将固定宽度文件转换为 .txt，然后将 .txt 转换为 .csv
Converting Fixed-Width File to .txt then the .txt to .csv
我有一个固定宽度的文件，导入和拆分成 31 个 txt 文件没有问题。固定宽度文件中的空格在此过程中得到保留，因为写入 txt 只是将固定宽度文件中的每个条目作为一个新行写入。
我的问题是，当我使用 python 的 csv 函数时，这些空格被替换为 "（引号）作为占位符。
我想看看是否有一种方法可以在不使用这些双引号作为占位符的情况下生成 csv 文件，同时保持最初在固定宽度文件中设置的所需格式。
txt 文档中的第一行：
'PAY90004100095206 9581400086000909 0008141000 5350 3810 C 000021841998051319980513P810406247 FELT, MARTIN & FRAZIER, P.C. FELT, MARTIN & FRAZIER, P.C. 208 NORTH BROADWAY STE 313 BILLINGS MT59101-0 NLance Martin v. Whitman College N00000000NN98004264225 SYS656 19980512+000000378761998041319980421+000000378769581400086000909 000+000000 Lance Martin v. Whitman College 00000000 00010001 +00000000000002184 000000021023.005000000003921.005\n'
.py:
import csv
read_loc = 'c:/Users/location/e0290000005.txt'
e02ext_start = read_loc.find('e02')
e02_ext = read_loc[int(e02ext_start):]
with open(read_loc, 'r') as f:
contents = f.readlines()
dict_of_record_lists = {}
# takes first 3 characters of each line and if a matching dictionary key is found
# it appends the line to the value-list
for line in contents:
record_type = (line[:3])
dict_of_record_lists.setdefault(record_type,[]).append(line)
slice_list_CLM = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,47),(47,55),(55,59),(59,109),(109,189),(189,191),(191,193),(193,194),(194,195),(195,203),(203,211),(211,219),(219,227),(227,235),(235,237),(237,239),(239,241),(241,245),(245,249),(249,253),(253,257),(257,261),(261,291),(291,316),(316,331),(331,332),(332,357),(357,377),(377,378),(378,408),(408,438),(438,468),(468,470),(470,485),(485,505),(505,514),(514,517),(517,525),(525,533),(533,535),(535,536),(536,537),(537,545),(545,551),(551,553),(553,568),(568,572),(572,587),(587,602),(602,627),(627,631),(631,638),(638,642),(642,646),(646,654),(654,662),(662,670),(670,672),(672,674),(674,675),(675,676),(676,682),(682,700),(700,708),(708,716),(716,717),(717,725),(725,733),(733,741),(741,749),(749,759),(759,761),(761,762),(762,763),(763,764),(764,765),(765,768),(768,769),(769,770),(770,778),(778,779),(779,783),(783,787),(787,788),(788,805),(805,817),(817,829),(829,833),(833,863),(863,893),(893,896),(896,897),(897,898),(898,928),(928,936),(936,944),(944,945),(945,947),(947,959),(959,971),(971,983),(983,995),(995,1007),(1007,1019),(1019,1031),(1031,1043),(1043,1055),(1055,1067),(1067,1079),(1079,1091),(1091,1103),(1103,1115),(1115,1127),(1127,1139),(1139,1151),(1151,1163),(1163,1175),(1175,1187),(1187,1197),(1197,1202),(1202,1203),(1203,1211),(1211,1214),(1214,1215),(1215,1233),(1233,1241),(1241,1257),(1257,1272),(1272,1273),(1273,1285),(1285,1289),(1289,1293),(1293,1343),(1343,1365),(1365,1685),(1685,1686),(1686,1704),(1704,1708),(1708,1748),(1748,1768),(1768,1770),(1770,1772),(1772,1773),(1773,1782),(1782,1784),(1784,1792),(1792,1793),(1793,1796),(1796,1800)]
slice_list_CTL = [(0,3),(3,7),(7,15),(15,23),(23,31),(31,39),(39,47),(47,55),(55,56),(56,65),(65,74),(74,83),(83,98),(98,113),(113,128),(128,143),(143,158),(158,173),(173,188),(188,203),(203,218),(218,233),(233,248),(248,263),(263,278),(278,293),(293,308),(308,323),(323,338),(338,353),(353,368),(368,383),(383,398),(398,413),(413,428),(428,443),(443,458),(458,473),(473,488),(488,503),(503,518),(518,527),(527,536),(536,545),(545,554),(554,563),(563,572),(572,581),(581,590),(590,599),(599,614),(614,623),(623,638),(638,647),(647,662),(662,671),(671,686),(686,695),(695,710),(710,719),(719,728),(728,737),(737,746),(746,755),(755,764),(764,773),(773,782),(782,791),(791,800),(800,809),(809,818),(818,827),(827,836),(836,845),(845,854),(854,863),(863,872),(872,881),(881,890),(890,899),(899,908)]
slice_list_ADR = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,50),(50,53),(53,62),(62,65),(65,66),(66,91),(91,111),(111,121),(121,151),(151,181),(181,206),(206,208),(208,223),(223,243),(243,261),(261,265),(265,283),(283,287),(287,305),(305,335),(335,375),(375,383),(383,387),(387,437),(437,438),(438,446),(446,454),(454,461),(461,468),(468,484),(484,500)]
slice_list_AGR = [(0,3),(3,7),(7,45),(45,85),(85,93),(93,101),(101,109),(109,117),(117,127),(127,139),(139,151)]
slice_list_ACN = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,65),(65,95),(95,115),(115,145),(145,165),(165,195),(195,215),(215,245),(245,265),(265,295),(295,315),(315,345),(345,365),(365,395),(395,415),(415,445),(445,465),(465,495),(495,515),(515,545),(545,565),(565,595),(595,615),(615,645),(645,665),(665,695),(695,715),(715,745),(745,765),(765,795),(795,815),(815,845),(845,865),(865,895),(895,915),(915,945),(945,965),(965,995),(995,1015),(1015,1045),(1045,1061)]
