3 回答

TA貢獻(xiàn)1820條經(jīng)驗(yàn) 獲得超2個(gè)贊
現(xiàn)在,根據(jù)您的評(píng)論,我更好地理解您的問題,這是一個(gè)完全不同的答案。請(qǐng)注意,它不使用json模塊,只是“手動(dòng)”進(jìn)行所需的處理。雖然它可能可以使用該模塊來完成,但與下面使用的相對(duì)簡(jiǎn)單的邏輯相比,讓它以不同的方式格式化默認(rèn)情況下識(shí)別的 Python 數(shù)據(jù)類型可能相當(dāng)復(fù)雜——我從經(jīng)驗(yàn)中知道——。
花藥注釋:與您的代碼一樣,這會(huì)將 csv 文件的每一行轉(zhuǎn)換為有效的 JSON 對(duì)象,并將每一行寫入文件中的單獨(dú)行。然而,結(jié)果文件的內(nèi)容在技術(shù)上不會(huì)是有效的 JSON,因?yàn)樗羞@些單獨(dú)的對(duì)象都需要用逗號(hào)分隔并括在[ ]括號(hào)中(即,從而成為有效的 JSON“數(shù)組”對(duì)象)。
import csv
with open('output2.csv', 'r', newline='') as csvfile, \
open('output2.json', 'w') as jsonfile:
for row in csv.DictReader(csvfile):
newfmt = []
for field, value in row.items():
field = '"{}"'.format(field)
try:
float(value)
except ValueError:
value = 'null' if value == '' else '"{}"'.format(value)
else:
# Avoid changing integer values to float.
try:
int(value)
except ValueError:
pass
else:
value = '"{}"'.format(value)
newfmt.append((field, value))
json_repr = '{' + ','.join(':'.join(pair) for pair in newfmt) + '}'
jsonfile.write(json_repr + '\n')
這是寫入文件的 JSON:
{"ACCOUNTNAMEDENORM":"John Smith","DELINQUENCYSTATUS":2.0000000000,"RETIRED":0.0000000000,"INVOICEDAYOFWEEK":5.0000000000,"ID":1234567.0000000000,"BEANVERSION":69.0000000000,"ACCOUNTTYPE":1.0000000000,"ORGANIZATIONTYPEDENORM":null,"HIDDENTACCOUNTCONTAINERID":4321987.0000000000,"NEWPOLICYPAYMENTDISTRIBUTABLE":"1","ACCOUNTNUMBER":"000-000-000-00","PAYMENTMETHOD":12345.0000000000,"INVOICEDELIVERYTYPE":98765.0000000000,"DISTRIBUTIONLIMITTYPE":3.0000000000,"CLOSEDATE":null,"FIRSTTWICEPERMTHINVOICEDOM":1.0000000000,"HELDFORINVOICESENDING":"0","FEINDENORM":null,"COLLECTING":"0","ACCOUNTNUMBERDENORM":"000-000-000-00","CHARGEHELD":"0","PUBLICID":"bc:1234346"}
下面再次顯示添加了空格:
{"ACCOUNTNAMEDENORM": "John Smith",
"DELINQUENCYSTATUS": 2.0000000000,
"RETIRED": 0.0000000000,
"INVOICEDAYOFWEEK": 5.0000000000,
"ID": 1234567.0000000000,
"BEANVERSION": 69.0000000000,
"ACCOUNTTYPE": 1.0000000000,
"ORGANIZATIONTYPEDENORM": null,
"HIDDENTACCOUNTCONTAINERID": 4321987.0000000000,
"NEWPOLICYPAYMENTDISTRIBUTABLE": "1",
"ACCOUNTNUMBER": "000-000-000-00",
"PAYMENTMETHOD": 12345.0000000000,
"INVOICEDELIVERYTYPE": 98765.0000000000,
"DISTRIBUTIONLIMITTYPE": 3.0000000000,
"CLOSEDATE": null,
"FIRSTTWICEPERMTHINVOICEDOM": 1.0000000000,
"HELDFORINVOICESENDING": "0",
"FEINDENORM": null,
"COLLECTING": "0",
"ACCOUNTNUMBERDENORM": "000-000-000-00",
"CHARGEHELD": "0",
"PUBLICID": "bc:1234346"}

TA貢獻(xiàn)1851條經(jīng)驗(yàn) 獲得超3個(gè)贊
哈,真的很有趣,我想和你找到相反的答案,結(jié)果是帶引號(hào)的。
其實(shí)很容易自動(dòng)刪除它,只需刪除參數(shù)“separators=(',',':')”。
對(duì)我來說,只需添加這個(gè)參數(shù)就可以了。

TA貢獻(xiàn)1900條經(jīng)驗(yàn) 獲得超5個(gè)贊
一種解決方案是使用正則表達(dá)式查看字符串值是否看起來像浮點(diǎn)數(shù),如果是,則將其轉(zhuǎn)換為浮點(diǎn)數(shù)。
import re
null = None
j = {"ACCOUNTNAMEDENORM":"John Smith","DELINQUENCYSTATUS":"2.0000000000",
"RETIRED":"0.0000000000","INVOICEDAYOFWEEK":"5.0000000000",
"ID":"1234567.0000000000","BEANVERSION":"69.0000000000",
"ACCOUNTTYPE":"1.0000000000","ORGANIZATIONTYPEDENORM":null,
"HIDDENTACCOUNTCONTAINERID":"4321987.0000000000",
"NEWPOLICYPAYMENTDISTRIBUTABLE":"1","ACCOUNTNUMBER":"000-000-000-00",
"PAYMENTMETHOD":"12345.0000000000","INVOICEDELIVERYTYPE":"98765.0000000000",
"DISTRIBUTIONLIMITTYPE":"3.0000000000","CLOSEDATE":null,
"FIRSTTWICEPERMTHINVOICEDOM":"1.0000000000","HELDFORINVOICESENDING":"0",
"FEINDENORM":null,"COLLECTING":"0","ACCOUNTNUMBERDENORM":"000-000-000-00",
"CHARGEHELD":"0","PUBLICID":"xx:1234346"}
for key in j:
if j[key] is not None:
if re.match("^\d+?\.\d+?$", j[key]):
j[key] = float(j[key])
我null = None在這里用來處理出現(xiàn)在 JSON 中的“null”。但是您可以在此處用您正在閱讀的每個(gè) CSV 行替換 'j',然后使用它來更新該行,然后用浮點(diǎn)數(shù)替換字符串將其寫回。
如果您可以將任何數(shù)字字符串轉(zhuǎn)換為浮點(diǎn)數(shù),那么您可以跳過正則表達(dá)式(re.match()命令)并將其替換為j[key].isnumeric(),如果它適用于您的 Python 版本。
編輯:我不認(rèn)為 Python 中的浮點(diǎn)數(shù)以您可能認(rèn)為的方式處理“精度”。它可能看起來像是2.0000000000被“截?cái)唷睘?.0,但我認(rèn)為這更多是格式和顯示問題,而不是丟失信息。考慮以下示例:
>>> float(2.0000000000)
2.0
>>> float(2.00000000001)
2.00000000001
>>> float(1.00) == float(1.000000000)
True
>>> float(3.141) == float(3.140999999)
False
>>> float(3.141) == float(3.1409999999999999)
True
>>> print('%.10f' % 3.14)
3.1400000000
雖然有可能讓 JSON 包含這些零,但在這種情況下,它歸結(jié)為將數(shù)字視為字符串,即格式化的字符串。
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