SAS
*In SAS you can use an IF statement in a data step to filter the rows of a dataset based on a given condition. The IF statement is similar to the WHERE statement, but it allows you to specify more complex conditions and to perform different actions depending on the condition.
Here's an example of how to use the IF statement in a data step:
;
data output_dataset;
set input_dataset;
if column1 > 0 and column2 < 10 then output;
run;
This data step will create a new dataset called output_dataset that contains only the rows from the input_dataset where the value in the column1 column is greater than 0 and the value in the column2 column is less than 10. The output statement causes the current row to be written to the output dataset.
*You can also use other comparison operators in the IF statement, such as =, <>, <, >=, and BETWEEN. For example:;
data output_dataset;
set input_dataset;
if column1 between 0 and 10 then output;
run;
*This data step will create a new dataset called output_dataset that contains only the rows from the input_dataset where the value in the column1 column is between 0 and 10, inclusive.;
Here's an example of how to use the IF statement in a data step:
;
data output_dataset;
set input_dataset;
if column1 > 0 and column2 < 10 then output;
run;
This data step will create a new dataset called output_dataset that contains only the rows from the input_dataset where the value in the column1 column is greater than 0 and the value in the column2 column is less than 10. The output statement causes the current row to be written to the output dataset.
*You can also use other comparison operators in the IF statement, such as =, <>, <, >=, and BETWEEN. For example:;
data output_dataset;
set input_dataset;
if column1 between 0 and 10 then output;
run;
*This data step will create a new dataset called output_dataset that contains only the rows from the input_dataset where the value in the column1 column is between 0 and 10, inclusive.;
Python
"""
In pandas, you can use the .query() method to perform a subquery on a DataFrame. This method allows you to filter the rows of a DataFrame based on a given condition, similar to the way you would use a WHERE clause in a SQL statement.
For example, suppose you have a DataFrame df with columns 'A', 'B', and 'C'. To select rows where column 'A' is greater than 0 and column 'B' is less than 10, you can use the following query:
"""
df.query('A > 0 and B < 10')
#You can also use the @ symbol to reference variables in the query string. For #example:
min_val = 0
max_val = 10
df.query('A > @min_val and B < @max_val')
#Note that the .query() method only works with pandas DataFrames, and not with #other types of data structures such as NumPy arrays.
In pandas, you can use the .query() method to perform a subquery on a DataFrame. This method allows you to filter the rows of a DataFrame based on a given condition, similar to the way you would use a WHERE clause in a SQL statement.
For example, suppose you have a DataFrame df with columns 'A', 'B', and 'C'. To select rows where column 'A' is greater than 0 and column 'B' is less than 10, you can use the following query:
"""
df.query('A > 0 and B < 10')
#You can also use the @ symbol to reference variables in the query string. For #example:
min_val = 0
max_val = 10
df.query('A > @min_val and B < @max_val')
#Note that the .query() method only works with pandas DataFrames, and not with #other types of data structures such as NumPy arrays.