slice_list_CST = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,59),(59,60),(60,61),(61,62),(62,64),(64,80),(80,82),(82,84),(84,86),(86,88),(88,104)]
slice_list_MCF = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,49),(49,79),(79,94),(94,159),(159,175),(175,191)]
slice_list_DD1 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,54),(54,62),(62,63),(63,69),(69,75),(75,81),(81,87),(87,93),(93,94),(94,95),(95,103),(103,111),(111,119),(119,126),(126,134),(134,143),(143,154),(154,162),(162,170),(170,178),(178,186),(186,194),(194,202),(202,205),(205,208),(208,210),(210,218),(218,220),(220,228),(228,230),(230,238),(238,240),(240,248),(248,250),(250,258),(258,274)]
slice_list_DES = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,1300),(1300,1316)]
slice_list_IBC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,48),(48,50),(50,54),(54,55),(55,56),(56,81),(81,101),(101,121),(121,124),(124,125),(125,145),(145,146),(146,149),(149,152),(152,154),(154,179),(179,199),(199,219),(219,222),(222,224),(224,227),(227,230),(230,238),(238,249),(249,265),(265,281)]
slice_list_ICD = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,57),(57,63),(63,69),(69,75),(75,81),(81,87),(87,95),(95,103),(103,111),(111,114),(114,122),(122,125),(125,126),(126,142),(142,144),(144,152),(152,154),(154,162),(162,164),(164,172),(172,174),(174,182),(182,184),(184,192),(192,208)]
slice_list_LEG = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,65),(65,73),(73,81),(81,82),(82,90),(90,98),(98,133),(133,148),(148,163),(163,164),(164,172),(172,180),(180,181),(181,216),(216,256),(256,296),(296,326),(326,356),(356,381),(381,383),(383,398),(398,418),(418,438),(438,456),(456,474),(474,509),(509,549),(549,589),(589,619),(619,649),(649,674),(674,676),(676,691),(691,711),(711,731),(731,749),(749,767),(767,782),(782,790),(790,798),(798,806),(806,810),(810,818),(818,826),(826,834),(834,840),(840,849),(849,879),(879,888),(888,918),(918,920),(920,921),(921,923),(923,931),(931,939),(939,943),(943,944),(944,952),(952,960),(960,990),(990,1020),(1020,1050),(1050,1051),(1051,1086),(1086,1095),(1095,1135),(1135,1175),(1175,1205),(1205,1235),(1235,1260),(1260,1262),(1262,1277),(1277,1295),(1295,1304),(1304,1312),(1312,1328)]
slice_list_LD1 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,65),(65,95),(95,125),(125,150),(150,152),(152,167),(167,187),(187,205),(205,223),(223,227),(227,252),(252,267),(267,279),(279,309),(309,339),(339,359),(359,361),(361,376),(376,396),(396,414),(414,439),(439,440),(440,448),(448,454),(454,456),(456,871),(471,472),(472,492),(492,522),(522,552),(552,572),(572,574),(574,589),(589,609),(609,627),(627,637),(637,645),(645,685),(685,686),(686,706),(706,714),(714,744),(744,774),(774,794),(794,796),(796,811),(811,831),(831,849),(849,879),(879,909),(909,929),(929,931),(931,946),(946,966),(966,984),(984,992),(992,1004),(1004,1024),(1024,1064),(1064,1081),(1081,1098),(1098,1106),(1106,1121),(1121,1122),(1122,1152),(1152,1153),(1153,1162),(1162,1170),(1170,1185),(1185,1190),(1190,1220),(1220,1238),(1238,1253),(1253,1283),(1283,1301),(1301,1302),(1302,1303),(1303,1333),(1333,1363),(1363,1388),(1388,1390),(1390,1405),(1405,1406),(1406,1436),(1436,1442),(1442,1462),(1462,1463),(1463,1478),(1478,1493),(1493,1533),(1533,1535),(1535,1538),(1538,1540),(1540,1556),(1556,1756)]
slice_list_LD2 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,60),(60,78),(78,118),(118,148),(148,178),(178,203),(203,205),(205,220),(220,238),(238,256),(256,260),(260,270),(270,290),(290,300),(300,302),(302,322),(322,352),(352,377),(377,397),(397,398),(398,423),(423,424),(424,454),(454,455),(455,456),(456,458),(458,474)]
slice_list_LD3 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,71),(71,91),(91,92),(92,122),(122,152),(152,177),(177,179),(179,194),(194,197),(197,205),(205,213),(213,221),(221,229),(229,237),(237,297),(297,305),(305,313),(313,321),(321,329),(329,337),(337,345),(345,353),(353,361),(361,421),(421,429),(429,489),(489,497),(497,557),(557,617),(617,633)]
slice_list_NET = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,69),(69,77),(77,88),(88,99),(99,105),(105,135),(135,146),(146,152),(152,182),(182,193),(193,199),(199,229),(229,240),(240,246),(246,276),(276,287),(287,293),(293,323),(323,334),(334,340),(340,370),(370,381),(381,387),(387,417),(417,428),(428,434),(434,464),(464,475),(475,481),(481,511),(511,522),(522,528),(528,558),(558,569),(569,575),(575,605),(605,616),(616,622),(622,652),(652,663),(663,669),(669,699),(699,710),(710,716),(716,746),(746,757),(757,763),(763,793),(793,804),(804,810),(810,840),(840,851),(851,857),(857,887),(887,898),(898,904),(904,934),(934,945),(945,951),(951,981),(981,992),(992,998),(998,1028),(1028,1039),(1039,1047),(1047,1055),(1055,1061),(1061,1077),(1077,1087),(1087,1103)]
