Map counts of a numerical column from a new DataFrame to the bin range column of training data

Question

I am trying to get the count of Age column and append it to my existing bin-range column created. I am able to do it for the training df and want to do it for prediction data. How do I map values of count of Age column from prediction data to to Age_bin column in my training data? The first one is my output DF whereas the 2nd one is the sample DF. I can get the count using value_counts() for the file I am reading.

1. First image - bin and count from training data
2. Second image - Training data
3. Third image - Prediction data
4. Fourth image - Final output

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Answer 1

The Data

import pandas as pd 

data = { 
     0: 0, 
     11: 1500, 
     12: 1000, 
     22: 3000, 
     32: 35000,
     34: 40000, 
     44: 55000, 
     65: 7000, 
     80: 8000, 
     100: 1000000, 
}

df = pd.DataFrame(data.items(), columns=['Age', 'Salary'])

   Age   Salary
0    0        0
1   11     1500
2   12     1000
3   22     3000
4   32    35000
5   34    40000
6   44    55000
7   65     7000
8   80     8000
9  100  1000000

The Code

bins = [-0.1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

# create a "binned" column 
df['binned'] = pd.cut(df['Age'], bins)

# add bin count
df['count'] = df.groupby('binned')['binned'].transform('count')

The Output

   Age   Salary         binned  count
0    0        0   (-0.1, 10.0]      1
1   11     1500   (10.0, 20.0]      2
2   12     1000   (10.0, 20.0]      2
3   22     3000   (20.0, 30.0]      1
4   32    35000   (30.0, 40.0]      2
5   34    40000   (30.0, 40.0]      2
6   44    55000   (40.0, 50.0]      1
7   65     7000   (60.0, 70.0]      1
8   80     8000   (70.0, 80.0]      1
9  100  1000000  (90.0, 100.0]      1