CSV中的字典列表

Question

I have a CSV document which has a column where each cell contains a list of dicts. Any advice on how to extract that data while keeping it as lists of dicts would be appreciated. I've tried the usual json/pandas/csv read-ins and none of them seem to work properly (converts to strings/unicode, which isn't surprising but is still frustrating). Ultimately, I'd like the output to be a dataframe, where the header row is the keys and each following row is the data.

Sample CSV Section:

1    results

2    [{"y": 47, "type": "square"}, {"type": "square", "b": 49}, {"type": "square", "z": 29}, {"a": 69, "type": "square"}, {"type": "square", "x": 81}]

3    [{"type": "circle", "b": 90}, {"y": 12, "type": "circle"}, {"a": 78, "type": "circle"}, {"type": "circle", "c": 74}, {"type": "circle", "x": 14}, {"type": "circle", "z": 19}]

4    [{"type": "square", "b": 85}, {"type": "square", "x": 73}, {"type": "square", "c": 50}]

5    [{"type": "triangle", "c": 71}, {"type": "triangle", "z": 66}, {"type": "triangle", "x": 16}, {"type": "triangle", "b": 38}, {"y": 67, "type": "triangle"}, {"a": 80, "type": "triangle"}]

Sample Output:

  type      a   b   c   x   y   z
0 square    69  49  NaN 81  47  29
1 circle    78  90  74  14  12  19
2 square    NaN 85  50  73  NaN NaN
3 triangle  80  38  71  16  67  66

Answer 1

Evaluating each line in the file and doing some dictionary work gets you the desired result:

with open(filename) as fobj:
    next(fobj)  # skip first line with word `results`
    data = [eval(line) for line in fobj if line.strip()]
res = []
for entry in data:
    d = entry[0].copy()
    for x in entry[1:]:
        d.update(x)
    res.append(d)
df = pd.DataFrame(res)
df.reindex_axis(['type', 'a', 'b', 'c', 'x', 'y', 'z'], axis=1)
df

If you unwanted text on these line. You can remove everything out side the [] :

eval('[' + line.split('[')[-1].split(']')[0] + ']')

Alternatively, you can use a regular expression:

import re

eval(re.findall(r'\[.*?\]', line)[0])