slice_list_NOT = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,47),(47,55),(55,63),(63,71),(71,77),(77,79),(79,1279),(1279,1295),(1295,1296),(1296,1312)]
slice_list_OFF = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,75),(75,78),(78,93),(93,105),(105,107),(107,115),(115,123),(123,131),(131,132),(132,148)]
slice_list_PAY = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,60),(60,61),(61,65),(65,73),(73,81),(81,89),(89,90),(90,130),(130,165),(165,205),(205,245),(245,275),(275,305),(305,330),(330,332),(332,347),(347,367),(367,368),(368,428),(428,429),(429,437),(437,438),(438,439),(439,450),(450,452),(452,455),(455,458),(458,473),(473,481),(481,493),(493,501),(501,509),(509,521),(521,539),(539,542),(542,549),(549,552),(552,562),(562,567),(567,627),(627,635),(635,643),(643,647),(647,651),(651,653),(653,654),(654,684),(684,692),(692,702),(702,713),(713,1034),(1034,1050),(1050,1066)]
slice_list_PRC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,51),(51,81),(81,84),(84,87),(87,95),(95,103),(103,119),(119,125),(125,131),(131,147)]
slice_list_ACR = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,51),(51,59),(59,71),(71,79),(79,91),(91,103),(103,119),(119,135)]
slice_list_REC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,58),(58,71),(71,84),(84,97),(97,110),(110,123),(123,136),(136,149),(149,162),(162,175),(175,188),(188,201),(201,214),(214,227),(227,240),(240,253),(253,266),(266,279),(279,292),(292,305),(305,318),(318,331),(331,344),(344,357),(357,370),(370,383),(383,396),(396,409),(409,422),(422,435),(435,448),(448,461),(461,474),(474,487),(487,500),(500,513),(513,526),(526,539),(539,552),(552,565),(565,578),(578,591),(591,604),(604,617),(617,630),(630,643),(643,656),(656,669),(669,682),(682,695),(695,708),(708,721),(721,734),(734,747),(747,760),(760,773),(773,786),(786,799),(799,812),(812,825),(825,838),(838,851),(851,864),(864,877),(877,890),(890,903),(903,916),(916,929),(929,942),(942,955),(955,968),(968,981),(981,997)]
slice_list_RED = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,57),(57,69),(69,81),(81,93),(93,105),(105,117),(117,129),(129,141),(141,157)]
slice_list_REI = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,61),(61,67),(67,87),(87,88),(88,100),(100,108),(108,116),(116,176),(176,192),(192,193),(193,199),(199,214),(214,222),(222,230),(230,238),(238,250),(250,251),(251,311),(311,327)]
slice_list_RES = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,54),(54,134),(134,136),(136,148),(148,160),(160,172),(172,184),(184,196),(196,208),(208,220),(220,232),(232,242),(242,252),(252,262),(262,272),(272,282),(282,292),(292,299),(299,309),(309,319),(319,329),(329,339),(339,349),(349,359),(359,369),(369,379),(379,389),(389,399),(399,409),(409,419),(419,429),(429,439),(439,449),(449,465),(465,475),(475,975),(975,991)]
slice_list_RST = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,69),(69,77),(77,87),(87,95),(95,125),(125,145),(145,161),(161,177)]
slice_list_SPC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,69),(69,77),(77,85),(85,93),(93,101),(101,109),(109,117),(117,125),(125,133),(133,149)]
slice_list_SSN = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,54),(54,62),(62,74),(74,82),(82,94),(94,102),(102,114),(114,122),(122,134),(134,142),(142,143),(143,151),(151,159),(159,160),(160,168),(168,176),(176,177),(177,185),(185,193),(193,194),(194,202),(202,210),(210,211),(211,219),(219,220),(220,228),(228,268),(268,276),(276,277),(277,293)]
slice_list_WRK = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,57),(57,72),(72,73),(73,81),(81,82),(82,90),(90,98),(98,106),(106,114),(114,122),(122,130),(130,131),(131,132),(132,133),(133,153),(153,154),(154,155),(155,159),(159,179),(179,180),(180,240),(240,248),(248,256),(256,264),(264,272),(272,280),(280,284),(284,288),(288,298),(298,314),(314,330)]
