Question

我是Python的新手，对于我的项目之一，我需要将csv转换为嵌套的Json。在网上搜索时，我发现pandas在这种情况下很有帮助。我遵循了Convert CSV Data to Nested JSON in Python中给出的方法但是我收到了keyError异常KeyError: 'state'

df info
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 3 columns):
country    4 non-null object
 state     4 non-null object
 city      4 non-null object
dtypes: object(3)
memory usage: 176.0+ bytes
None
Traceback (most recent call last):
  File "csvToJson.py", line 31, in <module>
    grouped = df.groupby(['country', 'state'])
  File "/home/simarpreet/Envs/j/lib/python3.7/site-packages/pandas/core/generic.py", line 7632, in groupby
    observed=observed, **kwargs)
  File "/home/simarpreet/Envs/j/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 2110, in groupby
    return klass(obj, by, **kwds)
  File "/home/simarpreet/Envs/j/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 360, in __init__
    mutated=self.mutated)
  File "/home/simarpreet/Envs/j/lib/python3.7/site-packages/pandas/core/groupby/grouper.py", line 578, in _get_grouper
    raise KeyError(gpr)
KeyError: 'state'

输入csv：

country, state, city
India, Delhi, Tilak nagar
India, Mumbai, Bandra
Australia, Queensland, Gold Coast
US, California, Los Angeles

我的代码：

csvFilePath = "/home/simarpreet/sampleCsv.csv"
jsonFilePath = "/home/simarpreet/sampleJson.json"
jsonFile = open(jsonFilePath, 'w')

df = pd.read_csv(csvFilePath, encoding='utf-8-sig')
print("df info")
print(df.info())
finalList = []

grouped = df.groupby(['country', 'state'])
for key, value in grouped:
    dictionary = {}

    j = grouped.get_group(key).reset_index(drop=True)
    dictionary['country'] = j.at[0, 'country']
    dictionary['state'] = j.at[0, 'state']

    dictList = []
    anotherDict = {}
    for i in j.index:

        anotherDict['city'] = j.at[i, 'city']

        dictList.append(anotherDict)

    dictionary['children'] = dictList

    finalList.append(dictionary)

json.dumps(finalList)

Answer 1

问题出在您的csv文件中，列名中存在主要的whistespaces，因此将出现关键错误。

@ cs95指出您可以做到

df.columns = df.columns.str.strip()

或者您可以使用read_csv处理空格：

pd.read_csv(csvFilePath, encoding='utf-8-sig', sep='\s*,\s*', engine='python')

PS：处理它的不好方法：

grouped = df.groupby(['country', ' state'])

熊猫groupby正在给出“ keyError”，即使该密钥存在

1 个答案: