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This article is to share with you about how to achieve SQL query data operation in Pandas, the editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article.

1. Select Row

SELECT * FROM

If you want to select the entire table, simply call the name of the table:

# SQL SELECT * FROM table_df # Pandas table_df

SELECT a, b FROM

If you want to select a specific column from the table, list the column you want in double parentheses:

# SQL SELECT column_a, column_b FROM table_df # Pandas table_df [['column_a',' column_b']]

SELECT DISTINCT

Just use .drop\ u duplicates () to get different values:

# SQL SELECT DISTINCT column_a FROM table_df # Pandas table_df ['column_a']. Drop_duplicates ()

SELECT an as b

Use .rename () if you want to rename the column:

# SQL SELECT column_a as Apple, column_b as Banana FROM table_df # Pandas table_df [['column_a',' column_b']] .rename (columns= {'column_a':' Apple', 'column_b':'Banana'})

SELECT CASE WHEN

For the equivalent of "SELECT CASE WHEN", you can use np.select (), where you first specify the selection and value of each option.

# SQL SELECT CASE WHEN column_a > 30 THEN "Large" WHEN column_a 30, table_df ['column_b']

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