Get unique values from pandas series of lists

问题:

I have a column in DataFrame containing list of categories. For example:

0                                                    [Pizza]
1                                 [Mexican, Bars, Nightlife]
2                                  [American, New, Barbeque]
3                                                     [Thai]
4          [Desserts, Asian, Fusion, Mexican, Hawaiian, F...
6                                           [Thai, Barbeque]
7                           [Asian, Fusion, Korean, Mexican]
8          [Barbeque, Bars, Pubs, American, Traditional, ...
9                       [Diners, Burgers, Breakfast, Brunch]
11                                [Pakistani, Halal, Indian]

I am attempting to do two things:

1) Get unique categories - My approach is have a empty set, iterate through series and append each list.

my code:

unique_categories = {'Pizza'}
for lst in restaurant_review_df['categories_arr']:
    unique_categories = unique_categories | set(lst)

This give me a set of unique categories contained in all the lists in the column.

2) Generate pie plot of category counts and each restaurant can belong to multiple categories. For example: restaurant 11 belongs to Pakistani, Indian and Halal categories. My approach is again iterate through categories and one more iteration through series to get counts.

Are there simpler or elegant ways of doing this?

Thanks in advance.

回答1:

Update using pandas 0.25.0+ with explode

df['category'].explode().value_counts()

Output:

Barbeque       3
Mexican        3
Fusion         2
Thai           2
American       2
Bars           2
Asian          2
Hawaiian       1
New            1
Brunch         1
Pizza          1
Traditional    1
Pubs           1
Korean         1
Pakistani      1
Burgers        1
Diners         1
Indian         1
Desserts       1
Halal          1
Nightlife      1
Breakfast      1
Name: Places, dtype: int64

And with plotting:

df['category'].explode().value_counts().plot.pie(figsize=(8,8))

Output:

---

For older verions of pandas before 0.25.0 Try:

df['category'].apply(pd.Series).stack().value_counts()

Output:

Mexican        3
Barbeque       3
Thai           2
Fusion         2
American       2
Bars           2
Asian          2
Pubs           1
Burgers        1
Traditional    1
Brunch         1
Indian         1
Korean         1
Halal          1
Pakistani      1
Hawaiian       1
Diners         1
Pizza          1
Nightlife      1
New            1
Desserts       1
Breakfast      1
dtype: int64

With plotting:

df['category'].apply(pd.Series).stack().value_counts().plot.pie()

Output:

Per @coldspeed's comments

from itertools import chain
from collections import Counter

pd.DataFrame.from_dict(Counter(chain(*df['category'])), orient='index').sort_values(0, ascending=False)

Output:

Barbeque     3
Mexican      3
Bars         2
American     2
Thai         2
Asian        2
Fusion       2
Pizza        1
Diners       1
Halal        1
Pakistani    1
Brunch       1
Breakfast    1
Burgers      1
Hawaiian     1
Traditional  1
Pubs         1
Korean       1
Desserts     1
New          1
Nightlife    1
Indian       1