Hello, I am an introvert who has been practicing split, for, and if moon recently.
df = pd.DataFrame({"idx5":[ "ex1.x.1",
"ex1.x",
"ex2.x.2",
"ex2.x.3",
"ex1.x.2",
"ex2.x",
"ex2.x.4",
"ex2.x.5",
"ex1.x.3",
"ex3.x",
"ex2.x.6",
"ex2.x.7",
"ex1.x.4",
"ex4.x",
"ex2.x.8",
"ex2.x.9",
"ex1.x.5",
"ex5.x",
"ex2.x.10",
"ex2.x.11",
"ex1.x.6",
"ex6.x",
"ex2.x.12",
"ex2.x.13",
"ex1.x.7",
]})
for i in range(len(df)-1) :
df.at[i+1, "idx6"], df.at[i+1, "idx7"], df.at[i+1, "idx8"] = df.at[i+1, "idx5"].split(".")
ValueError: not enough values to unpack (expected 3, got 2)
The error code above is coming out. Is there a way to split even if the number of " is different?
Ultimately, I want to make idx6, idx7, idx8 with idx5 only as below. It doesn't matter if you put any characters in the empty space of idx8.!
idx5idx6idx7idx8
ex1.x.1 ex1 x 1
ex1.x ex1 x
ex2.x.2 ex2 x 2
ex2.x.3 ex2 x 3
ex1.x.2 ex1 x 2
ex2.x ex2 x
ex2.x.4 ex2 x 4
ex2.x.5 ex2 x 5
ex1.x.3 ex1 x 3
ex3.x ex3 x
ex2.x.6 ex2 x 6
ex2.x.7 ex2 x 7
ex1.x.4 ex1 x 4
ex4.x ex4 x
ex2.x.8 ex2 x 8
ex2.x.9 ex2 x 9
ex1.x.5 ex1 x 5
ex5.x ex5 x
ex2.x.10 ex2 x 10
ex2.x.11 ex2 x 11
ex1.x.6 ex1 x 6
ex6.x ex6 x
ex2.x.12 ex2 x 12
ex2.x.13 ex2 x 13
ex1.x.7 ex1 x 7
Python 3.8.5 (tags/v3.8.5:580fbb0, Jul 20 2020, 15:57:54) [MSC v.1924 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license()" for more information.
>>> import pandas as pd
>>> df = pd.DataFrame({"idx5":[ "ex1.x.1",
"ex1.x",
"ex2.x.2",
"ex2.x.3",
"ex1.x.2",
"ex2.x",
"ex2.x.4",
"ex2.x.5",
"ex1.x.3",
"ex3.x",
"ex2.x.6",
"ex2.x.7",
"ex1.x.4",
"ex4.x",
"ex2.x.8",
"ex2.x.9",
"ex1.x.5",
"ex5.x",
"ex2.x.10",
"ex2.x.11",
"ex1.x.6",
"ex6.x",
"ex2.x.12",
"ex2.x.13",
"ex1.x.7",
]})
>>> df
idx5
0 ex1.x.1
1 ex1.x
2 ex2.x.2
3 ex2.x.3
4 ex1.x.2
5 ex2.x
6 ex2.x.4
7 ex2.x.5
8 ex1.x.3
9 ex3.x
10 ex2.x.6
11 ex2.x.7
12 ex1.x.4
13 ex4.x
14 ex2.x.8
15 ex2.x.9
16 ex1.x.5
17 ex5.x
18 ex2.x.10
19 ex2.x.11
20 ex1.x.6
21 ex6.x
22 ex2.x.12
23 ex2.x.13
24 ex1.x.7
>>> df[["idx6", "idx7", "idx9"]] = df["idx5"].str.split(".", 3, expand=True)
>>> df
idx5 idx6 idx7 idx9
0 ex1.x.1 ex1 x 1
1 ex1.x ex1 x None
2 ex2.x.2 ex2 x 2
3 ex2.x.3 ex2 x 3
4 ex1.x.2 ex1 x 2
5 ex2.x ex2 x None
6 ex2.x.4 ex2 x 4
7 ex2.x.5 ex2 x 5
8 ex1.x.3 ex1 x 3
9 ex3.x ex3 x None
10 ex2.x.6 ex2 x 6
11 ex2.x.7 ex2 x 7
12 ex1.x.4 ex1 x 4
13 ex4.x ex4 x None
14 ex2.x.8 ex2 x 8
15 ex2.x.9 ex2 x 9
16 ex1.x.5 ex1 x 5
17 ex5.x ex5 x None
18 ex2.x.10 ex2 x 10
19 ex2.x.11 ex2 x 11
20 ex1.x.6 ex1 x 6
21 ex6.x ex6 x None
22 ex2.x.12 ex2 x 12
23 ex2.x.13 ex2 x 13
24 ex1.x.7 ex1 x 7
>>> df = df.fillna("-")
>>> df
idx5 idx6 idx7 idx9
0 ex1.x.1 ex1 x 1
1 ex1.x ex1 x -
2 ex2.x.2 ex2 x 2
3 ex2.x.3 ex2 x 3
4 ex1.x.2 ex1 x 2
5 ex2.x ex2 x -
6 ex2.x.4 ex2 x 4
7 ex2.x.5 ex2 x 5
8 ex1.x.3 ex1 x 3
9 ex3.x ex3 x -
10 ex2.x.6 ex2 x 6
11 ex2.x.7 ex2 x 7
12 ex1.x.4 ex1 x 4
13 ex4.x ex4 x -
14 ex2.x.8 ex2 x 8
15 ex2.x.9 ex2 x 9
16 ex1.x.5 ex1 x 5
17 ex5.x ex5 x -
18 ex2.x.10 ex2 x 10
19 ex2.x.11 ex2 x 11
20 ex1.x.6 ex1 x 6
21 ex6.x ex6 x -
22 ex2.x.12 ex2 x 12
23 ex2.x.13 ex2 x 13
24 ex1.x.7 ex1 x 7
>>>
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