lambda x:

lambda x:

lambda x: mapping[x]

explain lambda x: (len(x), x)

map(lambda x: x**x,num)

df.apply(lambda X: )

.apply(lambda x:

.apply(lambda x: x[-1])

lambda x: x*2 if x==2

lambda x: float(x[1:-1])

pandas apply(lambda x: )

lambda x: %d % ', '.join(x) for integers

(filter(lambda x: x % 2 == 0, list1))

list(map(lambda x: x**3, a))

(lambda x: (x + 3) * 5 / 2)(9)

print list(map(lambda x:x+x,x))

diabetesDF.apply(lambda x: x - diabetesDF.mean(axis = 1))

df_analise['custos_b_light'].apply(lambda x: x[6])

set(map(lambda x: (x[1], x[2]), wall_info))

apply(lambda x: list(map