pandas 将每一行与数据框中的所有行进行比较，并将结果保存在每行的列表中
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Compare each row with all rows in data frame and save results in list for each row
提问by pirr
I try to compare each row with all rows in a pandas dataframe with fuzzywuzzy.fuzzy.partial_ratio() >= 85and write the results in a list for each row.
我尝试将每行与Pandas数据fuzzywuzzy.fuzzy.partial_ratio() >= 85框中的所有行进行比较，并将结果写入每行的列表中。
Example:
例子：
df = pd.DataFrame({'id': [1, 2, 3, 4, 5, 6], 'name': ['dog', 'cat', 'mad cat', 'good dog', 'bad dog', 'chicken']})
I want to use a pandas function with the fuzzywuzzylibrary to get the result:
我想在fuzzywuzzy库中使用 pandas 函数来获得结果：
id name match_id_list
1 dog [4, 5]
2 cat [3, ]
3 mad cat [2, ]
4 good dog [1, 5]
5 bad dog [1, 4]
6 chicken []
But I don't understand how to get this.
但我不明白如何得到这个。
回答by IanS
The first step would be to find the indices that match the condition for a given name. Since partial_ratioonly takes strings, we applyit to the dataframe:
第一步是找到与给定 的条件匹配的索引name。由于partial_ratio只需要字符串，我们apply把它放到数据帧中：
name = 'dog'
df.apply(lambda row: (partial_ratio(row['name'], name) >= 85), axis=1)
We can then use enumerateand list comprehension to generate the list of trueindices in the boolean array:
然后我们可以使用enumerate和列表理解来生成true布尔数组中的索引列表：
matches = df.apply(lambda row: (partial_ratio(row['name'], name) >= 85), axis=1)
[i for i, x in enumerate(matches) if x]
Let's put all this inside a function:
让我们把所有这些都放在一个函数中：
def func(name):
matches = df.apply(lambda row: (partial_ratio(row['name'], name) >= 85), axis=1)
return [i for i, x in enumerate(matches) if x]
We can now apply the function to the entire dataframe:
我们现在可以将该函数应用于整个数据帧：
df.apply(lambda row: func(row['name']), axis=1)
