Pandas read_table Fehler

Question

Ich versuche, eine tabulatorgetrennte Textdatei in einen Datenrahmen zu lesen.

Dies ist, wie die Datei in Excel aussieht:

CALENDAR_DATE ORDER_NUMBER INVOICE_NUMBER TRANSACTION_TYPE CUSTOMER_NUMBER CUSTOMER_NAME 
5/13/2016 0:00 13867666  6892372    S     2026   CUSTOMER 1 

Import in ein df:

df = p.read_table("E:/FileLoc/ThisIsAFile.txt", encoding = "iso-8859-1") 

Nun ist es nicht die ersten 3 Spalten als Teil des Spaltenindex (siehe df [0] = Transaktionstyp) und alle Header werden umgestellt, um dies widerzuspiegeln.

       CALENDAR_DATE ORDER_NUMBER INVOICE_NUMBER 
5/13/2016 0:00 13867666 6892372  S    2026   CUSTOMER 1 

Ich versuche, die Textdatei zu manipulieren und dann als Endergebnis in eine MySQL-Datenbank zu importieren.

Answer 1

können Sie verwenden read_csv mit Separator 2 und mehr Leerzeichen:

import pandas as pd 
import io 

temp=u"""CALENDAR_DATE ORDER_NUMBER INVOICE_NUMBER TRANSACTION_TYPE CUSTOMER_NUMBER CUSTOMER_NAME 
5/13/2016 0:00 13867666  6892372    S     2026   CUSTOMER 1""" 

#after testing replace io.StringIO(temp) to filename 
df =pd.read_csv(io.StringIO(temp), sep=r'\s{2,}', engine='python', encoding = "iso-8859-1") 
print (df) 
    CALENDAR_DATE ORDER_NUMBER INVOICE_NUMBER TRANSACTION_TYPE \ 
0 5/13/2016 0:00  13867666   6892372    S 

    CUSTOMER_NUMBER CUSTOMER_NAME 
0    2026 CUSTOMER 1 

Wenn Separator tabulator ist, sep='\t' zu verwenden.

EDIT:

ich testen Sie es mit Ihren Daten und es funktioniert:

import pandas as pd 

df = pd.read_csv('test/AnonymizedData.txt', sep='\t') 

print (df) 

    CUSTOMER_NUMBER CUSTOMER_NAME CUSTOMER_BRANCH_CODE CUSTOMER_BRANCH_NAME \ 
0    2026 CUSTOMER 1     83  SALES BRANCH 1 
1    2359 CUSTOMER 2     76  SALES BRANCH 2 
2   100662 CUSTOMER 3     28  SALES BRANCH 3 
3    3245 CUSTOMER 4     84  SALES BRANCH 4 
4    3179 CUSTOMER 5     28  SALES BRANCH 5 
5   39881 CUSTOMER 6     67  SALES BRANCH 6 
6   37020 CUSTOMER 7     58  SALES BRANCH 7 
7    1239 CUSTOMER 8     50  SALES BRANCH 8 
8    2379 CUSTOMER 9     76  SALES BRANCH 9 

    CUSTOMER_CITY CUSTOMER_STATE  ...  PRICING_PRODUCT_TYPE_CODE \ 
0  TOWN 1    CO  ...        11 
1  TOWN 2    OH  ...        11 
2  TOWN 3    ME  ...        11 
3  TOWN 4    IL  ...        11 
4  TOWN 5    NH  ...        11 
5  TOWN 6    TX  ...        11 
6  TOWN 7    NC  ...        11 
7  TOWN 8    NY  ...        11 
8  TOWN 9    OH  ...        11 

    PRICING_PRODUCT_TYPE ORGANIZATION_ID ORGANIZATION_NAME PRODUCT_LINE_CODE \ 
0   DISPOSABLES    83 ORGANIZATIONNAME    891 
1   DISPOSABLES    83 ORGANIZATIONNAME    891 
2   DISPOSABLES    83 ORGANIZATIONNAME    891 
3   DISPOSABLES    83 ORGANIZATIONNAME    891 
4   DISPOSABLES    83 ORGANIZATIONNAME    891 
5   DISPOSABLES    83 ORGANIZATIONNAME    891 
6   DISPOSABLES    83 ORGANIZATIONNAME    891 
7   DISPOSABLES    83 ORGANIZATIONNAME    891 
8   DISPOSABLES    83 ORGANIZATIONNAME    891 

    PRODUCT_LINE ROBOTIC_FLAG Unnamed: 52 Unnamed: 53 Unnamed: 54 
0 PRODUCTNAME    N   N   NaN   3 
1 PRODUCTNAME    N   N   NaN   3 
2 PRODUCTNAME    N   N   NaN   2 
3 PRODUCTNAME    N   N   NaN   7 
4 PRODUCTNAME    N   N   NaN   1 
5 PRODUCTNAME    N   N   NaN   4 
6 PRODUCTNAME    N   N   NaN   3 
7 PRODUCTNAME    N   N   NaN   5 
8 PRODUCTNAME    N   N   NaN   3 

[9 rows x 55 columns]