I have a dataframe like this:
A B C
False True False
False False False
True True False
False False True
False True False
And I need to obtain something like this:
Total
B
NaN
A
C
B
I tried with:
df[["A", "B", "C"]].idxmax(1)
but is not returning the NaN, is returning A in place of NaN.
Thanks for any kind of help.
Try this, idxmax with series.where and df.any
df.idxmax(1).where(df.any(1))
#or np.where(df.any(1),df.idxmax(1),np.nan)
0 B
1 NaN
2 A
3 C
4 B
dtype: object
Simply check for any() being True
import pandas as pd
import io
import numpy as np
df = pd.read_csv(io.StringIO("""A B C
False True False
False False False
True True False
False False True
False True False"""), sep="\s+")
pd.Series(np.where(df.loc[:,["A","B","C"]].any(axis=1), df.loc[:,["A","B","C"]].idxmax(axis=1), np.nan), name="Total")
0 B
1 NaN
2 A
3 C
4 B
Name: Total, dtype: object
You could easily do this by checking the index of True. You have to be aware that index can throw a ValueError.
data = '''
A B C
False True False
False False False
True True False
False False True
False True False
'''
def safe_index(arr, val):
try: return arr.index(val)
except ValueError: return -1
def calculate_totals(data):
lines = data.strip().split('\n')
keys = lines[0].split()
return list(
map(lambda i: keys[i] if i != -1 else 'NaN',
[safe_index(line.split(), 'True') for line in lines[1:]]))
if __name__ == '__main__':
print('\n'.join(['Total'] + calculate_totals(data)))
