i have toy_df
toy_df = pd.DataFrame(['animal':['cat','cat','cat','dog']
animal
0 cat
1 cat
2 cat
3 dog
i'm using toy_df_4 = toy_df[toy_df.animal == 'dog']
animal
3 dog
how i can drop certain daaframe toy_df_4 from general toy_df
i have tried toy df - toy_df_4 but this error
TypeError: unsupported operand type(s) for -: 'str' and 'str'
this dataframes are toy so in real dataset i have dataframes with many conditions
Simpliest here is invert condition for get not matched rows by dog:
toy_df = pd.DataFrame({'animal':['cat','cat','cat','dog']})
toy_df_5 = toy_df[toy_df.animal != 'dog']
print (toy_df_5)
animal
0 cat
1 cat
2 cat
But if really need drop by index value of filtered DataFrame:
toy_df_4 = toy_df[toy_df.animal == 'dog']
toy_df_5 = toy_df.drop(toy_df_4.index)
print (toy_df_5)
animal
0 cat
1 cat
2 cat
toy_df = pd.DataFrame({'animal':['cat','cat','cat','dog']})
toy_df4 = toy_df[toy_df.animal=='dog']
df = toy_df.merge(toy_df_4, how='outer', indicator=True).loc[lambda x: x['_merge']=='left_only'].reset_index(drop=True)
df = df.drop('_merge', axis=1)
output:
animal
0 cat
1 cat
2 cat
Another approach is to use pandas.DataFrame.isin
toy_df = pd.DataFrame(['animal':['cat','cat','cat','dog']
toy_df_4 = toy_df[toy_df.animal == 'dog']
toy_df = toy_df[~toy_df.isin(toy_df_4)].dropna()
print(toy_df)
animal
0 cat
1 cat
2 cat
