Plotting frequency of elements within a list of lists

Question

I have a list of lists:

my_list= [['UV'],
     ['SB'],
     ['NMR'],
     ['ISSN'],
     ['UK', 'USA'],
     ['MT'],
     ['UK'],
     ['UK'],
     ['ESP'],
     ['UK'],
     ['UK'],
     ['UK'],
     ['UK'],
     ['UK'],
     ['UK']]

that I would like to plot in terms of frequency (from the most frequent term to the less frequent).

I am finding some issue in counting the items. What I first did is to flatten the list of lists:

flattened = [] 
for sublist in my_list: 
    for val in sublist: 
        flattened.append(val) 

Then I tried to count items it

from collections import Counter
import pandas as pd

counts = Counter(flattened)
df_ver = pd.DataFrame.from_dict(counts, orient='index')
df_ver.plot(kind='bar')

However it does not work. Also it should be not sorted, I guess.

Answer 1

Let's try with pure Python:

counts = {}

for countries in my_list:
    for country in countries:
        counts[country] = counts.get(country,0) +1

sorted_counts = sorted(counts.items(), key=lambda i: (-i[1],i[0])) # sort by count and alphabetically if draw
# ktop = 10
# sorted_counts = sorted_counts[:ktop]
countries, counts = list(zip(*sorted_counts))

plt.bar(countries, counts);

Answer 2

Since you use Counter:

from matplotlib import pyplot as plt
from collections import Counter
from itertools import chain
    
my_list= [['UV'],
     ['SB'],
     ['NMR'],
     ['ISSN'],
     ['UK', 'USA'],
     ['MT'],
     ['UK'],
     ['UK'],
     ['ESP'],
     ['UK'],
     ['UK'],
     ['UK'],
     ['UK'],
     ['UK'],
     ['UK']]

counts = Counter(chain.from_iterable(my_list))
plt.bar(*zip(*counts.most_common()))
plt.show()

Output:

Answer 3

Another option:

df_ver =  df_ver.sort_values(0, ascending = False)
df_ver.plot(kind = "bar", legend = False)