Pandas select rows by multiple conditions on columns

Question

I would like to reduce my code. So instead of 2 lines I would like to select rows by 3 conditions on 2 columns. My DataFrame contains Country's population between 2000 and 2018 by granularity (Total, Female, Male, Urban, Rural)

    Zone        Granularity Year    Value
0   Afghanistan     Total   2000    20779.953
1   Afghanistan     Male    2000    10689.508
2   Afghanistan     Female  2000    10090.449
3   Afghanistan     Rural   2000    15657.474
4   Afghanistan     Urban   2000    4436.282
20909 Zimbabwe      Total   2018    14438.802
20910 Zimbabwe      Male    2018    6879.119
20911 Zimbabwe      Female  2018    7559.693
20912 Zimbabwe      Rural   2018    11465.748
20913 Zimbabwe      Urban   2018    5447.513

I would like all rows of the Year 2017 with granularity Total AND Urban. I tried something like this below but not working but each condition working well in separate code.

df.loc[(df['Granularity'].isin(['Total', 'Urban'])) & (df['Year'] == '2017')]

Thanks for tips to help

For sure df['Year'] == 2017 is working good but also df['Year'] == '2017' and the only way to have result is to use the quote as Int for df.loc[df['Year'] == '2017'] — Lilly_Co
– Lilly_Co, Commented Apr 26, 2022 at 18:26
Can you check if, 1. is the year column object type? 2. are there data points where both the conditions are true? — Vaishali
– Vaishali, Commented Apr 26, 2022 at 18:26
Year is an obejct so I need to use '' and yes for all my country I have 2 rows meeting the condition. Actually I got a result in 2 times with 2 dataframe. First df1= df[df['Granularity'].isin(['Total', 'Urban'])].reset_index(drop=True) and then df3= df1[df1['Year'] == '2017'].reset_index(drop=True) — Lilly_Co
– Lilly_Co, Commented Apr 26, 2022 at 18:32

mozway · Accepted Answer · 2022-04-26 18:28:02Z

5

Very likely, you're using the wrong type for the year. I imagine these are integers.

You should try:

df.loc[(df['Granularity'].isin(['Total', 'Urban'])) & df['Year'].eq(2017)]

output (for the Year 2018 as 2017 is missing from the provided data):

           Zone Granularity  Year      Value
20909  Zimbabwe       Total  2018  14438.802
20913  Zimbabwe       Urban  2018   5447.513

answered Apr 26, 2022 at 18:28

mozway

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3 Comments

Scott Boston Over a year ago

Likewise it is good idea to check for whitespace around strings df['Gradularity'].str.strip().isin([.....

Lilly_Co Over a year ago

So we can't associate a isin condition with a == operator ?

mozway Over a year ago

yes you can, eq and == are equivalent (in most cases), the issue was the type (you can have integers in a object column)

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