[英]Groupby and count columns with multiple values
Given this dataframe:鉴于此数据框:
df = pd.DataFrame({
"names": [["Kevin, Jack"], ["Antoine, Mary, Johanne, Iv"], ["Ali"]],
"commented": [["Kevin, Antoine, Iv"], ["Antoine, Mary, Ali"], ["Mary, Jack"]],
}, index=["1", "2", "3"])
that'll look like this:看起来像这样:
names commented
1 [Kevin, Jack] [Kevin, Antoine, Iv]
2 [Antoine, Mary, Johanne, Iv] [Antoine, Mary, Ali]
3 [Ali] [Mary, Jack]
I want to get a new dataframe that will count all comments all people made.我想获得一个新的数据框,它将计算所有人发表的所有评论。 Something like:
就像是:
Kevin![]() |
Jack![]() |
Antoine![]() |
Mary![]() |
Johanne![]() |
Iv ![]() |
Ali![]() |
|
---|---|---|---|---|---|---|---|
Kevin![]() |
1 ![]() |
0 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
0 ![]() |
Jack![]() |
1 ![]() |
0 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
0 ![]() |
Antoine![]() |
0 ![]() |
0 ![]() |
1 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
Mary![]() |
0 ![]() |
0 ![]() |
1 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
Johanne![]() |
0 ![]() |
0 ![]() |
1 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
Iv ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
1 ![]() |
Ali![]() |
0 ![]() |
1 ![]() |
0 ![]() |
1 ![]() |
0 ![]() |
0 ![]() |
0 ![]() |
This dataframe might be too small for it to make sense, but my original dataframe is 100k rows and there will be numbers higher than 0 and 1.这个数据框可能太小而无法理解,但我的原始数据框是 100k 行,并且会有高于 0 和 1 的数字。
I've looked at various options using pivot_table and several variations of group by but I can't seem to figure this out.我已经查看了使用 pivot_table 和 group by 的几种变体的各种选项,但我似乎无法弄清楚这一点。
df.pivot_table(index = 'names', columns= 'commented', aggfunc= 'count')
df.groupby('names').commented.apply(list).reset_index()
df.explode('names')['commented'].value_counts()
df.set_index('names').apply(pd.Series.explode).reset_index()
Almost all solutions I tried give me the error: TypeError: unhashable type: 'list'
我尝试过的几乎所有解决方案都给我错误:
TypeError: unhashable type: 'list'
You can try explode the list of strings to rows then use pandas.crosstab
您可以尝试将字符串列表分解为行,然后使用
pandas.crosstab
df = (df.explode(df.columns.tolist())
.apply(lambda col: col.str.split(', '))
.explode('names')
.explode('commented'))
out = pd.crosstab(df['names'], df['commented'])
print(df)
names commented
1 Kevin Kevin
1 Kevin Antoine
1 Kevin Iv
1 Jack Kevin
1 Jack Antoine
1 Jack Iv
2 Antoine Antoine
2 Antoine Mary
2 Antoine Ali
2 Mary Antoine
2 Mary Mary
2 Mary Ali
2 Johanne Antoine
2 Johanne Mary
2 Johanne Ali
2 Iv Antoine
2 Iv Mary
2 Iv Ali
3 Ali Mary
3 Ali Jack
print(out)
commented Ali Antoine Iv Jack Kevin Mary
names
Ali 0 0 0 1 0 1
Antoine 1 1 0 0 0 1
Iv 1 1 0 0 0 1
Jack 0 1 1 0 1 0
Johanne 1 1 0 0 0 1
Kevin 0 1 1 0 1 0
Mary 1 1 0 0 0 1
In your sample input, each element in the names
and commented
columns is an array with only 1 element (a string).在您的示例输入中,
names
和commented
列中的每个元素都是一个只有 1 个元素(字符串)的数组。 Not sure if that is the case with your real data.不确定您的真实数据是否如此。
You can split each string by the comma, and then explode and pivot the dataframe:您可以用逗号分割每个字符串,然后分解和旋转数据框:
split = lambda x: x[0].split(", ")
(
df.assign(
names=df["names"].apply(split),
commented=df["commented"].apply(split),
dummy=1
)
.explode("names")
.explode("commented")
.pivot_table(index="names", columns="commented", values="dummy", aggfunc="count", fill_value=0)
)
Here is another way using str.get_dummies()
这是使用
str.get_dummies()
的另一种方法
(df.assign(names = df['names'].str[0].str.split(', '))
.explode('names')
.set_index('names')
.squeeze()
.str[0]
.str.get_dummies(sep=', '))
Output:输出:
Ali Antoine Iv Jack Kevin Mary
names
Kevin 0 1 1 0 1 0
Jack 0 1 1 0 1 0
Antoine 1 1 0 0 0 1
Mary 1 1 0 0 0 1
Johanne 1 1 0 0 0 1
Iv 1 1 0 0 0 1
Ali 0 0 0 1 0 1
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