How to drop specific pandas rows by value

Question

Is there any way that I can drop the value if its index = column index.

I mean, this is my toy dataframe

d = {'Non': [1, 2,4,5,2,7], 'Schzerando': [3, 4,8,4,7,7], 'cc': [1,2,0.75,0.25,0.3,1]}
df = pd.DataFrame(data=d)
df

Then I just want to keep the row which df["cc"] == 1 and 2, like this

Toy dataframe to try.

Anyay, yes you want to filter rows where `df['cc'] in [1,2]`. Note: you can do this exactly with integer values, but using `in` on floats can give errors due to floating-point inexactness. — smci, Jul 17 '22 at 06:21
Duplicate of [Filter dataframe rows if value in column is in a set list of values](https://stackoverflow.com/questions/12065885/filter-dataframe-rows-if-value-in-column-is-in-a-set-list-of-values) -> [Use a list of values to select rows from a Pandas dataframe](https://stackoverflow.com/questions/12096252/use-a-list-of-values-to-select-rows-from-a-pandas-dataframe) — smci, Jul 17 '22 at 06:26
No, this operation is indeed called 'filtering', but ignore the actual [`filter()` function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.filter.html), it only filters by label, not by value; yes the name is historically confusing. Here, the [`.isin()` method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isin.html) is what you want. — smci, Jul 17 '22 at 06:29

Himanshu Poddar · Accepted Answer · 2022-07-17T06:35:23.317

1

You can filter out the rows by converting the cc column to int type then filter by applying mask.

df['cc'] = df['cc'].astype('Int64')
df = df[df['cc'] == 1 | df['cc'] == 2 | df['cc'] == 3]

or you can declare a list with all the values you want to filter for then use pandas isin

f_list = [1,2,3]
df[df['cc'].isin(f_list)]

edited Jul 17 '22 at 06:35

answered Jul 17 '22 at 06:25

Himanshu Poddar

Thanks, How to filter 2 or more. ```df = df[df['cc'] == 1 and 2 and 3] ```something like this LOLL? – Megan Jul 17 '22 at 06:27
@smci you don't have to worry about it if all you have are integers, that was added to convert string to int type just in case – Himanshu Poddar Jul 17 '22 at 22:05
@HimanshuPoddar: yes but if the input contains floats, this will unwantedly truncate them to the integer-floor value, then mismatch them against the ints-of-interest. I'm pointing out the danger. – smci Jul 17 '22 at 22:11
That's right, what do you suggest we should type cast to here? – Himanshu Poddar Jul 17 '22 at 22:12
Himanshu, it would be fundamentally dodgy programming to have mixed types (int and float) within a single column, yes we could write code that relies on that, but it would be very brittle. – smci Jul 22 '22 at 18:04
wdym, I didn't get you? – Himanshu Poddar Jul 22 '22 at 18:08

score 0 · Answer 2 · answered Jul 17 '22 at 06:26

0

df[(df['cc'] == 1) | (df['cc'] == 2)]

Output :

answered Jul 17 '22 at 06:26

ASLAN

2 Answers2