Return subset of DataFrame in Python Pandas - pandas

Question

I have the following DataFrame:
import pandas as pd
# create simple dataset of people
data = {'Name': ["John", "Anna", "Peter", "Linda"],
'Location': ["New York", "Paris", "Berlin", "London"],
'Age': [24, 13, 53, 33]
}
data_pandas = pd.DataFrame(data)
# IPython.display allows "pretty printing" of dataframes
# in the Jupyter notebook
#display(data_pandas)
data_pandas
What is returned is the following DF:
Age Location Name
0 24 New York John
1 13 Paris Anna
2 53 Berlin Peter
3 33 London Linda
I then do this:
olderThan30 = data_pandas[data_pandas > 30]
olderThan30
And it returns the following:
Age Location Name
0 NaN New York John
1 NaN Paris Anna
2 53.0 Berlin Peter
3 33.0 London Linda
What I would like to return is only those that have the Age column greater than 30. Something like this:
Age Location Name
2 53.0 Berlin Peter
3 33.0 London Linda
How do I do that?

Answer 1

You need to pass the appropriate boolean condition to mask:
In [104]:
data_pandas[data_pandas['Age'] > 30]
Out[104]:
Age Location Name
2 53 Berlin Peter
3 33 London Linda
what you did was compare the entire df:
In [105]:
data_pandas > 30
Out[105]:
Age Location Name
0 False True True
1 False True True
2 True True True
3 True True True
this then masks the cells in the entire df, which is why you get NaN in the first 2 rows of age
Whilst masking just the col of interest:
In [106]:
data_pandas['Age'] > 30
Out[106]:
0 False
1 False
2 True
3 True
Name: Age, dtype: bool
when passed as a mask to a df, masks the rows
as #JonClements has suggested, you may feel more comfortable using query:
In [110]:
data_pandas.query('Age > 30')
Out[110]:
Age Location Name
2 53 Berlin Peter
3 33 London Linda
This has a dependency on numexpr library but this is normally installed correctly in my experience