slice_list_WD1 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,54),(54,58),(58,59),(59,60),(60,61),(61,63),(63,73),(73,74),(74,82),(82,83),(83,91),(91,99),(99,107),(107,108),(108,118),(118,120),(120,130),(130,137),(137,139),(139,149),(149,156),(156,158),(158,168),(168,175),(175,177),(177,187),(187,194),(194,196),(196,206),(206,213),(213,223),(223,233),(233,243),(243,253),(253,263),(263,273),(273,283),(283,293),(293,303),(303,311),(311,314),(314,322),(322,332),(332,342),(342,352),(352,353),(353,354),(354,355),(355,365),(365,375),(375,385),(385,395),(395,405),(405,415),(415,425),(425,435),(435,436),(436,437),(437,438),(438,439),(439,440),(440,442),(442,443),(443,444),(444,445),(445,446),(446,448),(448,458),(458,460),(460,470),(470,472),(472,482),(482,484),(484,494),(494,496),(496,506),(506,508),(508,518),(518,528),(528,542),(542,543),(543,551),(551,559),(559,561),(561,565),(565,567),(567,574),(574,582),(582,583),(583,584),(584,585),(585,593),(593,594),(594,595),(595,596),(596,604),(604,605),(605,606),(606,607),(607,615),(615,616),(616,617),(617,618),(618,626),(626,627),(627,628),(628,629),(629,637),(637,645),(645,653),(653,661),(661,669),(669,677),(677,685),(685,693),(693,701),(701,709),(709,717),(717,721),(721,729),(729,732),(732,734),(734,738),(738,746),(746,749),(749,751),(751,755),(755,763),(763,766),(766,774),(774,782),(782,790),(790,798),(798,800),(800,801),(801,802),(802,813),(813,829)]
slice_list_WD3 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,47),(47,48),(48,49),(49,50),(50,51),(51,52),(52,53),(53,54),(54,55),(55,56),(56,57),(57,58),(58,98),(98,138),(138,178),(178,182),(182,183),(183,191),(191,197),(197,213)]
slice_dict = {
'CLM' : slice_list_CLM,
'CTL' : slice_list_CTL,
'ADR' : slice_list_ADR,
'AGR' : slice_list_AGR,
'ACN' : slice_list_ACN,
'CST' : slice_list_CST,
'MCF' : slice_list_MCF,
'DD1' : slice_list_DD1,
'DES' : slice_list_DES,
'IBC' : slice_list_IBC,
'ICD' : slice_list_ICD,
'LEG' : slice_list_LEG,
'LD1' : slice_list_LD1,
'LD2' : slice_list_LD2,
'LD3' : slice_list_LD3,
'NET' : slice_list_NET,
'NOT' : slice_list_NOT,
'OFF' : slice_list_OFF,
'PAY' : slice_list_PAY,
'PRC' : slice_list_PRC,
'ACR' : slice_list_ACR,
'REC' : slice_list_REC,
'RED' : slice_list_RED,
'REI' : slice_list_REI,
'RES' : slice_list_RES,
'RST' : slice_list_RST,
'SPC' : slice_list_SPC,
'SSN' : slice_list_SSN,
'WRK' : slice_list_WRK,
'WD1' : slice_list_WD1,
'WD3' : slice_list_WD3,
}
def slicer(file,slice_list):
csv_string = ""
for i in slice_list:
csv_string += (file[i[0]:i[1]]+",")
return csv_string
overview_loc = 'c:/Users/location/E02_ingestion/'+ 'overview_'+e02_ext #put in file location wehre you would like to see logs
with open(overview_loc, 'w') as overview_file:
for key, value in dict_of_record_lists.items():
overview_file.write((key+' '+(str(len(value)))+'\n'))
for key, value in dict_of_record_lists.items():
for k, v in slice_dict.items():
if key == k:
iteration = 0
for i in value:
s = slicer(i,v)
value[iteration] = s
iteration+= 1
e02_ext = read_loc[int(e02ext_start):]
csv_ext = e02_ext[:-3]+'csv'
# file overview/log that shows how many lines should exist in the other files to ensure everything wrote correctly
overview_loc = 'c:/Users/location/E02_ingestion/'+ 'overview_'+e02_ext #put in file location wehre you would like to see logs
with open(overview_loc, 'w') as overview_file:
for key, value in dict_of_record_lists.items():
overview_file.write((key+' '+(str(len(value)))+'\n'))
# if the list isn't empty writes a new file w/prefix matching key and includes the lines
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/location/E02_ingestion/'+ key +'_'+e02_ext
with open(write_loc, "w", newline='') as parsed_file:
for line in value:
line_pre = "%s\n" % line
parsed_file.write(line_pre[:-1])
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/location/E02_ingestion/'+ key +'_'+csv_ext
with open(write_loc, "w", newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for i in value:
writer.writerow(i)
这是 Excel 和我们的 SQL table:
中的一部分输出示例
P A Y 9 0 0 0 4 1 0 0 0 9 5 2 0 7 " " " " " " " "
期望的输出（没有 " 作为空格的占位符）：
P A Y 9 0 0 0 4 1 0 0 0 9 5 2 0 7
如有任何帮助，我们将不胜感激。
为什么：
您面临的问题是，您的 row 已处理数据中的列表条目仅包含 csv.delimiter 字符。该模块然后 引用 它们以区分您的 "delimiter-only-data" 和您的 "delimiter between columns" .
当使用 ' ' 作为分隔符将类似 [ ["PAY","...."," "," "," "," "] ] 的内容写入 csv 时，您会在输出中引用它们：
导入 csv
dict_of_record_lists = {"K": [ ["PAY","...."," "," "," "," "] ] }
for key, value in dict_of_record_lists.items():
write_loc = 't.txt'
with open(write_loc, "w", newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for i in value:
writer.writerow(i)
print( open(write_loc).read()) # PAY .... " " " " " " " "
修正:
您可以通过指定 quoting=csv.QUOTE_NONE 并提供 escapechar = ... 或通过修复您的数据来修复该问题。提供 escapechar 会将其放入您的文件中。
文档的相关部分：csv.QUOTE_NONE.
