Pandas groupby给出了“keyError”,即使这个键存在

2024-09-29 17:22:35 发布

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我是Python新手,对于我的一个项目,我需要将csv转换为嵌套Json。在网上搜索时,我发现pandas在这种情况下很有用。 我遵循了Convert CSV Data to Nested JSON in Python中给出的建议 但是我得到了一个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:

^{pr2}$

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)


Tags: csvinpycorepandasdfhomedictionary
1条回答
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1楼 · 发布于 2024-09-29 17:22:35

问题是你的csv文件,列名中有前导whitespaces,因此键错误就来了。在

正如@cs95所指出的,你可以做到

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

也可以使用read_csv处理空间:

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

PS:糟糕的处理方法:

^{pr2}$

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