merge()
import pandas as pd (DateFrame1,DateFrame2,on = ' ',how = ' ')
merge is the method used in pandas toincorporationFunctions for data, unlike concat which is merged according to a particular row or column, connect data according to a specific field in the data.
The meaning of specific parameters, with examples, at a glance!
give an example
Let's start by listing two DataFrames
import pandas as pd
df_1 = ({'Name': ["Little Ming.","Little Red.","Kong."],
'Age': [10,9,12],
'The City': ['Shanghai','Beijing','Shenzhen']})
df_1
name and surname age municipalities
0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10 Shanghai
1 little red 9 Beijing, capital of People's *
2 Xiaogang (1976-), * politician, former vice-president of the PRC from 2008 12 Shenzhen subprovincial city in Guangdong, special economic zone close *df_2 = ({'pocket money': [50,200,600,400,80],
'The City': ['Suzhou','Beijing','Shanghai','Guangzhou','Chongqing']})
df_2
allowance municipalities
0 50 Suzhou prefecture level city in Jiangsu
1 200 Beijing, capital of People's *
2 600 Shanghai
3 400 Guangzhou subprovincial city and capital of Guangdong
4 80 Chongqingon means to find the same field according to that feature
# Both DataFrames have a "city", and the "city" has the same elements, which can be stitched together according to those same elements. result = (df_1,df_2, on = 'The City') result name and surname age municipalities allowance 0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10 Shanghai 600 1 little red 9 Beijing, capital of People's * 200
If there is no on, it automatically looks for the same field
# If there's no on, it's automatically looking for the same field # result = (df_1,df_2) result name and surname age municipalities allowance 0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10 Shanghai 600 1 little red 9 Beijing, capital of People's * 200
how refers to the way two DateFrames are stitched together
-
how = ‘outer’:external, equivalent to two DateFrames for concatenation -
how = ‘right’: right-set, merged, according to the right-most non-empty sample -
how = ‘left’: left-placed, merged and displayed according to the leftmost non-empty sample -
how = ‘inner’: Show samples of only matched fields
# External, equivalent to two DateFrames in concatenation result = (df_1,df_2, on = 'The City', how = 'outer') result name and surname age municipalities allowance 0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10.0 Shanghai 600.0 1 little red 9.0 Beijing, capital of People's * 200.0 2 Xiaogang (1976-), * politician, former vice-president of the PRC from 2008 12.0 Shenzhen subprovincial city in Guangdong, special economic zone close * NaN 3 NaN NaN Suzhou prefecture level city in Jiangsu 50.0 4 NaN NaN Guangzhou subprovincial city and capital of Guangdong 400.0 5 NaN NaN Chongqing 80.0
# Right-set. result = (df_1,df_2, on = 'The City', how = 'right') result name and surname age municipalities allowance 0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10.0 Shanghai 600 1 little red 9.0 Beijing, capital of People's * 200 2 NaN NaN Suzhou prefecture level city in Jiangsu 50 3 NaN NaN Guangzhou subprovincial city and capital of Guangdong 400 4 NaN NaN Chongqing 80
# Left result = (df_1,df_2, on = 'The City', how = 'left') result name and surname age municipalities allowance 0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10 Shanghai 600.0 1 little red 9 Beijing, capital of People's * 200.0 2 Xiaogang (1976-), * politician, former vice-president of the PRC from 2008 12 Shenzhen subprovincial city in Guangdong, special economic zone close * NaN
# how = 'inner' to show only samples with the same fields result = (df_1,df_2, on = 'The City', how = 'inner') result name and surname age municipalities allowance 0 Xiaoming (1904-1971), Soviet trained Chinese * leader, a martyr of the Cultural Revolution 10 Shanghai 600 1 little red 9 Beijing, capital of People's * 200
The above is a personal experience, I hope it can give you a reference, and I hope you can support me more.