将颜色的字符串表示形式转换回列表

Question

I had data where one column was a list of numbers, but when saving to a CSV, I guess this got stored as a string. I want to convert this list of strings back to a list of lists.

So here's what my data looks like now:

import pandas as pd 
from ast import literal_eval

colors = ["(120, 120, 80)", "(90, 10, 100)"]
names = ["name1", "name2"]
data = {
    "colors":colors,
    "names":names
}
df = pd.DataFrame(data)

And from reading Stackoverflow, I tried the literal eval method but it did not work:

try:
  df['colors'] = literal_eval( df['colors'].tolist() )
except ValueError as e:
  print(e)

I get a malformed string error.

Answer 1

您可以为每个列执行以下操作：

col = [int(val) for val in colors.replace("(","").replace(")","").split(",")]

Answer 2

Using literal_eval() is a good approach. The issue is that it needs to be applied to each sub-list (string) individually. A pythonic approach would be to use a list comprehension as follows:

>>> from ast import literal_eval
>>> colors = ["(120, 120, 80)", "(90, 10, 100)"]
>>> colors = [literal_eval(x) for x in colors]
>>> colors
[(120, 120, 80), (90, 10, 100)]

To get a list of list instead of a list of tuple , you could use:

>>> from ast import literal_eval
>>> colors = ["(120, 120, 80)", "(90, 10, 100)"]
>>> colors = [list(literal_eval(x)) for x in colors]
>>> colors
[[120, 120, 80], [90, 10, 100]]

The Python documentation for ast.literal_eval(node_or_string) states:

Safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None.

This can be used for safely evaluating strings containing Python values from untrusted sources without the need to parse the values oneself. It is not capable of evaluating arbitrarily complex expressions, for example involving operators or indexing.

Answer 3

Using re.findall to extract digits and apply over the series:

import re
df['colors'].apply(lambda str : [int(s) for s in re.findall(r'\d+',str) ]).tolist()

#  [[120, 120, 80], [90, 10, 100]]