您可以操纵数据以不包含"only"分隔符作为数据：
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/location/E02_ingestion/'+ key +'_'+csv_ext
with open(write_loc, "w", newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for i in value:
# if an inner item only contains delimiter characters, set it to empty string
cleared = [x for x in i if i.strip(" ") else ""]
writer.writerow(cleared)
HTH
独库：
能够将初始文本写作部分更改为：
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/Steve Barnard/Desktop/Git_Projects/E02_ingestion/'+ key +'_'+csv_ext
with open(write_loc, "w", newline='') as parsed_file:
for line in value:
line_pre = "%s" % line
parsed_file.write(line_pre[:-1]+'\n')
所有问题都已通过避免 python 的内置 CSV 编写器得到解决。
我的程序在行切片后添加一个逗号的方式，留下一个额外的逗号和'\n';这导致写入函数中的 [:-1] 切片删除了 \n 而不是最后的 ','。通过在逗号删除后添加“\n”，整个问题得到解决，并创建了一个保留间距的功能 CSV。
可以通过写入时换出扩展名来创建文本文件。
我有一个固定宽度的文件，导入和拆分成 31 个 txt 文件没有问题。固定宽度文件中的空格在此过程中得到保留，因为写入 txt 只是将固定宽度文件中的每个条目作为一个新行写入。
我的问题是，当我使用 python 的 csv 函数时，这些空格被替换为 "（引号）作为占位符。
我想看看是否有一种方法可以在不使用这些双引号作为占位符的情况下生成 csv 文件，同时保持最初在固定宽度文件中设置的所需格式。
txt 文档中的第一行：
'PAY90004100095206 9581400086000909 0008141000 5350 3810 C 000021841998051319980513P810406247 FELT, MARTIN & FRAZIER, P.C. FELT, MARTIN & FRAZIER, P.C. 208 NORTH BROADWAY STE 313 BILLINGS MT59101-0 NLance Martin v. Whitman College N00000000NN98004264225 SYS656 19980512+000000378761998041319980421+000000378769581400086000909 000+000000 Lance Martin v. Whitman College 00000000 00010001 +00000000000002184 000000021023.005000000003921.005\n'
.py:
import csv
read_loc = 'c:/Users/location/e0290000005.txt'
e02ext_start = read_loc.find('e02')
e02_ext = read_loc[int(e02ext_start):]
with open(read_loc, 'r') as f:
contents = f.readlines()
dict_of_record_lists = {}
# takes first 3 characters of each line and if a matching dictionary key is found
# it appends the line to the value-list
for line in contents:
record_type = (line[:3])
dict_of_record_lists.setdefault(record_type,[]).append(line)
slice_list_CLM = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,47),(47,55),(55,59),(59,109),(109,189),(189,191),(191,193),(193,194),(194,195),(195,203),(203,211),(211,219),(219,227),(227,235),(235,237),(237,239),(239,241),(241,245),(245,249),(249,253),(253,257),(257,261),(261,291),(291,316),(316,331),(331,332),(332,357),(357,377),(377,378),(378,408),(408,438),(438,468),(468,470),(470,485),(485,505),(505,514),(514,517),(517,525),(525,533),(533,535),(535,536),(536,537),(537,545),(545,551),(551,553),(553,568),(568,572),(572,587),(587,602),(602,627),(627,631),(631,638),(638,642),(642,646),(646,654),(654,662),(662,670),(670,672),(672,674),(674,675),(675,676),(676,682),(682,700),(700,708),(708,716),(716,717),(717,725),(725,733),(733,741),(741,749),(749,759),(759,761),(761,762),(762,763),(763,764),(764,765),(765,768),(768,769),(769,770),(770,778),(778,779),(779,783),(783,787),(787,788),(788,805),(805,817),(817,829),(829,833),(833,863),(863,893),(893,896),(896,897),(897,898),(898,928),(928,936),(936,944),(944,945),(945,947),(947,959),(959,971),(971,983),(983,995),(995,1007),(1007,1019),(1019,1031),(1031,1043),(1043,1055),(1055,1067),(1067,1079),(1079,1091),(1091,1103),(1103,1115),(1115,1127),(1127,1139),(1139,1151),(1151,1163),(1163,1175),(1175,1187),(1187,1197),(1197,1202),(1202,1203),(1203,1211),(1211,1214),(1214,1215),(1215,1233),(1233,1241),(1241,1257),(1257,1272),(1272,1273),(1273,1285),(1285,1289),(1289,1293),(1293,1343),(1343,1365),(1365,1685),(1685,1686),(1686,1704),(1704,1708),(1708,1748),(1748,1768),(1768,1770),(1770,1772),(1772,1773),(1773,1782),(1782,1784),(1784,1792),(1792,1793),(1793,1796),(1796,1800)]
slice_list_CTL = [(0,3),(3,7),(7,15),(15,23),(23,31),(31,39),(39,47),(47,55),(55,56),(56,65),(65,74),(74,83),(83,98),(98,113),(113,128),(128,143),(143,158),(158,173),(173,188),(188,203),(203,218),(218,233),(233,248),(248,263),(263,278),(278,293),(293,308),(308,323),(323,338),(338,353),(353,368),(368,383),(383,398),(398,413),(413,428),(428,443),(443,458),(458,473),(473,488),(488,503),(503,518),(518,527),(527,536),(536,545),(545,554),(554,563),(563,572),(572,581),(581,590),(590,599),(599,614),(614,623),(623,638),(638,647),(647,662),(662,671),(671,686),(686,695),(695,710),(710,719),(719,728),(728,737),(737,746),(746,755),(755,764),(764,773),(773,782),(782,791),(791,800),(800,809),(809,818),(818,827),(827,836),(836,845),(845,854),(854,863),(863,872),(872,881),(881,890),(890,899),(899,908)]
slice_list_ADR = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,50),(50,53),(53,62),(62,65),(65,66),(66,91),(91,111),(111,121),(121,151),(151,181),(181,206),(206,208),(208,223),(223,243),(243,261),(261,265),(265,283),(283,287),(287,305),(305,335),(335,375),(375,383),(383,387),(387,437),(437,438),(438,446),(446,454),(454,461),(461,468),(468,484),(484,500)]
slice_list_AGR = [(0,3),(3,7),(7,45),(45,85),(85,93),(93,101),(101,109),(109,117),(117,127),(127,139),(139,151)]
