我使用的是SAP的数据输出,但是它既不是CSV,也不是包含定界符的字符串,也不是固定宽度,因为它具有多字节字符。这是一种“固定宽度”字符方式。
要将其放入大熊猫中,我目前正在读取文件,获取定界符位置,将定界符周围的每一行切成薄片,然后将其保存为适当的CSV文件,我可以轻松读取它。
我看到熊猫read_csv可以获取文件缓冲区。如何在不保存csv文件的情况下直接将其流传递给它?我应该制造发电机吗?是否可以在不提供文件句柄的情况下获取csv.writer.writerow输出?
这是我的代码:
import pandas as pd
caminho= r'C:\Users\user\Documents\SAP\Tests\\'
arquivo = "ExpComp_01.txt"
tipo_dado = {"KEY_GUID":"object", "DEL_IND":"object", "HDR_GUID":"object", , "PRICE":"object", "LEADTIME":"int16", "MANUFACTURER":"object", "LOAD_TIME":"object", "APPR_TIME":"object", "SEND_TIME":"object", "DESCRIPTION":"object"}
def desmembra(linha, limites):
# This functions receives each delimiter's index and cuts around it
posicao=limites[0]
for limite in limites[1:]:
yield linha[posicao+1:limite]
posicao=limite
def pre_processa(arquivo):
import csv
import os
# Translates SAP output in standard CSV
with open(arquivo,"r", encoding="mbcs") as entrada, open(arquivo[:-3] +
"csv", "w", newline="", encoding="mbcs") as saida:
escreve=csv.writer(saida,csv.QUOTE_MINIMAL, delimiter=";").writerow
for line in entrada:
# Find heading
if line[0]=="|":
delimitadores = [x for x, v in enumerate(line) if v == '|']
if line[-2] != "|":
delimitadores.append(None)
cabecalho_teste=line[:50]
escreve([campo.strip() for campo in desmembra(line,delimitadores)])
break
for line in entrada:
if line[0]=="|" and line[:50]!=cabecalho_teste:
escreve([campo.strip() for campo in desmembra(line, delimitadores)])
pre_processa(caminho+arquivo)
dados = pd.read_csv(caminho + arquivo[:-3] + "csv", sep=";",
header=0, encoding="mbcs", dtype=tipo_dado)
另外,如果您可以分享最佳做法:我有一些奇怪的datetime字符串,20.120.813.132.432
可以使用
dados["SEND_TIME"]=pd.to_datetime(dados["SEND_TIME"], format="%Y%m%d%H%M%S")
dados["SEND_TIME"].replace(regex=False,inplace=True,to_replace=r'.',value=r'')
我无法为其编写解析器,因为我以不同的字符串格式存储了日期。指定在导入期间执行转换的转换器会更快还是还是让熊猫最终按列进行执行?我在代码99999999
中也遇到了类似的问题,必须在上添加点99.999.999
。我不知道该写转换器还是等到导入后再执行df.replace
编辑-样本数据:
| KEY_GUID|DEL_IND| HDR_GUID|Prod_CD |DESCRIPTION | PRICE|LEADTIME|MANUFACTURER| LOAD_TIME|APPR_TIME | SEND_TIME|
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|000427507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123636|Vneráéíoaeot.sadot.m | 29,55 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.157 |
|000527507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123643|Tnerasodaeot|sadot.m | 122,91 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.141 |
|0005DB50112F9E69E10000000A1D2028| |384BB350BF56315DE20062700D627978|75123676|Dnerasodáeot.sadot.m |252.446,99 |3 |POLAND |20.121.226.175.640 |20121226183608|20.121.222.000.015 |
|000627507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123652|Pner|sodaeot.sadot.m | 657,49 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.128 |
|000727507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83| |Rnerasodaeot.sadot.m | 523,63 |30 | |20.120.813.132.432 |20120813132929|20.120.707.010.119 |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| KEY_GUID|DEL_IND| HDR_GUID|Prod_CD |DESCRIPTION | PRICE|LEADTIME|MANUFACTURER| LOAD_TIME|APPR_TIME | SEND_TIME|
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |000827507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123603|Inerasodéeot.sadot.m | 2.073,63 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.127 |
|000927507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123662|Ane|asodaeot.sadot.m | 0,22 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.135 |
|000A27507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123626|Pneraíodaeot.sadot.m | 300,75 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.140 |
|000B27507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83| |Aneraéodaeot.sadot.m | 1,19 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.131 |
