i have a dynamic situation, wherein i want to filter the data as per bellow logic.
dynamic_cols = ['A', 'B'. ...]
dynamic_values = [1,2,...]
data_frame.LOC[data_frame[dynamic_cols == dynamic_values ]]
i have used pandas, and numpy.
Any suggestion on this please?
Ex: In above case i want to filter the rows where in A=1 and Column B=2
Use np.logical_and + reduce of all masks created by list comprehension or create helper DataFrame and merge:
df = pd.DataFrame({
'A':[1,2,4,1,5,4],
'B':[2,8,9,2,2,3],
'C':[3,3,5,3,1,0],
})
print (df)
A B C
0 1 2 3
1 2 8 3
2 4 9 5
3 1 2 3
4 5 2 1
5 4 3 0
dynamic_cols = ['A','B','C']
dynamic_values = [1, 2, 3]
m = np.logical_and.reduce([df[a] == b for a, b in (zip(dynamic_cols, dynamic_values))])
df1 = df[m]
print (df1)
A B C
0 1 2 3
3 1 2 3
df2 = pd.DataFrame([dynamic_values], columns=dynamic_cols)
df1 = df.merge(df2)
print (df1)
A B C
0 1 2 3
3 1 2 3
pd.DataFrame([dynamic_values], columns=dynamic_cols) if i want to update the column D in the main df how can i do it now
df.loc[m, 'D'] = 10m = np.logical_and.reduce([df[a] == b for a, b in (zip(dynamic_cols, dynamic_values))]) is not possible use?