slice_list_ACN = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,65),(65,95),(95,115),(115,145),(145,165),(165,195),(195,215),(215,245),(245,265),(265,295),(295,315),(315,345),(345,365),(365,395),(395,415),(415,445),(445,465),(465,495),(495,515),(515,545),(545,565),(565,595),(595,615),(615,645),(645,665),(665,695),(695,715),(715,745),(745,765),(765,795),(795,815),(815,845),(845,865),(865,895),(895,915),(915,945),(945,965),(965,995),(995,1015),(1015,1045),(1045,1061)]
slice_list_CST = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,59),(59,60),(60,61),(61,62),(62,64),(64,80),(80,82),(82,84),(84,86),(86,88),(88,104)]
slice_list_MCF = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,49),(49,79),(79,94),(94,159),(159,175),(175,191)]
slice_list_DD1 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,54),(54,62),(62,63),(63,69),(69,75),(75,81),(81,87),(87,93),(93,94),(94,95),(95,103),(103,111),(111,119),(119,126),(126,134),(134,143),(143,154),(154,162),(162,170),(170,178),(178,186),(186,194),(194,202),(202,205),(205,208),(208,210),(210,218),(218,220),(220,228),(228,230),(230,238),(238,240),(240,248),(248,250),(250,258),(258,274)]
slice_list_DES = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,1300),(1300,1316)]
slice_list_IBC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,48),(48,50),(50,54),(54,55),(55,56),(56,81),(81,101),(101,121),(121,124),(124,125),(125,145),(145,146),(146,149),(149,152),(152,154),(154,179),(179,199),(199,219),(219,222),(222,224),(224,227),(227,230),(230,238),(238,249),(249,265),(265,281)]
slice_list_ICD = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,57),(57,63),(63,69),(69,75),(75,81),(81,87),(87,95),(95,103),(103,111),(111,114),(114,122),(122,125),(125,126),(126,142),(142,144),(144,152),(152,154),(154,162),(162,164),(164,172),(172,174),(174,182),(182,184),(184,192),(192,208)]
slice_list_LEG = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,65),(65,73),(73,81),(81,82),(82,90),(90,98),(98,133),(133,148),(148,163),(163,164),(164,172),(172,180),(180,181),(181,216),(216,256),(256,296),(296,326),(326,356),(356,381),(381,383),(383,398),(398,418),(418,438),(438,456),(456,474),(474,509),(509,549),(549,589),(589,619),(619,649),(649,674),(674,676),(676,691),(691,711),(711,731),(731,749),(749,767),(767,782),(782,790),(790,798),(798,806),(806,810),(810,818),(818,826),(826,834),(834,840),(840,849),(849,879),(879,888),(888,918),(918,920),(920,921),(921,923),(923,931),(931,939),(939,943),(943,944),(944,952),(952,960),(960,990),(990,1020),(1020,1050),(1050,1051),(1051,1086),(1086,1095),(1095,1135),(1135,1175),(1175,1205),(1205,1235),(1235,1260),(1260,1262),(1262,1277),(1277,1295),(1295,1304),(1304,1312),(1312,1328)]
slice_list_LD1 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,65),(65,95),(95,125),(125,150),(150,152),(152,167),(167,187),(187,205),(205,223),(223,227),(227,252),(252,267),(267,279),(279,309),(309,339),(339,359),(359,361),(361,376),(376,396),(396,414),(414,439),(439,440),(440,448),(448,454),(454,456),(456,871),(471,472),(472,492),(492,522),(522,552),(552,572),(572,574),(574,589),(589,609),(609,627),(627,637),(637,645),(645,685),(685,686),(686,706),(706,714),(714,744),(744,774),(774,794),(794,796),(796,811),(811,831),(831,849),(849,879),(879,909),(909,929),(929,931),(931,946),(946,966),(966,984),(984,992),(992,1004),(1004,1024),(1024,1064),(1064,1081),(1081,1098),(1098,1106),(1106,1121),(1121,1122),(1122,1152),(1152,1153),(1153,1162),(1162,1170),(1170,1185),(1185,1190),(1190,1220),(1220,1238),(1238,1253),(1253,1283),(1283,1301),(1301,1302),(1302,1303),(1303,1333),(1333,1363),(1363,1388),(1388,1390),(1390,1405),(1405,1406),(1406,1436),(1436,1442),(1442,1462),(1462,1463),(1463,1478),(1478,1493),(1493,1533),(1533,1535),(1535,1538),(1538,1540),(1540,1556),(1556,1756)]
slice_list_LD2 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,60),(60,78),(78,118),(118,148),(148,178),(178,203),(203,205),(205,220),(220,238),(238,256),(256,260),(260,270),(270,290),(290,300),(300,302),(302,322),(322,352),(352,377),(377,397),(397,398),(398,423),(423,424),(424,454),(454,455),(455,456),(456,458),(458,474)]
slice_list_LD3 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,71),(71,91),(91,92),(92,122),(122,152),(152,177),(177,179),(179,194),(194,197),(197,205),(205,213),(213,221),(221,229),(229,237),(237,297),(297,305),(305,313),(313,321),(321,329),(329,337),(337,345),(345,353),(353,361),(361,421),(421,429),(429,489),(489,497),(497,557),(557,617),(617,633)]
slice_list_NET = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,69),(69,77),(77,88),(88,99),(99,105),(105,135),(135,146),(146,152),(152,182),(182,193),(193,199),(199,229),(229,240),(240,246),(246,276),(276,287),(287,293),(293,323),(323,334),(334,340),(340,370),(370,381),(381,387),(387,417),(417,428),(428,434),(434,464),(464,475),(475,481),(481,511),(511,522),(522,528),(528,558),(558,569),(569,575),(575,605),(605,616),(616,622),(622,652),(652,663),(663,669),(669,699),(699,710),(710,716),(716,746),(746,757),(757,763),(763,793),(793,804),(804,810),(810,840),(840,851),(851,857),(857,887),(887,898),(898,904),(904,934),(934,945),(945,951),(951,981),(981,992),(992,998),(998,1028),(1028,1039),(1039,1047),(1047,1055),(1055,1061),(1061,1077),(1077,1087),(1087,1103)]
