avatarJustin Davis

Summary

The provided context discusses techniques for joining PySpark dataframes with multiple conditions, particularly when dealing with null values and varying data resolutions.

Abstract

The text outlines a common challenge in data analysis: merging datasets with differing levels of detail. In this case, the author illustrates how to join weather data with real estate listings using PySpark. The weather dataset contains entries at both state and city levels, with some city fields intentionally left null to represent state-level data. The real estate dataset, however, includes city-level information for each property listing. The author demonstrates that a simple left join in PySpark, which typically ignores null values, is insufficient for this task. Instead, the author proposes using an "or" condition in the join clause to account for both city-level matches and state-level aggregates where city information is null. This approach ensures that all relevant weather data is correctly associated with the corresponding real estate listings, facilitating a more comprehensive analysis for making informed decisions about property investments.

Opinions

  • The author emphasizes the importance of handling null values appropriately in joins when data resolutions differ.
  • The default behavior of left joins in PySpark, which excludes nulls, may lead to loss of valuable information in certain scenarios.
  • The use of an "or" condition in the join clause is presented as a practical solution to include null values in the join operation.
  • The author suggests that this method is particularly useful for datasets with varying granularity, ensuring that analysts do not overlook data at different levels of detail.

Joining Pyspark dataframes with multiple conditions and null values

It is important to be able to join dataframes based on multiple conditions. The default behavior for a left join when one of the join columns is null is to disregard that column and say there is not a match. However, if you have data at different resolutions, you may not want to drop those null values. I will use fabricated real estate and climate information to explain.

Say that you have a dataset with weather data. However your weather dataset for some states is on the state level, but for other states is on a city level:

geo_data = [
    {"state": "CA", "city": "Los Angeles", "average_temp": 66},
    {"state": "HI", "city": None, "average_temp": 70},
    {"state": "CA", "city": "San Francisco", "average_temp": 60},
]
geo_df = spark.createDataFrame(Row(**x) for x in geo_data).cache()
geo_df.show()
+------------+-------------+-----+
|average_temp|         city|state|
+------------+-------------+-----+
|          66|  Los Angeles|   CA|
|          70|         null|   HI|
|          60|San Francisco|   CA|
+------------+-------------+-----+

You want to join this weather data on a list of homes that you are interested in buying. The homes table looks like the following:

house_data = [
    {"mls": 111,"state": "CA", "city": "Los Angeles", "price": 1000000},
    {"mls": 222, "state": "CA", "city": "San Francisco", "price": 1200000},
    {"mls": 333, "state": "HI", "city": "Waimea", "price": 7000000},
    {"mls": 444, "state": "HI", "city": "Hawaii Kai", "price": 8000000},
]
house_df = spark.createDataFrame(Row(**x) for x in house_data).cache()
house_df.show()

+-------------+---+-------+-----+ 
|         city|mls|  price|state| 
+-------------+---+-------+-----+ 
|  Los Angeles|111|1000000|   CA| 
|San Francisco|222|1200000|   CA| 
|       Waimea|333|7000000|   HI| 
|   Hawaii Kai|444|8000000|   HI| 
+-------------+---+-------+-----+

Now what can you do even though the resolution on the weather data does not match up with the resolution on the real estate data? The first thought may to be to use the eqNullSafe() operator in pyspark. But that function only applies when the value is null in both dataframes. Instead you can use an “or” statement in your join conditions

join_conditions = [
    house_df.state == geo_df.state,
    (house_df.city == geo_df.city) | (geo_df.city.isNull())
]
(
    house_df
    .join(
        geo_df,
        join_conditions,
        "left"
    )
    .select(
        "mls", 
        "price", 
        house_df.state, 
        house_df.city, 
        "average_temp"
    )
    .show()
)
+---+-------+-----+-------------+------------+
|mls|  price|state|         city|average_temp|
+---+-------+-----+-------------+------------+
|111|1000000|   CA|  Los Angeles|          66|
|222|1200000|   CA|San Francisco|          60|
|333|7000000|   HI|       Waimea|          70|
|444|8000000|   HI|   Hawaii Kai|          70|
+---+-------+-----+-------------+------------+

There you go! Now you have your important weather data joined on to your house data so you can make a better assessment on where you would like to live next.

Pyspark
Join
Null
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