Use PySpark Dataframe column in another spark sql query - pyspark

Question

I have a situation where I'm trying to query a table and use the result (dataframe) from that query as IN clause of another query.
From the first query I have the dataframe below:
+-----------------+
|key |
+-----------------+
| 10000000000004|
| 10000000000003|
| 10000000000008|
| 10000000000009|
| 10000000000007|
| 10000000000006|
| 10000000000010|
| 10000000000002|
+-----------------+
And now I want to run a query like the one below using the values of that dataframe dynamically instead of hard coding the values:
spark.sql("""select country from table1 where key in (10000000000004, 10000000000003, 10000000000008, 10000000000009, 10000000000007, 10000000000006, 10000000000010, 10000000000002)""").show()
I tried the following, however it didn't work:
df = spark.sql("""select key from table0 """)
a = df.select("key").collect()
spark.sql("""select country from table1 where key in ({0})""".format(a)).show()
Can somebody help me?

Answer 1

You should use an (inner) join between two data frames to get the countries you would like. See my example:
# Create a list of countries with Id's
countries = [('Netherlands', 1), ('France', 2), ('Germany', 3), ('Belgium', 4)]
# Create a list of Ids
numbers = [(1,), (2,)]
# Create two data frames
df_countries = spark.createDataFrame(countries, ['CountryName', 'Id'])
df_numbers = spark.createDataFrame(numbers, ['Id'])
The data frames look like the following:
df_countries:
+-----------+---+
|CountryName| Id|
+-----------+---+
|Netherlands| 1|
| France| 2|
| Germany| 3|
| Belgium| 4|
+-----------+---+
df_numbers:
+---+
| Id|
+---+
| 1|
| 2|
+---+
You can join them as follows:
countries.join(numbers, on='Id', how='inner')
Resulting in:
+---+-----------+
| Id|CountryName|
+---+-----------+
| 1|Netherlands|
| 2| France|
+---+-----------+
Hope that clears things up!