Esproc vs python 1 addition, deletion, modification and query
1. Add record: add a record to the second position
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4 > A3.insert (2Power100 NAME Eid, "wang": NAME, "lao": SURNAME, "Femal": GENDER, "CA": STATE,date ("1999-1-1"): BIRTHDAY,date ("2009-3-4"): HIREDATE, "HR": DEPT,3000:SALARY) 5=interval@ms (A1 Magazine now ())
A4: add a record (field value before ":" and field after ":"), where 2 indicates the location of the second record
A5: calculate the operation time (interval (): calculate the interval. @ ms means in milliseconds)
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Values= [100,100, "wang", "lao", "Femal", "CA", "1999-01-01", "2009-03-04", "HR", 3000]
Line_dic= {}
For i in range (len (data.columns)):
Line_ [data.columns [I]] = values [I]
Line = pd.DataFrame (line_dic,index= [1])
Data = pd.concat ([data.loc [: 0], line,data.loc [1:]], ignore_index=True)
Print (data)
E=time.time ()
Print (eMurs)
Use pd.concat ([df1,df2, … , dfn) to add new records. Records in dataframe structure are counted from 0. For example, Df.Lok [1:] indicates that all records after the second entry is removed from the slice.
Finally, it is calculated that the operation takes time.
Results:
Esproc
Python
Time-consuming esproc0.004python0.0392. Delete record: delete Article 2 record
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4 > A3.delete (2) 5=interval@ms (A1 Magi now ())
A4: delete Article 2 record
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Data = data.drop (1)
Print (data)
E=time.time ()
Print (eMurs)
Delete a record by using the df.drop () function
Results:
Esproc
Python
Time-consuming esproc0.003python0.0343. Modify the record: change the NAME recorded in Article 5 to aaa,SALARY to 1000
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4 > A3.modify (5, "aaa": NAME,1000:SALARY) 5=interval@ms (A1 now ())
A4: modify the value of the NAME field in the fifth record to "aaa" and the value of the SALARY field to 1000
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Data.loc [4, ['NAME','SALARY']] = [' aaa',1000]
Print (data)
E=time.time ()
Print (eMurs)
The NAME,SALARY field of the fifth record is extracted by df.loc [] slice and assigned to 'aaa' and 1000'.
Results:
Esproc
Python
Time-consuming esproc0.003python0.0374. Query line: query item 2 / 10 record
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4=A3 (to (2je 10)) 5=interval@ms (A1 Magi now ())
A4:to (m) n: we use T to denote the order table and A to represent the sequence. T (A) means to take out the records containing values in the sequence. Here, it means to take out the 2nd to 10th records.
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Data = data.loc [1:9]
Print (data)
E=time.time ()
Print (eMurs)
Using df.loc [] slice to take out the 2nd ~ 10th record
Results:
Esproc
Python
Time-consuming esproc0.003python0.0235. Add column: add a field Fullname
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4=A3.derive (NAME+ "" + SURNAME:Fullname) 5=interval@ms (A1 Magi now ())
A4:derive () adds a field, which means that the Fullname field is generated with the original NAME and SURNAME connection.
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Data ['Fullname'] = data [' NAME'] + data ['SURNAME']
Print (data)
E=time.time ()
Print (eMurs)
Take out NAME and SURNAME and merge them into Fullname
Results:
Esproc
Python
Time-consuming esproc0.004python0.0376. Filter field: filter out field NAME,SURNAME,STATE,GENDER
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4=A3.new (NAME,SURNAME,STATE,GENDER) 5=interval@ms (A1 now ())
A4:T.new () generates a new ordinal table. This means to generate a new sequence table that contains the fields NAME,SURNAME,STATE,GENDER in the A3 sequence table.
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Data = data [['NAME','SURNAME','STATE','GENDER']]
Print (data)
E=time.time ()
Print (eMurs)
Take out the fields of NAME,SURNAME,STATE,GENDER and copy them to the new dataframe.
Results:
Esproc
Python
Time-consuming esproc0.002python0.0227. Modify field name: change EID to ID
Esproc
A1=now () 2=file ("C:\\ Users\\ Sean\\ Desktop\\ esproc_vs_python\\ EMPLOYEE.txt") 3=A2.import@t () 4=A3.rename (EID:ID) 5=interval@ms (A1 now ())
A4:rename () modifies the field name. This means to change EID to ID.
Python:
Import time
Import pandas as pd
Import datetime
Import numpy as np
Import random
S=time.time ()
Data = pd.read_csv ("C:/Users/Sean/Desktop/esproc_vs_python/EMPLOYEE.txt", sep= "\ t")
Data.rename (columns= {'EID':'ID'}, inplace=True)
Print (data)
E=time.time ()
Print (eMurs)
Use the df.rename () function to modify the field name and change EID to ID. The parameter inplace controls whether the original dataframe structure is modified.
Results:
Esproc
Python
Time-consuming esproc0.002python0.030
Summary: through the basic operations of adding, deleting, changing and checking records and fields, we use esproc and python to deal with the same data in the same way of thinking. In terms of description efficiency, there is little difference between them, which is very convenient and easy to use.
EMPLOYEE.txt
