Pandas data analysis (c)

Pandas data conversion, packet, fusion

Pandas Data Fusion

• concat () method
• merge () method

concat

. 1 Frames = [DF, DS]
 2  # Axis. 1 = longitudinal transverse axis = 0 
. 3 pd.concat (Frames, = Axis 0)
 . 4  
. 5  # taken and set 
. 6 pd.concat (Frames, axis = 0, the Join = " Outer " )
 7  # intersected 
. 8 pd.concat (Frames, Axis = 0, the Join = " Inner " )
 . 9  
10 D1 = pd.DataFrame ([22 is, 33 is], index = [ " height " , " weight " ], Columns = [ " Joe Smith " ])
 11pd.DataFrame = D2 ([33 is, 44 is], index = [ " height " , " weight " ], Columns = [ " John Doe " ])
 12 is Frames = [D1, D2]
 13 is  
14 pd.concat (Frames, Axis . 1 =, = the Join " Inner " )
 15  
16 DF1 = pd.DataFrame ({ ' Age ' : [22, 26 ],
 17                                        ' origin ' : [ ' Beijing ' , ' Hebei ' ]},
18                                       index=['Zhang ' , ' Lee ' ])
 . 19 DF2 = pd.DataFrame ({ ' height ' : [175, 180 ],
 20                                        ' weight ' : [70, 85 ]},
 21 is                                        index = [ ' Zhang ' , ' Lee ' ])
 22 is DF3 = pd.DataFrame ({ ' height ' : [175, 183 ],
 23                                        ' weight ' : [70, 87 ]},
 24                                        index = [ 'Zhang' , ' Qianmou ' ])
 25  
26 is pd.concat ([DF1, DF3], Axis =. 1, the Join = " Inner " )

 

merge

. 1 left pd.DataFrame = ({ ' Name ' : [ ' Zhang ' , ' Lee ' , ' segment of a ' ],
 2                                         ' Age ' : [20 is, 26,24 ]}) 
 . 3 right pd.DataFrame = ( { ' name ' : [ ' Zhang ' , ' Lee ' , ' Qianmou ' ],
 4                                           ' origin ' : [ ' Beijing ' , 'Hebei ' ,' Jiangsu ' ]}) 
 . 5  
. 6 pd.merge (left, right, left_index = True, right_on = " name " , How = " Outer " )
 . 7  
. 8  # accordance indexing 
. 9 pd.merge (left, right, left_index = True , right_index = True, How = " Outer " )
 10  # opposite to realize the method according to an example of fusion join simpler, defaults to join left connection 
. 11 left.join (right, How = " Outer " )

 

Data Fusion combine_first instance method to achieve a patch for the missing data by calling the object's data parameter object

. 1 df2.combine_first (df1)   # The values df1 is to fill in missing values df2

Sequence

• sorting the index sort_index
• sort_values ​​sorted according to the value of a column
• Shuffled

  . 1 df.sort_values ( ' Score ' , ascending = False)   # adjusting ascending determines is ascending or descending, the default is True, ascending 
  2  # generates a random reordering column index 
  . 3 Sampler np.random.permutation = (. 3 )
   . 4  Sampler
   . 5 df.take (Sampler)

   

GroupBy technology

• Use GroupBy () method may be grouped along any axis, and the packet based on each of the group name as a key, there are three methods
  • df.groupby(key)
  • df.groupby(key, axis=1)
  • df.groupby([key1, key2])

 

The basic method of data packet

• Packet size and packet sequencing
• The packet iteration
• Column selection within the specified group or groups