Pandas groupby .all method-CodePudding

I am trying to understand how to use .all, for example:

import pandas as pd
df = pd.DataFrame({
    "user_id": [1,1,1,1,1,2,2,2,3,3,3,3],
    "score":   [1,2,3,4,5,3,4,5,5,6,7,8]
})

When I try:

df.groupby("user_id").all(lambda x: x["score"] > 2)

I get:

         score
user_id       
1         True
2         True
3         True

But I expect:

         score
user_id       
1         False  # Since for first group of users the score is not greater than 2 for all
2         True
3         True

In fact it doesn't even matter what value I pass instead of 2, the result DataFrame always has True for the score column.

Why do I get the result that I get? How can I get my expected result?

I looked at the documentation: https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.all.html, but it is very brief and did not help me.

CodePudding user response：

the line

df.groupby("user_id").all(lambda x: x["score"] > 2)

is not asking "are all datapoints larger than 2?", in reality is asking "are there datapoints?"

to ask what you really want you need to do the following:

df['score'].gt(2).groupby(df['user_id']).all()

Out

user_id
1    False
2     True
3     True

CodePudding user response：

groupby.all does not take any function as parameter. The only parameter (skipna) accepts a boolean and is used to change how NaN values are interpreted.

You probably want:

df['score'].gt(2).groupby(df['user_id']).all()

Which can also be written as:

df.assign(flag=df['score'].gt(2)).groupby('user_id')['flag'].all()