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

2

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]

answered Jan 18, 2021 at 6:54

Synthaze

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Comments

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

1

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']]

answered Jan 18, 2021 at 7:05

mustious

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

0

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]

answered Jan 18, 2021 at 7:00

Bryan Rich

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

0

Use:

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

or(without creating copy):

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

answered Jan 18, 2021 at 7:32

Srijita Tiwari

1