Get pandas data merge in one article

Get pandas data merge in one article

In the actual processing of data business requirements, we often encounter such a requirement: connect multiple tables and then perform data processing and analysis, similar to the connection query function in SQL.
Pandas also provides several methods to achieve this function. The most prominent and widely used method is merge. In this article, the following four methods and parameters will be explained in detail through actual cases.

• merge
• append
• join
• Concat
  
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  Article Directory

  
  
  These two libraries must be imported when importing the library for data analysis, and the international practice is general.

import pandas as pd
import numpy as np

— 01 —

merge

Official parameters The parameters
of the merge function officially provided are as follows:

The following will explain the use of several important parameters through cases:

DataFrame.merge(left, right, 
                how='inner',   # {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’
                on=None, 
                left_on=None, right_on=None, 
                sort=False, 
                suffixes=('_x', '_y'))

Simulation data

Note the difference between the 4 sets of data

Use default parameters

Two different writing methods have the same effect

Parameter how

• There are 4 values ​​for the how parameter:
• inner (default)
• outer
• right
• left
  
  
  
  

  Parameter on

The column index column name used for connection must exist in the two dataframe data at the same time, similar to the same field attributes of the two tables in SQL.
If not specified or other parameters are not specified, the two dataframe data Same key as connecting key

The on parameter is a single field

Another example: the


on parameter is a list of multiple fields

Parameter lefton/righton

Parameter suffixes

When merging, one column and two tables have the same name, but the values ​​are different. If you want to save both, use the suffix method, the default is _x, _y, you can specify it yourself

Parameter sort

Sort the values ​​of the same key when connecting

— 02 —

concat

Official parameters

The concat method is to merge the data in the two DataFrame data frames

• Specify whether to merge in the row or column direction through the axis parameter
• Parameter ignore_index realizes merged index rearrangement
  

  Generate data

  

  Specify merge axis

  

  Change index

  
  join parameters
  
  
  

  sort-attribute sort

  
  

  — 03 —

  append

  Official parameters

  

  Basic use

data3.append(data4)   # 等同于pd.append([data3, data4])  忽略pandas版本的警告

Change index-natural number sort

data3.append(data4, ignore_index=True)  # 设置参数

sort=True-sort of attributes

data3.append(data4)   # 默认对字段属性排序

— 04 —

join

Official parameters

Merge through the same index

Same field attribute refers to suffix

The same field becomes index

Keep the same field once

To facilitate your practice, you can reply to "20200917" in the backstage of the public account "Python Data Way" to get the source code file of this article.
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