Deleting DF rows in Pandas based on different column values

Question

I have the following DF:

I want to remove all rows whereby TO and GP columns are negative and '0', and remove all rows whereby TTS and BMI columns are positive and '0'.

I have tried creating code like the following for each column however this is also deleting rows that are null which I do not want:

df [df ['GP'] >= 0]

What's the most efficient way to do this?

df[(df['GP'] >= 0) & (df['GP'] != np.NaN)] and get round the difficulty of & - its caught me out many a time — Paul Brennan
– Paul Brennan, Commented Nov 25, 2020 at 23:22

IoaTzimas · Accepted Answer · 2020-11-26 14:27:58Z

1

Try with df.drop:

df.drop(df[(df.TO <= 0)  | (df.GP <=0) | (df.TS >= 0) | (df.BMI >=0)].index, inplace=True)

edited Nov 26, 2020 at 14:27

answered Nov 25, 2020 at 23:22

IoaTzimas

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5 Comments

DSouthy Over a year ago

I have tried this but receiving the following error message: TypeError: Cannot perform 'rand_' with a dtyped [float64] array and scalar of type [bool]. Can you help?

IoaTzimas Over a year ago

I did some changes, please check again

DSouthy Over a year ago

So this logic only removes a row if both TO and GP are <=0. I want to remove a row if either TO and GP are <=0. From reading my original question I should have explained this clearer so my bad. Thanks for you help

DSouthy Over a year ago

So just changing '&' to '|' resolves this: df.drop(df[((df.TO <= 0) | (df.GP <=0)) | ((df.TS >= 0) | (df.BMI >=0))].index, inplace=True)

IoaTzimas Over a year ago

Great, i changed my answer too. Please accept the answer if your problem was resolved, Cheers

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