More on Spark DataFrames

1. More on Spark DataFrames

Welcome back! In this video, we'll work with basic DataFrame operations.

2. Creating DataFrames from various data sources

In PySpark, reading data from various formats allows flexibility in handling diverse datasets, each format offering unique benefits CSV files, or Comma-Separated Values files, are widely used because of their simplicity and compatibility across many platforms. They store data in a plain text format, making them easy to read and write without needing specialized tools. However, CSV files lack schema enforcement, which means they don't define or enforce data types for each column, leading to potential inconsistencies. We can read the data using the `read.csv()` function. JSON, or JavaScript Object Notation files, are ideal for representing nested data structures, making them a good choice for data that includes hierarchical relationships or arrays for high compatibilty. However, JSON files can become storage-intensive when scaled to large datasets. We can load them using the `read.json()` function. Parquet is a columnar storage format optimized for read-heavy operations, making it a powerful choice for large datasets that require frequent querying. Additionally, Parquet enforces schema definitions, which helps maintain data consistency and supports complex data types like nested structures, much like JSON. We can load Parquet files using `read.parquet()`. Using these formats, PySpark enables data engineers and scientists to tailor data storage to the needs of their specific applications

3. Schema inference and manual schema definition

Spark can automatically infer schemas, but sometimes it misinterprets data types, particularly with complex or ambiguous data. Manually defining a schema can ensure accurate data handling.

4. DataTypes in PySpark DataFrames

To manually configure a schema, we need to define the datatype using `StructField` function, calling the right datatype method. PySpark DataFrames support various data types, similar to SQL and Pandas. The primary ones are IntegerType for whole numbers, LongType for larger integers, FloatType and DoubleType for decimal numbers, and StringType for strings.

5. DataTypes Syntax for PySpark DataFrames

We import the specific classes from `pyspark.sql.types`. To define the schema for our DataFrame, we use `StructType()` and `StructField()` functions to define the structure and fields of a DataFrame, filling in the columns and their types.

6. DataFrame operations - selection and filtering

Selecting specific columns and filtering rows are fundamental operations in data analysis. With Spark, you can perform these operations on large datasets with efficiency using the `.select()`, `.filter()`, `.sort() `.where()` methods. `.Where()` and `filter()` operates similar to in SQL, where we pass a column or columns and a condition to match.

7. Sorting and dropping missing values

Sorting and handling missing values are common tasks. Dropping nulls can clean data, but in some cases, we may want to fill or impute values instead, which Spark also supports. We can use `.sort()`(for simple flexible sorting) and `.orderby()` (for complex multi-column sorting) methods to sort and order a DataFrame similar to the same commands in SQL. We can use `.na.drop()` to drop all nulls in a DataFrame.

8. Cheatsheet

Here is a cheat sheet to help you.

9. Let's practice!

Let's go see these DataFrames in practice!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.