Path to mathematics-python Data Processing (2)-python Data Processing

Path to mathematics-python Data Processing (2)-python Data Processing

Insert column

#-*-Coding: UTF-8 -*-

"""

Created on Mon Mar 09 11:21:02 2015

@ Author: myhaspl@myhaspl.com

"""

Print u "python data analysis \ n"

Import pandas as pd

Import numpy as np

# Constructing product sales data

Mydf = pd. dataFrame ({u'item region Code': [,], u'item a': np. random. randint (, size = 6), u'item B ': np. random. randint (, size = 6), u'item C': np. random. randint (0,1000, size = 6 )})

Allsales = mydf. values [:, 1] + mydf. values [:, 2] + mydf. values [:, 3]

# Insert a column

Mydf. insert (0, u 'total sales', pd. Series (allsales ))

# Sort by two column names first, by followed by the column name, indicating by column name

Mynewdf = mydf. sort_index (axis = 0, by = [u 'region Code', u 'total sales'], ascending = [True, False])

Print mynewdf

 

Python Data Analysis

 

Total sales product A product B Product C product region code

0 1436 805 858 577 1

1 1370 422 606 763 1

3 397 445 41 354 2

5 851 737 629 219 3

2 815 682 133 679 3

4 749 999 521 224 4

>>>

 

The following is an example.

#-*-Coding: UTF-8 -*-

"""

Created on Mon Mar 09 11:21:02 2015

@ Author: myhaspl@myhaspl.com

"""

All content of this blog is original, if reproduced please indicate the source http://blog.csdn.net/myhaspl/

Print u "python data analysis \ n"

Import pandas as pd

Import numpy as np

# Constructing product sales data

Mydf = pd. dataFrame ({u'item region Code': [,], u'item a': np. random. randint (, size = 6), u'item B ': np. random. randint (, size = 6), u'item C': np. random. randint (0,1000, size = 6 )})

Allsales = mydf. values [:, 1] + mydf. values [:, 2] + mydf. values [:, 3]

# Insert a column

Mydf. insert (0, u 'total sales', pd. Series (allsales ))

Print mydf

# Deleting Columns

Mynewdf = mydf. drop ([u 'total sales'], axis = 1)

Print mynewdf

# Moving Columns

Myb = mynewdf. pop (u'item B ')

Mynewdf. insert (2, u 'item B ', myb)

Print mynewdf

 

The result is as follows:

Python Data Analysis

 

Total sales product A product B Product C product region code

0 964 80 940 23 1

1 1188 373 450 737 1

2 1137 907 642 492 3

3 1001 646 952 47 2

4 899 526 19 876 4

5 1225 342 430 792 3

Product A product B Product C product region code

0 80 940 23 1

1 373 450 737 1

2 907 642 492 3

3 646 952 47 2

4 526 19 876 4

5 342 430 792 3

Product A product C product B Product region code

0 80 23 940 1

1 373 737 450 1

2 907 492 642 3

3 646 47 952 2

4 526 876 19 4

5 342 792 430 3

>>>