Title
Left JoinsSAS
*This code will create a new dataset called merged_data by merging the data from left_table and right_table using the variable key_variable. The in= option assigns a temporary variable in_left or in_right to each observation indicating which input dataset it came from. The if in_left statement keeps all observations from left_table in the merged dataset and drops any observations from right_table that do not have a match in left_table. This results in a left join, where all observations from left_table are included in the merged dataset, and any observations from right_table that do not have a match in left_table are dropped.
;
data merged_data;
merge left_table(in=in_left) right_table(in=in_right);
by key_variable;
if in_left;
run;
*Note that you can also use the left join statement in SAS SQL to perform a left join:;
proc sql;
create table merged_data as
select *
from left_table
left join right_table
on left_table.key_variable = right_table.key_variable;
quit;
;
data merged_data;
merge left_table(in=in_left) right_table(in=in_right);
by key_variable;
if in_left;
run;
*Note that you can also use the left join statement in SAS SQL to perform a left join:;
proc sql;
create table merged_data as
select *
from left_table
left join right_table
on left_table.key_variable = right_table.key_variable;
quit;
Python
#In Python, use the merge function from the pandas library to perform a left
#join. Here is an example of how to perform a left join using pandas:
import pandas as pd
left_table = pd.DataFrame({
'key_variable': [1, 2, 3, 4],
'left_col1': ['a', 'b', 'c', 'd'],
'left_col2': [1, 2, 3, 4]
})
right_table = pd.DataFrame({
'key_variable': [1, 2, 3, 4, 5],
'right_col1': ['e', 'f', 'g', 'h', 'i'],
'right_col2': [5, 6, 7, 8, 9]
})
merged_data = pd.merge(left_table, right_table, on='key_variable', how='left')
"""
This code will create a new DataFrame called merged_data by merging the data from left_table and right_table using the key_variable column. The how='left' argument specifies that the merge should be a left join, where all rows from left_table are included in the merged DataFrame and any rows from right_table that do not have a match in left_table are dropped.
"""
#Note that you can also use the merge method on a DataFrame to perform a left join:
merged_data = left_table.merge(right_table, on='key_variable', how='left')
#join. Here is an example of how to perform a left join using pandas:
import pandas as pd
left_table = pd.DataFrame({
'key_variable': [1, 2, 3, 4],
'left_col1': ['a', 'b', 'c', 'd'],
'left_col2': [1, 2, 3, 4]
})
right_table = pd.DataFrame({
'key_variable': [1, 2, 3, 4, 5],
'right_col1': ['e', 'f', 'g', 'h', 'i'],
'right_col2': [5, 6, 7, 8, 9]
})
merged_data = pd.merge(left_table, right_table, on='key_variable', how='left')
"""
This code will create a new DataFrame called merged_data by merging the data from left_table and right_table using the key_variable column. The how='left' argument specifies that the merge should be a left join, where all rows from left_table are included in the merged DataFrame and any rows from right_table that do not have a match in left_table are dropped.
"""
#Note that you can also use the merge method on a DataFrame to perform a left join:
merged_data = left_table.merge(right_table, on='key_variable', how='left')