用多个方程过滤 python 中的 dataframe

Question

I have dataframe df that looks like this

    Lvl    Distance    iMap     Grp
0   37     63          A3       1
1   37     59          A9       1
2   37     54          A3       2
3   37     48          A4       2
...
190 37     27          A3       1
191 37     20          A3       4

I have 2 filters that I am trying to combine with "OR"

The first is m1

m1 = df[(df["Distance"]<55)].groupby('Grp').cumcount().eq(0)

you notice that m1 index starts with 2 (not 0)

>>> m1
2      True
3      False
4      False
       ...
187    True
188    False
189    False
190    False
191    False

also the 2nd filter

m2 = df['Distance'].gt(55)

you notice that m2 index starts with 0

>>> m2
0      True
1      True
2      False
3      False
4      False
       ...
187    False
188    False
189    False
190    False
191    False

When I try to combine both filters

df[m1 | m2]

and results

    Lvl    Distance    iMap     Grp
2   37     54          A3       2
19  37     41          A4       3
74  37     36          A3       1
187 37     29          A3       4

you can see that the first 2 records were not selected although their value is True in m2

but that value does not exist in m1

any idea how to fix this? so if any index is True it shows

Answer 1

IMO, m1 construction is incomplete. You need to create a mask that has the same length as df . To do that, one method is, via the isin() method, check if the indexes of the rows flagged as True in m1 exist in df.index . That way, you can make the True values in m1 correspond to df.index .

# first create groups as before
m1 = df[df['Distance']<55].groupby('Grp').cumcount().eq(0)
# filter for the index of the values flagged True in `m1` and 
# flag the rows of these indexes as True
m1 = df.index.isin(m1.index[m1])

# m2 construction as before
m2 = df['Distance'].gt(55)

# filter
df[m1|m2]

For the given input, the above code produces the following dataframe.

Answer 2

You can reindex m1 to make it conform to df , like so

m1 = df[(df["Distance"]<55)].groupby('Grp').cumcount().eq(0).reindex(df.index, fill_values=False)