How to remove certain rows in dataframe using condtion with Pandas? [duplicate]

Question

user_id user_verified 1 False 2 False 3 False 4 True 5 False 6 True

How to remove all the 'False'values and keep 'True' values?

Does this answer your question? Deleting DataFrame row in Pandas based on column value — Abdul Aziz Barkat
– Abdul Aziz Barkat, Commented Jan 18, 2021 at 7:02

Synthaze · Accepted Answer · 2021-01-18 06:54:35Z

df = df[df['user_verified'] == True]

You can check the condition that way. This will keep the row if True in column 2.

You can also drop row based on bolean:

df.drop(df[df['user_verified'] == False].index, inplace=True)

Or even, to keep the True:

df = df[df.user_verified]

mustious · Accepted Answer · 2021-01-18 07:05:02Z

Assuming your data is in a dataframe as specified in a similar format below:

data = pd.DataFrame(zip(range(1,7), [False, False, False, False, True, False, True]), columns=['user_id', 'user_verified'])

You can simply use masking since the user_verified is boolean:

verified = data[data['user_verified']]

Bryan Rich · Accepted Answer · 2021-01-18 07:00:37Z

There are some ways to do it

df = df[df['user_verified'] == True]

Or you can also use

df = df.loc[df['user_verified'] == True]

Srijita Tiwari · Accepted Answer · 2021-01-18 07:32:50Z

Use:

df = df[df['user_verified'] == True]

or(without creating copy):

df = df.loc[df.user_verified,:]

4 Answers 4