[FIXED] Filter a pandas dataframe using values from a dict

Issue

I need to filter a data frame with a dict, constructed with the key being the column name and the value being the value that I want to filter:

filter_v = {'A':1, 'B':0, 'C':'This is right'}
# this would be the normal approach
df[(df['A'] == 1) & (df['B'] ==0)& (df['C'] == 'This is right')]

But I want to do something on the lines

for column, value in filter_v.items():
    df[df[column] == value]

but this will filter the data frame several times, one value at a time, and not apply all filters at the same time. Is there a way to do it programmatically?

EDIT: an example:

df1 = pd.DataFrame({'A':[1,0,1,1, np.nan], 'B':[1,1,1,0,1], 'C':['right','right','wrong','right', 'right'],'D':[1,2,2,3,4]})
filter_v = {'A':1, 'B':0, 'C':'right'}
df1.loc[df1[filter_v.keys()].isin(filter_v.values()).all(axis=1), :]

gives

    A   B   C   D
0   1   1   right   1
1   0   1   right   2
3   1   0   right   3

but the expected result was

    A   B   C   D
3   1   0   right   3

only the last one should be selected.

Solution

IIUC, you should be able to do something like this:

>>> df1.loc[(df1[list(filter_v)] == pd.Series(filter_v)).all(axis=1)]
   A  B      C  D
3  1  0  right  3

---

This works by making a Series to compare against:

>>> pd.Series(filter_v)
A        1
B        0
C    right
dtype: object

Selecting the corresponding part of df1:

>>> df1[list(filter_v)]
    A      C  B
0   1  right  1
1   0  right  1
2   1  wrong  1
3   1  right  0
4 NaN  right  1

Finding where they match:

>>> df1[list(filter_v)] == pd.Series(filter_v)
       A      B      C
0   True  False   True
1  False  False   True
2   True  False  False
3   True   True   True
4  False  False   True

Finding where they all match:

>>> (df1[list(filter_v)] == pd.Series(filter_v)).all(axis=1)
0    False
1    False
2    False
3     True
4    False
dtype: bool

And finally using this to index into df1:

>>> df1.loc[(df1[list(filter_v)] == pd.Series(filter_v)).all(axis=1)]
   A  B      C  D
3  1  0  right  3

Answered By - DSM