I came across Ken Wei's comment on this answer stating that using pandas cartesian_product()
while initializing the dataframe with np.array().T
is faster than itertools.product
for combining elements of two lists.
I'm confused as to how it would be used. Given two lists:
l1 = ['A', 'B']
l2 = [1, 2]
How would you arrive at this dataframe using his cartesian_product()
and np.array().T
?
+-----+-----+-----+
| | l1 | l2 |
+-----+-----+-----+
| 0 | A | 1 |
+-----+-----+-----+
| 1 | A | 2 |
+-----+-----+-----+
| 2 | B | 1 |
+-----+-----+-----+
| 3 | B | 2 |
+-----+-----+-----+
Which means, as stated, not unpacking but rather using np.array().T
:
>>> pd.DataFrame(np.array(pd.core.reshape.util.cartesian_product([l1, l2])).T)
0 1
0 A 1
1 A 2
2 B 1
3 B 2
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.