The syntax you're looking for:
lambda x: True if x % 2 == 0 else False
But you can't use print or raise in a lambda.
The syntax you're looking for:
lambda x: True if x % 2 == 0 else False
But you can't use print or raise in a lambda.
why don't you just define a function?
def f(x):
if x == 2:
print(x)
else:
raise ValueError
there really is no justification to use lambda in this case.
can I use an if statement with a lambda function?
Using the if, else statement with reduce() and lambda()
pandas - Python lambda function if else condition - Stack Overflow
python - Lambda including if...elif...else - Stack Overflow
Videos
writing an if statement with a lambda function:
I'm trying to filter a map in spark using an if statement but get a syntax error. I have not been able to find the error. Can you guys tell me what I am doing wrong here?
full_count_with0val = full_rdd.map(lambda x: json.loads(x[1])).flatMap(lambda x: x['exposures']).map(lambda x: x['pdd_list'] if len(x['pdd_list'])==0).take(5)
Nest if .. elses:
lambda x: x*10 if x<2 else (x**2 if x<4 else x+10)
I do not recommend the use of apply here: it should be avoided if there are better alternatives.
For example, if you are performing the following operation on a Series:
if cond1:
exp1
elif cond2:
exp2
else:
exp3
This is usually a good use case for np.where or np.select.
numpy.where
The if else chain above can be written using
np.where(cond1, exp1, np.where(cond2, exp2, ...))
np.where allows nesting. With one level of nesting, your problem can be solved with,
df['three'] = (
np.where(
df['one'] < 2,
df['one'] * 10,
np.where(df['one'] < 4, df['one'] ** 2, df['one'] + 10))
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
numpy.select
Allows for flexible syntax and is easily extensible. It follows the form,
np.select([cond1, cond2, ...], [exp1, exp2, ...])
Or, in this case,
np.select([cond1, cond2], [exp1, exp2], default=exp3)
df['three'] = (
np.select(
condlist=[df['one'] < 2, df['one'] < 4],
choicelist=[df['one'] * 10, df['one'] ** 2],
default=df['one'] + 10))
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
and/or (similar to the if/else)
Similar to if-else, requires the lambda:
df['three'] = df["one"].apply(
lambda x: (x < 2 and x * 10) or (x < 4 and x ** 2) or x + 10)
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
List Comprehension
Loopy solution that is still faster than apply.
df['three'] = [x*10 if x<2 else (x**2 if x<4 else x+10) for x in df['one']]
# df['three'] = [
# (x < 2 and x * 10) or (x < 4 and x ** 2) or x + 10) for x in df['one']
# ]
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15