How to create columns in a dataframe out of columns of another dataframe in PySpark

Question

Assuming that I have the following Spark DataFrame df:

+-----+-------+-------+-------+
| id  | col1  |  col2 |  col3 |
+-----+-------+-------+-------+
| "a" |   10  |  5    |   75  |
| "b" |   20  |  3    |   3   | 
| "c" |   30  |  2    |   65  |
+-----+-------+-------+-------+

I want to create a new dataframe new_df that contains:

1) the id of each row

2) the value of the division between col1 / col2 and

3) the value of the division between col3 / col1

The desired output for new_df is:

+-----+-------+-------+
| id  | col1_2| col3_1|
+-----+-------+-------+
| "a" |  2    |  7.5  |
| "b" |  6.67 |  0.15 | 
| "c" |   15  |  2.17 |
+-----+-------+-------+

I have already tried

new_df = df.select("id").withColumn("col1_2", df["col1"] / df["col2"))

without any luck

Answer 1

Either use select:

df.select('id', 
  (df.col1 / df.col2).alias('col1_2'), 
  (df.col3 / df.col1).alias('col3_1')
).show()
+---+-----------------+------------------+
| id|           col1_2|            col3_1|
+---+-----------------+------------------+
|  a|              2.0|               7.5|
|  b|6.666666666666667|              0.15|
|  c|             15.0|2.1666666666666665|
+---+-----------------+------------------+

Or selectExpr:

df.selectExpr('id', 'col1 / col2 as col1_2', 'col3 / col1 as col3_1').show()
+---+-----------------+------------------+
| id|           col1_2|            col3_1|
+---+-----------------+------------------+
|  a|              2.0|               7.5|
|  b|6.666666666666667|              0.15|
|  c|             15.0|2.1666666666666665|
+---+-----------------+------------------+