Pandas Data Frame filtering based off a column condition

Question

I am trying to remove all the rows from a data frame where a certain condition is met in one column.

Lets say my data frame is this:

index  'Value 1'   'Grade'
1         10         2170A
2         15         2170B
3         10         NCR2170B
4         20         NCR2170A
5         30         NCR2170B

I want the output after filtering to be this

    index  'Value 1'   'Grade'
2         15         2170B
3         10         NCR2170B
5         30         NCR2170B

I have tried many different variations of using list comprehension, looping through each row and evaluating. I can make it work when I use something along the lines of:

data_filtered = data[data['Grade'] == '2170B']

But this obviously misses entries like NCR2170B.

Whenever I try something like:

data_filtered = data['2170B' in data['Grade']]

I get Key Error: 'True'.

I feel like I am missing something very obvious here.

I have also tried using np.where but it just outputs an empty array.

akuiper · Accepted Answer · 2017-08-15 17:46:08Z

1

You can use str.contains to check if a string column contains a sub string:

df[df.Grade.str.contains('2170B')]

#index  Value 1    Grade
#1  2        15    2170B
#2  3        10 NCR2170B
#4  5        30 NCR2170B

answered Aug 15, 2017 at 17:46

akuiper

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1 Comment

awsmagala Over a year ago

Thank you so much I knew it was something simple that I wasn't aware of. Sorry I am new to python.

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