keep rows based on the values of a given column in pandas

Question

Given a data frame, I would like to keep the rows where a given column value does not match the given strings.

For instance, if the column 'En' does not match 'x1', I will keep those rows. I use the following code to do it.

df1 = df1.loc[df1['En'] != 'x1']

If instead of x1 only, there are x1and x2 need to be examined. In other words, I will only keep the rows whose 'En' column does not match either x1 or x2. What's the most efficient way to do that.

This is how I did

 df1 = df1.loc[df1['En'] != 'x1']
 df1 = df1.loc[df1['En'] != 'x2']

You can use isin with an invert ~ for multiple values: df[~df1['En'].isin(['x1','x2',other_values])] — anky
– anky, Commented Aug 22, 2020 at 5:55

Aagam Sheth · Accepted Answer · 2020-08-22 04:57:15Z

1

Use logical AND operator :

df1 = df1.loc[(df1['En'] != 'x1') & (df1['En'] != 'x2')]

answered Aug 22, 2020 at 4:57

Aagam Sheth

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Joe Ferndz Over a year ago

didn't notice that you had already answered when I posted mine. Sorry about that. Upvoted your answer.

Joe Ferndz · Accepted Answer · 2020-08-22 05:22:42Z

Are you looking for something like this?

import pandas as pd
df1 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
                    'b' : ['x', 'y', 'z', 'x', 'y', 'x', 'z'],
                    'c' : [1,2,3,4,5,6,7]})
print(df1)

df2 = df1.loc[(df1['b'] != 'x') & (df1['b'] != 'y') ]

print  (df2)

If df1 is :

       a  b  c
0    one  x  1
1    one  y  2
2    two  z  3
3  three  x  4
4    two  y  5
5    one  x  6
6    six  z  7

then df2 will be:

     a  b  c
2  two  z  3
6  six  z  7

An alternate way to do this is using query.

df2 = df1.query("b != 'x' & b != 'y'")

OR

df2 = df1.query("b != ['x','y']")

This will also give you the same result.

For more information about using some of these operators, see https://pandas.pydata.org/pandas-docs/version/0.13.1/indexing.html

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