enter code here
Gender Value Year Measure
Name
Ma girl 0 1997 Rank
Nabiha girl 1204 2014 Rank
Muad. boy 1749 2009 Rank
Zynah girl 4763 2017 Rank
Aathavi girl 0 2019 Rank
Kia-Marie girl 0 2017 Count
Tishaan boy 0 2006 Rank
Ryyan boy 0 2010 Count
Llyr boy 1852 2012 Rank
Abdirazaq boy 0 2017 Rank
The way that rank is calculated, I created a function so that those with s[‘Measure’] == "Count" and s[‘Value’] == 0 could be adjusted so they were ranked bottom not 0 as top value.
def adjust_rank(s):
if s['Value'] == 0 & s['Measure'] == "Count":
if s["Gender"] == "boy":
s['Rank'] = 5000 # Bottom possible rank
elif s["Gender"] == "girl":
s['Rank'] = 3459 # Bottom possible rank
else:
s['Rank'] = s['Value']
df['Rank'] = df.apply(adjust_rank)
Why do I get: KeyError: ‘Value’?
>Solution :
Use numpy.select:
m = (df['Value'] == 0) & (df['Measure'] == "Count")
m1 = df["Gender"] == "boy"
m2 = df["Gender"] == "girl"
df['Rank'] = np.select([m & m1, m & m2, m & ~(m1 | m2)],
[5000, 3459, df['Value']], default=None)
print (df)
Gender Value Year Measure Rank
Ma girl 0 1997 Rank None
Nabiha girl 1204 2014 Rank None
Muad. boy 1749 2009 Rank None
Zynah girl 4763 2017 Rank None
Aathavi girl 0 2019 Rank None
Kia-Marie girl 0 2017 Count 3459
Tishaan boy 0 2006 Rank None
Ryyan boy 0 2010 Count 5000
Llyr boy 1852 2012 Rank None
Abdirazaq boy 0 2017 Rank None
Or maybe need:
m = (df['Value'] == 0) & (df['Measure'] == "Count")
m1 = df["Gender"] == "boy"
m2 = df["Gender"] == "girl"
df['Rank'] = np.select([m & m1, m & m2], [5000, 3459], df['Value'])
print (df)
Gender Value Year Measure Rank
Ma girl 0 1997 Rank 0
Nabiha girl 1204 2014 Rank 1204
Muad. boy 1749 2009 Rank 1749
Zynah girl 4763 2017 Rank 4763
Aathavi girl 0 2019 Rank 0
Kia-Marie girl 0 2017 Count 3459
Tishaan boy 0 2006 Rank 0
Ryyan boy 0 2010 Count 5000
Llyr boy 1852 2012 Rank 1852
Abdirazaq boy 0 2017 Rank 0