My df has 3 columns
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0), "col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0), "col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
I want to drop rows where df.col_1 is 1.0 and df.col_2 is 0.0. So, I would get:
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 0.0, 1.0), "col_2": (0.0, 0.24, 1.0, 0.22, 3.11), "col_3": ("Mon", "Tue", "Thu", "Mon", "Tue")})
I tried:
df_new = df.drop[df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index]
It gives me the error:
'method' object is not subscriptable
Any idea how to solve the above problem?
drop is a method, you are calling it using [], that is why it gives you:
'method' object is not subscriptable
change to () (a normal method call) an it should work:
import pandas as pd df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0), "col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0), "col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")}) df_new = df.drop(df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index) print(df_new)
Output
col_1 col_2 col_3 0 0.0 0.00 Mon 1 0.0 0.24 Tue 2 1.0 1.00 Thu 4 0.0 0.22 Mon 5 1.0 3.11 Tue
Try to filter your df with loc. It's so powerfull. The "~" means you want the opposit of your condition. The ":" means you want to keep all the columns
df = df.loc[~((df['col_1'] == 1.0) & (df['col_2'] == 0.0)),:]
You can use or (|) operator for this , Refer this link for it pandas: multiple conditions while indexing data frame - unexpected behavior
i.e dropping rows where both conditions are met
df = df.loc[~((df['col_1']==1) | (df['col_2']==0))]
