personal favorite way:

df.column_name.value_counts() / len(df)

Gives a series with the column's values as the index and the proportions as the values.

You can use GroupBy.value_counts with normalize=True, and reshaping:

(df
 .groupby(['A', 'B'])['C']
 .value_counts(normalize=True)
 .unstack('C', fill_value=0)
 .reset_index()
)

output:

C  A  B    0    1    2    3
0  x  i  0.5  0.5  0.0  0.0
1  x  j  0.0  1.0  0.0  0.0
2  y  j  0.5  0.0  0.5  0.0
3  y  k  0.0  0.0  0.0  1.0
4  z  k  0.5  0.0  0.0  0.5

Check below code:

import pandas as pd

df = pd.DataFrame({'col1':[1,1,1,2,3,3],'col2':['b','a','a','a','a','b']})

df['perc'] = df.groupby(['col1','col2'])['col2'].transform('count')/df.groupby('col1')['col2'].transform('count')

df.round(2).drop_duplicates()

Output:

You can use the following code:

df['asset wellness percentage'] = (df['asset wellness'].value_counts(normalize = True)) * 100

The above code will count the number of Poor, Warning and Good from your data and then normalize will divide the counts by the total number of rows.

You can keep that which is normalized data and most of the models works well on such data (linear models) but if you still want the percentage then multiply it by 100.

Try:

df.groupby(["ZIP", "YEAR"])["COLOR"].value_counts(normalize=True).mul(100).unstack("COLOR").fillna(0)

Outputs:

COLOR           0      1
ZIP   YEAR
11111 1990   50.0   50.0
      2000    0.0  100.0
22222 1990  100.0    0.0
      2000   50.0   50.0

You're already giving the barplot function the dataframe, so your x and y arguments can be strings matching columns in your frame. You're correct that the barplot will yield percentage on the y-axis with that estimator, and you can pass any numerical column as a dummy to y:

sns.barplot(x="Gender", y="Age", data=ds, estimator=lambda x: len(x) / len(ds) *100)

May be you can try with groupby and applying lambda to each group. Something like:

1. first apply groupby 'segment'
2. then for each group take the segment sum multiplied by 100
3. and divide by total sum of df

As below:

df.groupby('segment')['price'].apply(lambda g: sum(g)*100.0/df.price.sum())

Result:

segment
A    35.714286
B    40.476190
C    23.809524
Name: price, dtype: float64

groupby and apply transform to get the mean

df['num_true']=df.groupby('id').col1.transform('mean')



  id   col1  num_true
0   1   True      0.75
1   1   True      0.75
2   1  False      0.75
3   1   True      0.75
4   2  False      0.00
5   2  False      0.00

You want to make a cross-tabulation of two factors (col1 and col2) with the frequency normalized over each row. To do this you can use pd.crosstab() with normalize set to index:

>> df = pd.DataFrame({'col1': list('aaaaaabbb'), 'col2': list('xyxzzzxyx')})
>> pd.crosstab(df['col1'], df['col2'], normalize='index') * 100
col2    x           y           z
col1            
a       33.333333   16.666667   50.0
b       66.666667   33.333333   0.0

If you want to use multiple factors, just call crosstab with a list of factors:

>> df['col3'] = list('112231345')
>> pd.crosstab([df['col1'], df['col3']], df['col2'], normalize='index') * 100
        col2    x           y           z
col1    col3            
a       1       33.333333   33.333333   33.333333
        2       50.000000   0.000000    50.000000
        3       0.000000    0.000000    100.000000
b       3       100.000000  0.000000    0.000000
        4       0.000000    100.000000  0.000000
        5       100.000000  0.000000    0.000000

If you want to round up, just call round:

>> round(pd.crosstab(df['col1'], df['col2'], normalize='index') * 100, 2)
col2    x       y       z
col1            
a       33.33   16.67   50.0
b       66.67   33.33   0.0

Try:

promoted.department.value_counts()/train.department.value_counts()*100

It should give you the desired output:

Sales & Marketing    7.2030
Operations           9.0148
Technology          10.7593 
.....                 ...
Name: department, dtype: int64