Remove records from a Dataframe with python pandas

Question

I have a csv file containing three columns. This link shows how my Dataframe is organized: https://github.com/phdvidal92/Files/blob/master/Data_Qnty.csv.

I would like to let just some records presented. For the SectionA, values equals to "Propose" and for the SectionB just "Prospecting".

I'm importing it with pandas read_csv

arqv2 = pd.read_csv('Data_Qnty.csv', skiprows = 2,
                    delimiter = ',', encoding = 'latin1')

After it, i'm trying to use drop

arqv2 = arqv2.drop(arqv2[(arqv2['SectionA'] != 'Prospecting') && (arqv2['SectionB'] != 'Propose')].index)

And it's not working. Do you know how can i achive my objective?

Thanks!

Text "it's not working" is the most useless information. Create short example data and expected result - and put it as text in question. — furas
– furas, Commented Apr 27, 2019 at 22:48
did you check arqv2.head() to see what you have ? If you skiprows=2 then you skip headers and your DF doesn't use names 'SectionA' 'SectionB'. Maybe you should read all and later drop rows to keep headers. — furas
– furas, Commented Apr 27, 2019 at 22:52

Scott Boston · Accepted Answer · 2019-04-27 22:54:48Z

2

One method is to use query:

df.query('SectionA == "Propose" and SectionB =="Prospecting"')

Another method is to use boolean indexing:

df[(df['SectionA']  == 'Propose') & (df['SectionB'] == 'Prospecting')]

answered Apr 27, 2019 at 22:54

Scott Boston

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Hildeberto · Accepted Answer · 2019-05-15 00:23:20Z

0

If you want change the same dataframe, use inplace=True as second argument of drop(), and use the & operator, not &&, like this:

arqv2.drop(
    arqv2[(arqv2['SectionA'] != 'Prospecting') & 
          (arqv2['SectionB'] != 'Propose')].index, 
    inplace=True)

edited May 15, 2019 at 0:23

answered Apr 27, 2019 at 23:09

Hildeberto

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2 Comments

Pedro Henrique Duarte Vidal Over a year ago

Thanks Hildeberto, do you know why we need to use inplace = True?

Hildeberto Over a year ago

Was not working because of "&&". Use inplace=True for performance and better memory usage. It's don't make a copy of dataframe, and affect the underlying data. It's a parameter for various functions (replace, dropna, etc.). If you can not use inplace=True, use an assignment instead, to make a dataframe copy.

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