There are many methods for creating new columns in Pandas (I may have missed some in my examples so please let me know if there are others and I will include here) and I wanted to figure out when is the best time to use each method. Obviously some methods are better in certain situations compared to others but I want to evaluate it from a holistic view looking at efficiency, readability, and usefulness.

I'm primarily concerned with the first three but included other ways simply to show it's possible with different approaches. Here's your sample dataframe:

dataframe

df = pd.DataFrame({'a':[1,2,3],'b':[4,5,6]})


Most commonly known way is to name a new column such as df['c'] and use apply:

df['c']

apply

df['c'] = df['a'].apply(lambda x: x * 2)
df
a b c
0 1 4 2
1 2 5 4
2 3 6 6


Using assign can accomplish the same thing:

assign

df = df.assign(c = lambda x: x['a'] * 2)
df
a b c
0 1 4 2
1 2 5 4
2 3 6 6


Updated via @roganjosh:

df['c'] = df['a'] * 2
df
a b c
0 1 4 2
1 2 5 4
2 3 6 6


Using map (definitely not as efficient as apply):

map

apply

df['c'] = df['a'].map(lambda x: x * 2)
df
a b c
0 1 4 2
1 2 5 4
2 3 6 6


Creating a new pd.series and then concat to bring it into the dataframe:

pd.series

concat

dataframe

c = pd.Series(df['a'] * 2).rename("c")
df = pd.concat([df,c], axis = 1)
df
a b c
0 1 4 2
1 2 5 4
2 3 6 6


Using join:

join

df.join(c)
a b c
0 1 4 2
1 2 5 4
2 3 6 6


df['c'] = df['a'] * 2

lambda

apply

* 2

df['c'] = 2*df.a

assign

2 Answers
2

A succinct way would be:

df['c'] = 2 * df['a']


No need to compute the new column elementwise.

Why are you using lambda function?
You can easily achieve the above-mentioned task easily by

df['c'] = 2 * df['a']


This will not increase the overhead.

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