Count pattern frequency in pandas dataframe column that has multiple patterns

Question

I have the dataframe below:


details = {
    'container_id' : [1, 2, 3, 4, 5, 6 ],
    'container' : ['black box', 'orange box', 'blue box', 'black box','blue box', 'white box'],
    'fruits' : ['apples, black currant', 'oranges','peaches, oranges', 'apples','apples, peaches, oranges', 'black berries, peaches, oranges, apples'],
}
  
# creating a Dataframe object 

df = pd.DataFrame(details)

I want to find the frequency of each fruit separately on a list.

I tried this code

df['fruits'].str.split(expand=True).stack().value_counts()

but I get the black count 2 times instead of 1 for black currant and 1 for black berries.

Asked By: vartholomeos007

||

Source

Answer 1

Specify the comma separator followed by an optional space:

df['fruits'].str.split(',s?', expand=True).stack().value_counts()

OUTPUT:

apples           4
oranges          4
peaches          3
black currant    1
black berries    1
dtype: int64

Answered By: ScottC

Answer 2

You can do it like you did, but with specifying the delimiter. Be aware that when splitting the data, you get some leading whitespace unless your delimiter is a comma with a space. To be sure just use another step with str.strip.

df['fruits'].str.split(',', expand=False).explode().str.strip().value_counts()

your way (you can also use str.strip after the stack command if you want to)

df['fruits'].str.split(', ', expand=True).stack().value_counts()

Output:

apples           4
oranges          4
peaches          3
black currant    1
black berries    1
Name: fruits, dtype: int64

Answered By: Rabinzel

Count pattern frequency in pandas dataframe column that has multiple patterns

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