Filter a pandas dataframe using values from a dict

Question

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.

Answer 1

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 

Answer 2

Here is a way to do it:

df.loc[df[filter_v.keys()].isin(filter_v.values()).all(axis=1), :] 

UPDATE:

With values being the same across columns you could then do something like this:

# Create your filtering function:  def filter_dict(df, dic):     return df[df[dic.keys()].apply(             lambda x: x.equals(pd.Series(dic.values(), index=x.index, name=x.name)), asix=1)]  # Use it on your DataFrame:  filter_dict(df1, filter_v) 

Which yields:

   A  B      C  D 3  1  0  right  3             

If it something that you do frequently you could go as far as to patch DataFrame for an easy access to this filter:

pd.DataFrame.filter_dict_ = filter_dict 

And then use this filter like this:

df1.filter_dict_(filter_v) 

Which would yield the same result.

BUT, it is not the right way to do it, clearly. I would use DSM's approach.