|000C27507E64FB29E2006281548EB186| |4C1AD7E25DC50D61E10000000A19FF83|75123613|Cnerasodaeot.sadot.m | 30,90 |30 | |20.120.813.132.432 |20120813132929|20.120.505.010.144 |
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
我将处理具有其他字段的其他表。全部以这种一般形式出现。我只能相信标题中的分隔符。另外,我在数据中可能会有重复的标题。看起来像是矩阵打印输出。
如果您要在不先写入CSV的情况下构建DataFrame,则不需要pd.read_csv
。尽管可以使用io.BytesIO
或cString.StringIO
写入类似文件的内存中对象,但是将迭代的值(如desmembra(line, delimitadores)
)转换为单个字符串只是用来重新解析是没有意义的pd.read_csv
。
相反,它更直接使用pd.DataFrame
,因为它pd.DataFrame
可以接受行数据的迭代器。
使用普通Python一对一地操作值通常不是最快的方法。通常,在整个列上使用Pandas函数会更快。因此,我将首先解析arquivo
为字符串的DataFrame,然后使用Pandas函数将这些列后处理为正确的dtype和值。
import pandas as pd
import os
import csv
import io
caminho = r'C:\Users\u5en\Documents\SAP\Testes\\'
arquivo = os.path.join(caminho, "ExpComp_01.txt")
arquivo_csv = os.path.splitext(arquivo)[0] + '.csv'
def desmembra(linha, limites):
# This functions receives each delimiter's index and cuts around it
return [linha[limites[i]+1:limites[i+1]].strip()
for i in range(len(limites[:-1]))]
def pre_processa(arquivo, enc):
# Translates SAP output into an iterator of lists of strings
with io.open(arquivo, "r", encoding=enc) as entrada:
for line in entrada:
# Find heading
if line[0] == "|":
delimitadores = [x for x, v in enumerate(line) if v == '|']
if line[-2] != "|":
delimitadores.append(None)
cabecalho_teste = line[:50]
yield desmembra(line, delimitadores)
break
for line in entrada:
if line[0] == "|" and line[:50] != cabecalho_teste:
yield desmembra(line, delimitadores)
def post_process(dados):
dados['LEADTIME'] = dados['LEADTIME'].astype('int16')
for col in ('SEND_TIME', 'LOAD_TIME', 'PRICE'):
dados[col] = dados[col].str.replace(r'.', '')
for col in ('SEND_TIME', 'LOAD_TIME', 'APPR_TIME'):
dados[col] = pd.to_datetime(dados[col], format="%Y%m%d%H%M%S")
return dados
enc = 'mbcs'
saida = pre_processa(arquivo, enc)
header = next(saida)
dados = pd.DataFrame(saida, columns=header)
dados = post_process(dados)
print(dados)
产量
KEY_GUID DEL_IND HDR_GUID \
0 000427507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
1 000527507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
2 0005DB50112F9E69E10000000A1D2028 384BB350BF56315DE20062700D627978
3 000627507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
4 000727507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
5 000927507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
6 000A27507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
7 000B27507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
8 000C27507E64FB29E2006281548EB186 4C1AD7E25DC50D61E10000000A19FF83
Prod_CD DESCRIPTION PRICE LEADTIME MANUFACTURER \
0 75123636 Vneráéíoaeot.sadot.m 29,55 30
1 75123643 Tnerasodaeot|sadot.m 122,91 30
2 75123676 Dnerasodáeot.sadot.m 252446,99 3 POLAND
3 75123652 Pner|sodaeot.sadot.m 657,49 30
4 Rnerasodaeot.sadot.m 523,63 30
5 75123662 Ane|asodaeot.sadot.m 0,22 30
6 75123626 Pneraíodaeot.sadot.m 300,75 30
7 Aneraéodaeot.sadot.m 1,19 30
8 75123613 Cnerasodaeot.sadot.m 30,90 30
LOAD_TIME APPR_TIME SEND_TIME
0 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:57
1 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:41
2 2012-12-26 17:56:40 2012-12-26 18:36:08 2012-12-22 00:00:15
3 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:28
4 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-07-07 01:01:19
5 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:35
6 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:40
7 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:31
8 2012-08-13 13:24:32 2012-08-13 13:29:29 2012-05-05 01:01:44
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