Drop rows on multiple conditions in pandas dataframe

Question

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?

Dani Mesejo · Accepted Answer · 2022-06-16 15:16:25Z

63

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) and 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

edited Jun 16, 2022 at 15:16

answered Sep 22, 2018 at 13:03

Dani Mesejo

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2 Comments

Daniel Lavedonio de Lima Over a year ago

Just to complement this answer: & is the logical AND operator, | is the logical OR and ~ is the logical NOT

Yogesh Riyat Over a year ago

Why was the .index method needed at the end? I see that it doesn't work without it.

Mechi · Accepted Answer · 2022-03-31 07:38:37Z

19

Try to filter your df with loc. It's so powerful! The "~" means you want to keep those with the opposite 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)),:]

edited Mar 31, 2022 at 7:38

Mechi

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answered Sep 22, 2018 at 13:00

Charles R

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Comments

Saurabh · Accepted Answer · 2018-11-22 18:03:57Z

10

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))]

answered Nov 22, 2018 at 18:03

Saurabh

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1 Comment

Web Ster Over a year ago

The "|" operator is for "OR", while the "&" operator is for "AND" conditions. I think what is needed here is the & operator

zr0gravity7 · Accepted Answer · 2021-09-27 00:23:01Z

9

mask = df['Product_Code'].isin(['filter1', 'filter2', 'filter3'])
df = df[~mask]
df.head()

.isin() allows you to filter the entire dataframe based on multiple values in a series. This is the least amount of code to write, compared to other solutions that I know of.

Adding the ~ inside the column wise filter reverses the logic of isin().

edited Sep 27, 2021 at 0:23

zr0gravity7

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answered Sep 30, 2020 at 3:08

Alex Slessor

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Comments

xandermonkey · Accepted Answer · 2019-10-28 18:58:41Z

1

Put the location of row which you want remove at "location".

df = df.drop(['location' axix=1, inplace=True]

edited Oct 28, 2019 at 18:58

xandermonkey

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answered Oct 28, 2019 at 16:39

aliasgar paloda

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Comments

rachwa · Accepted Answer · 2022-07-30 14:53:37Z

0

You can also use query here:

In [4]: df.query('~(col_1 == 1 & col_2 == 0)')
Out[4]: 
   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

With ~ the query is negated, returning those observations where the condition col_1 == 1 & col_2 == 0 does not hold.

answered Jul 30, 2022 at 14:53

rachwa

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