python pandas - input values into new column

Question

I have a small dataframe below of spending of 4 persons. There is an empty column called 'Grade'. I would like to rate those who spent more than $100 grade A, and grade B for those less than $100. What is the most efficient method of filling up column 'Grade', assuming it is a big dataframe?

import pandas as pd
df=pd.DataFrame({'Customer':['Bob','Ken','Steve','Joe'],
             'Spending':[130,22,313,46]})
df['Grade']=''

Possible duplicate of [Python Pandas: Add column based on other column](http://stackoverflow.com/questions/35424567/python-pandas-add-column-based-on-other-column) — Wondercricket, Dec 15 '16 at 13:57

score 1 · Answer 1 · answered Dec 15 '16 at 13:55

1

Fastest way to do that would be to use lambda function with an apply function.

df['grade'] = df.apply(lambda row: 'A' if row['Spending'] > 100 else 'B', axis = 1)

answered Dec 15 '16 at 13:55

Vikash Singh

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no, `apply` is very slow. Please check timings in my answer. – jezrael Dec 15 '16 at 14:10
@jezrael valid point. http://stackoverflow.com/questions/41166348/why-is-np-where-faster-than-apply – Vikash Singh Dec 15 '16 at 14:13

score 1 · Accepted Answer · answered Dec 15 '16 at 14:06

You can use numpy.where:

df['Grade']= np.where(df['Spending'] > 100 ,'A','B')
print (df)
  Customer  Spending Grade
0      Bob       130     A
1      Ken        22     B
2    Steve       313     A
3      Joe        46     B

Timings:

df=pd.DataFrame({'Customer':['Bob','Ken','Steve','Joe'],
             'Spending':[130,22,313,46]})

#[400000 rows x 4 columns]
df = pd.concat([df]*100000).reset_index(drop=True)

In [129]: %timeit df['Grade']= np.where(df['Spending'] > 100 ,'A','B')
10 loops, best of 3: 21.6 ms per loop

In [130]: %timeit df['grade'] = df.apply(lambda row: 'A' if row['Spending'] > 100 else 'B', axis = 1)
1 loop, best of 3: 7.08 s per loop

python pandas - input values into new column

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