slice_list_NOT = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,47),(47,55),(55,63),(63,71),(71,77),(77,79),(79,1279),(1279,1295),(1295,1296),(1296,1312)]
slice_list_OFF = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,75),(75,78),(78,93),(93,105),(105,107),(107,115),(115,123),(123,131),(131,132),(132,148)]
slice_list_PAY = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,60),(60,61),(61,65),(65,73),(73,81),(81,89),(89,90),(90,130),(130,165),(165,205),(205,245),(245,275),(275,305),(305,330),(330,332),(332,347),(347,367),(367,368),(368,428),(428,429),(429,437),(437,438),(438,439),(439,450),(450,452),(452,455),(455,458),(458,473),(473,481),(481,493),(493,501),(501,509),(509,521),(521,539),(539,542),(542,549),(549,552),(552,562),(562,567),(567,627),(627,635),(635,643),(643,647),(647,651),(651,653),(653,654),(654,684),(684,692),(692,702),(702,713),(713,1034),(1034,1050),(1050,1066)]
slice_list_PRC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,51),(51,81),(81,84),(84,87),(87,95),(95,103),(103,119),(119,125),(125,131),(131,147)]
slice_list_ACR = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,51),(51,59),(59,71),(71,79),(79,91),(91,103),(103,119),(119,135)]
slice_list_REC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,58),(58,71),(71,84),(84,97),(97,110),(110,123),(123,136),(136,149),(149,162),(162,175),(175,188),(188,201),(201,214),(214,227),(227,240),(240,253),(253,266),(266,279),(279,292),(292,305),(305,318),(318,331),(331,344),(344,357),(357,370),(370,383),(383,396),(396,409),(409,422),(422,435),(435,448),(448,461),(461,474),(474,487),(487,500),(500,513),(513,526),(526,539),(539,552),(552,565),(565,578),(578,591),(591,604),(604,617),(617,630),(630,643),(643,656),(656,669),(669,682),(682,695),(695,708),(708,721),(721,734),(734,747),(747,760),(760,773),(773,786),(786,799),(799,812),(812,825),(825,838),(838,851),(851,864),(864,877),(877,890),(890,903),(903,916),(916,929),(929,942),(942,955),(955,968),(968,981),(981,997)]
slice_list_RED = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,57),(57,69),(69,81),(81,93),(93,105),(105,117),(117,129),(129,141),(141,157)]
slice_list_REI = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,61),(61,67),(67,87),(87,88),(88,100),(100,108),(108,116),(116,176),(176,192),(192,193),(193,199),(199,214),(214,222),(222,230),(230,238),(238,250),(250,251),(251,311),(311,327)]
slice_list_RES = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,54),(54,134),(134,136),(136,148),(148,160),(160,172),(172,184),(184,196),(196,208),(208,220),(220,232),(232,242),(242,252),(252,262),(262,272),(272,282),(282,292),(292,299),(299,309),(309,319),(319,329),(329,339),(339,349),(349,359),(359,369),(369,379),(379,389),(389,399),(399,409),(409,419),(419,429),(429,439),(439,449),(449,465),(465,475),(475,975),(975,991)]
slice_list_RST = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,69),(69,77),(77,87),(87,95),(95,125),(125,145),(145,161),(161,177)]
slice_list_SPC = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,61),(61,69),(69,77),(77,85),(85,93),(93,101),(101,109),(109,117),(117,125),(125,133),(133,149)]
slice_list_SSN = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,54),(54,62),(62,74),(74,82),(82,94),(94,102),(102,114),(114,122),(122,134),(134,142),(142,143),(143,151),(151,159),(159,160),(160,168),(168,176),(176,177),(177,185),(185,193),(193,194),(194,202),(202,210),(210,211),(211,219),(219,220),(220,228),(228,268),(268,276),(276,277),(277,293)]
slice_list_WRK = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,53),(53,57),(57,72),(72,73),(73,81),(81,82),(82,90),(90,98),(98,106),(106,114),(114,122),(122,130),(130,131),(131,132),(132,133),(133,153),(153,154),(154,155),(155,159),(159,179),(179,180),(180,240),(240,248),(248,256),(256,264),(264,272),(272,280),(280,284),(284,288),(288,298),(298,314),(314,330)]
slice_list_WD1 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,54),(54,58),(58,59),(59,60),(60,61),(61,63),(63,73),(73,74),(74,82),(82,83),(83,91),(91,99),(99,107),(107,108),(108,118),(118,120),(120,130),(130,137),(137,139),(139,149),(149,156),(156,158),(158,168),(168,175),(175,177),(177,187),(187,194),(194,196),(196,206),(206,213),(213,223),(223,233),(233,243),(243,253),(253,263),(263,273),(273,283),(283,293),(293,303),(303,311),(311,314),(314,322),(322,332),(332,342),(342,352),(352,353),(353,354),(354,355),(355,365),(365,375),(375,385),(385,395),(395,405),(405,415),(415,425),(425,435),(435,436),(436,437),(437,438),(438,439),(439,440),(440,442),(442,443),(443,444),(444,445),(445,446),(446,448),(448,458),(458,460),(460,470),(470,472),(472,482),(482,484),(484,494),(494,496),(496,506),(506,508),(508,518),(518,528),(528,542),(542,543),(543,551),(551,559),(559,561),(561,565),(565,567),(567,574),(574,582),(582,583),(583,584),(584,585),(585,593),(593,594),(594,595),(595,596),(596,604),(604,605),(605,606),(606,607),(607,615),(615,616),(616,617),(617,618),(618,626),(626,627),(627,628),(628,629),(629,637),(637,645),(645,653),(653,661),(661,669),(669,677),(677,685),(685,693),(693,701),(701,709),(709,717),(717,721),(721,729),(729,732),(732,734),(734,738),(738,746),(746,749),(749,751),(751,755),(755,763),(763,766),(766,774),(774,782),(782,790),(790,798),(798,800),(800,801),(801,802),(802,813),(813,829)]
