using conditionals in lambda functions

Question

I was going to use a lambda function that returns 'Purchase' for not null values and returns 'No Purchase' else. I am not be sure which one to use. If I use the first one it works. However, I can't understand why I have to use the first one instead of the second one.

df['is_purchase'] = df.click_day.apply(lambda x: 'Purchase' if pd.notnull(x) else 'No Purchase')

df['is_purchase'] = df.click_day.apply(lambda x: 'Purchase' if pd.notnull(x)==True else 'No Purchase')

Can someone please explain why the first one is true?

Answer 1

Because if pd.notnull(x)==True is bad style, in any programming language. The ==True is already implied, so why include it?

Answer 2

If something has the logical value True , then the result of comparison it with True :

something == True

is True , too.

---

The opposite is true, too: If something == True , then something has the True logical value. So the form

if something:

means the same as

if something == True:

---

To understand why the shortest form is preferred, compare

if (a < 10) == True:

with

if a < 10:

Which one is more natural?

Answer 3

Here's a visual example of what has already been explained:

In [45]: s = pd.Series([True, False])

In [46]: s
Out[46]: 
0     True
1    False
dtype: bool

In [47]: s2 = s == True

In [48]: s2
Out[48]: 
0     True
1    False
dtype: bool

In [49]: s.equals(s2)
Out[49]: True

Saying if some_bool is the same as saying if some_bool == True , so there's no need to type it out explicitly.