How to Calculate Percentage of Total in Dataframe Column Stacked on Top of Total of Column (Python)

Question

so let's say I have a dataframe:

data = [['1', 10,], ['2', 15], ['3', 14]] 
  
# Create the pandas DataFrame 
df = pd.DataFrame(data, columns = ['id', '# of Wagons'])

The output looks like:

  id  # of Wagons
0  1           10
1  2           15
2  3           14

How do I create percentages of the total while also keeping the total? If I use the.apply() function, I apply percentages to every value in the column, including the total, which I want to avoid doing. My preferred output is:

       id  # of Wagons  new_column
0       1           10  25.64%
1       2           15  38.46%
2       3           14  35.89%
Total                     39

Answer 1

You can add the percentage based on the '# of Wagons' like so:

 import numpy as np
 import pandas as pd
 from pandas import DataFrame   
 total = np.sum(df.loc[:,'# of Wagons':].values)
 df['percent'] = df.loc[:,'# of Wagons':].sum(axis=1)/total * 100
 df

And if you want to add a 'Total' row you can use this:

df.append(df.sum(numeric_only=True), ignore_index=True)

Answer 2

You can use pd.Series.div then use {:.precision%}.format to get values as percentage values.

df.assign(new_col = df['# of Wagons'].div(df['# of Wagons'].sum()).map('{:.2%}'.format))
  id  # of Wagons new_col
0  1           10  25.64%
1  2           15  38.46%
2  3           14  35.90%

Note:

• '{:.precision%}' is part of python's mini string language

Answer 3

We can do

df['New']=df['# of Wagons']/df['# of Wagons'].sum()
df=df.append(pd.Series(['Total',df['# of Wagons'].sum(),1],index=df.columns),ignore_index=True)
df
Out[158]: 
      id  # of Wagons       New
0      1           10  0.256410
1      2           15  0.384615
2      3           14  0.358974
3  Total           39  1.000000

Answer 4

You can do something like this:

total = sum(df['# of Wagons'].values)
df["percentage"] = df['# of Wagons'].apply(lambda x: "{:.2f}%".format((x/total)*100))
print(df)
#  id  # of Wagons percentage
#0  1           10     25.64%
#1  2           15     38.46%
#2  3           14     35.90%