从 Pandas 系列列表中获取唯一值
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Get unique values from pandas series of lists
提问by rohan
I have a column in DataFrame containing list of categories. For example:
我在 DataFrame 中有一个包含类别列表的列。例如：
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:
我正在尝试做两件事：
1) Get unique categories - My approach is have a empty set, iterate through series and append each list.
1）获取唯一类别 - 我的方法是有一个空集，遍历系列并附加每个列表。
my code:
我的代码：
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.
这为我提供了一组包含在列中所有列表中的独特类别。
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.
2）生成类别计数的饼图，每个餐厅可以属于多个类别。例如：餐厅 11 属于巴基斯坦、印度和清真类别。我的方法是再次迭代类别，再迭代一次系列以获得计数。
Are there simpler or elegant ways of doing this?
有没有更简单或优雅的方法来做到这一点？
Thanks in advance.
提前致谢。
回答by Scott Boston
Update using pandas 0.25.0+ with explode
使用 pandas 0.25.0+ 更新 explode
df['category'].explode().value_counts()
Output:
输出：
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:
并绘图：
df['category'].explode().value_counts().plot.pie(figsize=(8,8))
Output:
输出：
For older verions of pandas before 0.25.0 Try:
对于 0.25.0 之前的旧版Pandas，请尝试：
df['category'].apply(pd.Series).stack().value_counts()
Output:
输出：
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:
绘图：
df['category'].apply(pd.Series).stack().value_counts().plot.pie()
Output:
输出：
Per @coldspeed's comments
根据@coldspeed 的评论
from itertools import chain
from collections import Counter
pd.DataFrame.from_dict(Counter(chain(*df['category'])), orient='index').sort_values(0, ascending=False)
Output:
输出：
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
