Adding a new column to a Panda Data Frame

[rsherry8]

Please consider the following Python code:

import pandas as pd


data = {
  "l1": [50, 100, 150, 200, 250, -350, 450],
  "l2": [100, 120, 150, 190, 240, 200, 500],
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

I want to write code to add a new column to the data frame df where the new values are the larger of the values in l1 and l2. I can do this using a loop with the following code:

maxValue = []
limit = len ( df )
for i in range( limit ):
    if df.l1[i] > df.l2[i]:
        maxValue.append( df.l1[i] )
    else:
         maxValue.append( df.l2[i] )
df['l3'] = maxValue

However, I believe there should be a better way. I tired this:

df['l3'] = df['l1'] if df['l1'] > df['l2'] else df['l2']

This produced the following error message:

Error:
  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1478, in __nonzero__
    raise ValueError(

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

What is the right way to add the new column l3 to df?

[Caprone]

with numpy, like ifesle statment in R...

df['l3'] = np.where(df['l1'] > df['l2'], df['l1'],  df['l2'])

[jefsummers]

Alternatively

df['l3'] = df.apply(lambda row: max(row.l1,row.l2), axis=1)

Output:
	l1	l2	l3
0	50	100	100
1	100	120	120
2	150	150	150
3	200	190	200
4	250	240	250
5	-350	200	200
6	450	500	500