从包含字典列的csv构建pandas数据帧

Question

我有一个csv，它包含多个用一个dict填充的列。我想把这些dict提取出来，用它们的键生成列，用它们的值填充单元格，在缺少值的地方填充NaN。因此：

   id                            attributes
0   255RSSSTCHL-QLTDGLZD-BLK     {"color": "Black", "hardware": "Goldtone"}
1   C3ACCRDNFLP-QLTDS-S-BLK      {"size": "Small", "color": "Black"}

变成：

^{pr2}$

有几个列像'id'我希望在结果数据帧中保持不变，还有一些像'attributes'这样的列填充了dict，我想把这些列放大成列。为了便于说明，我把它们截短到上面的例子中。在

Answer 1

源数据源：

In [172]: df
Out[172]:
                         id                               attributes                       attr2
0  255RSSSTCHL-QLTDGLZD-BLK  {"color":"Black","hardware":"Goldtone"}  {"aaa":"aaa", "bbb":"bbb"}
1   C3ACCRDNFLP-QLTDS-S-BLK         {"size":"Small","color":"Black"}               {"ccc":"ccc"}

解决方案1:

^{pr2}$

解决方案2:感谢@DYZ for the hint：

import json

attr_cols = ['attributes','attr2']

def f(df, attr_col):
    return df.join(df.pop(attr_col) \
             .apply(lambda x: pd.Series(json.loads(x))))

for col in attr_cols:
    df = f(df, col)

结果：

In [175]: df
Out[175]:
                         id  color  hardware   size  aaa  bbb  ccc
0  255RSSSTCHL-QLTDGLZD-BLK  Black  Goldtone    NaN  aaa  bbb  NaN
1   C3ACCRDNFLP-QLTDS-S-BLK  Black       NaN  Small  NaN  NaN  ccc

<000行：<2000行：

In [198]: df = pd.concat([df] * 10**4, ignore_index=True)

In [199]: df.shape
Out[199]: (20000, 3)

In [201]: %paste
def f_ast(df, attr_col):
    return df.join(df.pop(attr_col) \
             .apply(lambda x: pd.Series(ast.literal_eval(x))))

def f_json(df, attr_col):
    return df.join(df.pop(attr_col) \
             .apply(lambda x: pd.Series(json.loads(x))))
##   End pasted text  

In [202]: %%timeit
     ...: for col in attr_cols:
     ...:     f_ast(df.copy(), col)
     ...:
1 loop, best of 3: 33.1 s per loop

In [203]:

In [203]: %%timeit
     ...: for col in attr_cols:
     ...:     f_json(df.copy(), col)
     ...:
1 loop, best of 3: 30 s per loop

In [204]: df.shape
Out[204]: (20000, 3)

Answer 2

可以使用converters选项将字符串解析嵌入到pd.read_csv调用中

import pandas as pd
from io import StringIO
from cytoolz.dicttoolz import merge as dmerge
from json import loads

txt = """id|attributes|attr2
255RSSSTCHL-QLTDGLZD-BLK|{"color":"Black","hardware":"Goldtone"}|{"aaa":"aaa", "bbb":"bbb"}
C3ACCRDNFLP-QLTDS-S-BLK|{"size":"Small","color":"Black"}|{"ccc":"ccc"}"""

converters = dict(attributes=loads, attr2=loads)

df = pd.read_csv(StringIO(txt), sep='|', index_col='id', converters=converters)
df

然后我们可以merge遍历每一行的字典并将其转换为pd.DataFrame。我将使用上面作为dmerge导入的cytoolz.dicttoolz.merge。在

^{pr2}$