
print(df)
print('-'*30)

stats = df.groupby(['Class'])['Force'].agg(['mean', 'count', 'std'])
print(stats)
print('-'*30)

ci95_hi = []
ci95_lo = []

for i in stats.index:
    m, c, s = stats.loc[i]
    ci95_hi.append(m + 1.96*s/math.sqrt(c))
    ci95_lo.append(m - 1.96*s/math.sqrt(c))

stats['ci95_hi'] = ci95_hi
stats['ci95_lo'] = ci95_lo
print(stats)

The output is

  Class  Force
0    A1     50
1    A1    150
2    A1    100
3    A2    120
4    A3    140
5    A3    160
------------------------------
       mean  count        std
Class                        
A1      100      3  50.000000
A2      120      1        NaN
A3      150      2  14.142136
------------------------------
       mean  count        std     ci95_hi     ci95_lo
Class                                                
A1      100      3  50.000000  156.580326   43.419674
A2      120      1        NaN         NaN         NaN
A3      150      2  14.142136  169.600000  130.400000