Get count of values in a column and show their percentage in a plot

Question

How can I count the frequency of values ​​in a column and calculate the percentage relative to the total?

I got a dataframe:

   range
0   G-L
1   M-R
2   G-L
3   M-R
4   A-F
5   S-Z
6   A-F
..   ..
..   ..

after df.range.value_counts() i get this:

A-F    1882
G-L    3096
M-R    3830
S-Z    1017

now i want to get the percentage of each range in comparison to the total sum and show this in a plot, where the x-axis got the ranges(AF; GL;...) und the y-axis shows the percentage of these ranges.

Answer 1

Assume this is your DataFrame :

data = {'labels': ["A-F", "G-L", "M-R", "S-Z"], 'count':[1882, 3096, 3830, 1017]}
df = pd.DataFrame.from_dict(data)

print(df)

  labels  count
0    A-F   1882
1    G-L   3096
2    M-R   3830
3    S-Z   1017

Now you have to calculate the percentage of each row:

df['percentage'] = (df['count'] / df['count'].sum()) * 100

print(df)

  labels  count  percentage
0    A-F   1882   19.155216
1    G-L   3096   31.511450
2    M-R   3830   38.982188
3    S-Z   1017   10.351145

and then plot the labels vs the percentage using the df.plot() function and specifying its kind , which I assume is a barplot.

df.plot(kind='bar', x='labels', y='percentage')

This will produce the following plot:

Edit: The value_counts() method returns a pd.Series() object. To plot it you can run the following line:

df.range.value_counts(normalize=True).plot(kind='bar')