How do I select all rows with a minimum value that meet a condition in another column pandas

Question

I would like to select all rows that have Points==0 and have the minimum value in the Day column from my dataset.

Dataframe

Points  Day  Name
55       0   Jon
0        7   Ron
0        8   Sam
44       6   Chris
0        7   Joan
49       2   Greg

What I'm hoping to get

Points  Day  Name
0        7   Ron
0        7   Joan

I have tired this code but I only get the first instance where this is true.

df1 = df.loc[[df.loc[df.points == 0, 'Day'].idxmin()]]

How do I get all rows?

Answer 1

You can use the min function to get the minimum value from the filtered dataset where Points == 0 and then use it to filter the whole dataset.

df[(df["Points"] == 0) & (df["Day"] == min(df[df["Points"] == 0]["Day"]))]

Now it works:

>>> df
   Points  Day
0      55    0
1       0    7
2       0    8
3      44    6
4       0    7
5      49    2

>>> df[(df["Points"] == 0) & (df["Day"] == min(df[df["Points"] == 0]["Day"]))]
   Points  Day
1       0    7
4       0    7

Answer 2

IIUC

df.query('Points==0').loc[lambda x : x['Day']==x['Day'].min()]
Out[207]: 
   Points  Day  Name
1       0    7   Ron
4       0    7  Joan

Answer 3

You can do it the following way:

df[(df['Points']==0) & (df['Day']==df[df['Points']==0]['Day'].min())]

The & means AND in pandas boolean indexing, if you want to use OR you can use |.