[FIXED] Filter a pandas DataFrame by a dict

Issue

Assume I have a DataFrame like this:

       Num1   Num2 
1       1      0
2       3      2
3       5      4
4       7      6
5       9      8

And I have a dictionary like this:

d = {
    
    "Num1": 2,
    "Num2": 5
}

I want to return the values from the DataFrame from each column that matches the key and the values greater than or equal to the value in the key.

My result should look like this:

       Num1   Num2 
1       nan    nan
2       3      nan
3       5      nan
4       7      6
5       9      8

How might I do this?

Solution

The same idea of @db's answer using df.where instead of df.mask to keep the logic, i.e use ge rather than lt:

>>> df.where(df >= d)  # or df.where(df.ge(d))
   Num1  Num2
0   NaN   NaN
1   3.0   NaN
2   5.0   NaN
3   7.0   6.0
4   9.0   8.0

Edit

To go further, if you want to use a different operator, you can change the input as follows:

filters = [  # col, op, val
    ('Num1', '>=', 2),
    ('Num2', '<', 5)
]

m = pd.concat([df.eval(f'{col} {op} {val}').rename(col)
               for col, op, val in filters], axis=1)
out = df.where(m)
print(out)

# Output
   Num1  Num2
0   NaN   0.0
1   3.0   2.0
2   5.0   4.0
3   7.0   NaN
4   9.0   NaN

Alternative:

filters = {
    'Num1': '>= 2',
    'Num2': '< 5'
}

m = pd.concat([df.eval(f'{col} {cond}').rename(col)
               for col, cond in filters.items()], axis=1)
out = df.where(m)

Answered By - Corralien