slice_list_WD3 = [(0,3),(3,7),(7,15),(15,21),(21,39),(39,42),(42,45),(45,46),(46,47),(47,48),(48,49),(49,50),(50,51),(51,52),(52,53),(53,54),(54,55),(55,56),(56,57),(57,58),(58,98),(98,138),(138,178),(178,182),(182,183),(183,191),(191,197),(197,213)]
slice_dict = {
'CLM' : slice_list_CLM,
'CTL' : slice_list_CTL,
'ADR' : slice_list_ADR,
'AGR' : slice_list_AGR,
'ACN' : slice_list_ACN,
'CST' : slice_list_CST,
'MCF' : slice_list_MCF,
'DD1' : slice_list_DD1,
'DES' : slice_list_DES,
'IBC' : slice_list_IBC,
'ICD' : slice_list_ICD,
'LEG' : slice_list_LEG,
'LD1' : slice_list_LD1,
'LD2' : slice_list_LD2,
'LD3' : slice_list_LD3,
'NET' : slice_list_NET,
'NOT' : slice_list_NOT,
'OFF' : slice_list_OFF,
'PAY' : slice_list_PAY,
'PRC' : slice_list_PRC,
'ACR' : slice_list_ACR,
'REC' : slice_list_REC,
'RED' : slice_list_RED,
'REI' : slice_list_REI,
'RES' : slice_list_RES,
'RST' : slice_list_RST,
'SPC' : slice_list_SPC,
'SSN' : slice_list_SSN,
'WRK' : slice_list_WRK,
'WD1' : slice_list_WD1,
'WD3' : slice_list_WD3,
}
def slicer(file,slice_list):
csv_string = ""
for i in slice_list:
csv_string += (file[i[0]:i[1]]+",")
return csv_string
overview_loc = 'c:/Users/location/E02_ingestion/'+ 'overview_'+e02_ext #put in file location wehre you would like to see logs
with open(overview_loc, 'w') as overview_file:
for key, value in dict_of_record_lists.items():
overview_file.write((key+' '+(str(len(value)))+'\n'))
for key, value in dict_of_record_lists.items():
for k, v in slice_dict.items():
if key == k:
iteration = 0
for i in value:
s = slicer(i,v)
value[iteration] = s
iteration+= 1
e02_ext = read_loc[int(e02ext_start):]
csv_ext = e02_ext[:-3]+'csv'
# file overview/log that shows how many lines should exist in the other files to ensure everything wrote correctly
overview_loc = 'c:/Users/location/E02_ingestion/'+ 'overview_'+e02_ext #put in file location wehre you would like to see logs
with open(overview_loc, 'w') as overview_file:
for key, value in dict_of_record_lists.items():
overview_file.write((key+' '+(str(len(value)))+'\n'))
# if the list isn't empty writes a new file w/prefix matching key and includes the lines
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/location/E02_ingestion/'+ key +'_'+e02_ext
with open(write_loc, "w", newline='') as parsed_file:
for line in value:
line_pre = "%s\n" % line
parsed_file.write(line_pre[:-1])
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/location/E02_ingestion/'+ key +'_'+csv_ext
with open(write_loc, "w", newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for i in value:
writer.writerow(i)
这是 Excel 和我们的 SQL table:
中的一部分输出示例P A Y 9 0 0 0 4 1 0 0 0 9 5 2 0 7 " " " " " " " "
期望的输出（没有 " 作为空格的占位符）：
P A Y 9 0 0 0 4 1 0 0 0 9 5 2 0 7
如有任何帮助，我们将不胜感激。
为什么：
您面临的问题是，您的 row 已处理数据中的列表条目仅包含 csv.delimiter 字符。该模块然后 引用 它们以区分您的 "delimiter-only-data" 和您的 "delimiter between columns" .
当使用 ' ' 作为分隔符将类似 [ ["PAY","...."," "," "," "," "] ] 的内容写入 csv 时，您会在输出中引用它们：
导入 csv
dict_of_record_lists = {"K": [ ["PAY","...."," "," "," "," "] ] }
for key, value in dict_of_record_lists.items():
write_loc = 't.txt'
with open(write_loc, "w", newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for i in value:
writer.writerow(i)
print( open(write_loc).read()) # PAY .... " " " " " " " "
修正:
您可以通过指定 quoting=csv.QUOTE_NONE 并提供 escapechar = ... 或通过修复您的数据来修复该问题。提供 escapechar 会将其放入您的文件中。
文档的相关部分：csv.QUOTE_NONE.
您可以操纵数据以不包含"only"分隔符作为数据：
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/location/E02_ingestion/'+ key +'_'+csv_ext
with open(write_loc, "w", newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=' ')
for i in value:
# if an inner item only contains delimiter characters, set it to empty string
cleared = [x for x in i if i.strip(" ") else ""]
writer.writerow(cleared)
HTH
独库：
能够将初始文本写作部分更改为：
for key, value in dict_of_record_lists.items():
write_loc = 'c:/Users/Steve Barnard/Desktop/Git_Projects/E02_ingestion/'+ key +'_'+csv_ext
with open(write_loc, "w", newline='') as parsed_file:
for line in value:
line_pre = "%s" % line
parsed_file.write(line_pre[:-1]+'\n')
所有问题都已通过避免 python 的内置 CSV 编写器得到解决。 我的程序在行切片后添加一个逗号的方式，留下一个额外的逗号和'\n';这导致写入函数中的 [:-1] 切片删除了 \n 而不是最后的 ','。通过在逗号删除后添加“\n”，整个问题得到解决，并创建了一个保留间距的功能 CSV。
可以通过写入时换出扩展名来创建文本文件。