Create Rows based on Column - pyspark

Question

I want to create a row based on a column.
For example - I have the following data frame.
| lookup_name | alt_name | inventory | location |
|-------------|----------|-----------|----------|
| Honda | Car | 1 | au |
| Apple | Fruit | 1 | us |
I want to convert it to the following
| lookup_name | inventory | location |
|-------------|-----------|----------|
| Honda | 1 | au |
| Car | 1 | au |
| Apple | 1 | us |
| Fruit | 1 | us |
Where the alternative name column is removed and the locations and inventory are copied against the new lookup_name entry.

Answer 1

data= [
('Honda', 'Car', 1, 'au'),
('Apple', 'Fruit', 1, 'us'),
]
df = spark.createDataFrame(data, ['lookup_name','alt_name', 'inventory', 'location'])
(
df
.withColumn('lookup_name', explode(array(col('lookup_name'), col('alt_name'))))
.drop('alt_name')
.show(10, False)
)
# +-----------+---------+--------+
# |lookup_name|inventory|location|
# +-----------+---------+--------+
# |Honda |1 |au |
# |Car |1 |au |
# |Apple |1 |us |
# |Fruit |1 |us |
# +-----------+---------+--------+
array(col('lookup_name'), col('alt_name')) => ['Honda', 'Car']
df.withColumn('lookup_name', array(col('lookup_name'), col('alt_name'))).show(10, False)
# +--------------+--------+---------+--------+
# |lookup_name |alt_name|inventory|location|
# +--------------+--------+---------+--------+
# |[Honda, Car] |Car |1 |au |
# |[Apple, Fruit]|Fruit |1 |us |
# +--------------+--------+---------+--------+
pyspark.sql.functions.explode(col)
Returns a new row for each element in the given array or map.