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Data Wrangling with DataFrames

General functions

We have used the folling general functions that are quite similar to methods of pandas dataframs:

select(): returns a new DataFrame with the selected columns
filter(): filters rows using the given condition
where(): is just an alias for filter()
groupBy(): groups the DataFrame using specified columns, so we can run aggregation on them
sort(): returns a new DataFrame sorted by the specified columns(s). By default the second parameter 'ascending' is True
dropDuplicates(): returns a new DataFrame with unique rows based on all or just a subset of columns
withColumn(): returns a new DataFrame by adding a column or replacing the existing column that has the same name. The first parameter is the name of the new column, the second is an expression of how to compute it.

Aggregate funcitons

Spark SQL provides built-in methods or the most common aggragations such as count(), countDistinct(), avg(), max(), min(), etc. in the pyspark.sql.functions module. This methods are not the same as the built-in methods in the Python Standard Library, where we can find min() for example as well, hence you need to be careful not to use them interchangeably.

In many cases, there are multiple ways to express the same aggreations. For example, if we would like to compute one type of aggregate for one or more columns of the DataFrame we can just simply chain the aggregate method after a groupBy(). If we would like to use different functions on different columns, agg() comes in handy. For example agg({"salary": "avg", "age": "max"}) computes the average salary and maximum age.

User defined functions (UDF)

In Spark SQL we can define our own functions with the udf method fomr the pyspark.sql.functions module. The default type of the returned variable for UDFs is string. If we need to explicitly do so by using the different types from the pyspark.sql.types module.

Window functions

Window functions are a way to combining the values of ranges of rows in a DataFrame. When defining the window we can choose how to sort and group (with the partitionBy method) the rows and how wide of a window we'd like to use (described by rangeBetween or rowsBetween).

Resilient Distributed Datasets (RDDs)
RDDs are a low-level abstraction of the data. In the first version of Spark, you worked directly with RDDs. You can think of RDDs as long lists distributed across various machines. You can still use RDDs as part of your Spark code although data frames and SQL are easier.

Abdelrahman13-coder / PySpark