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title: Introduction to Data Wrangling with Pandas and Spark

# Introduction to Data Wrangling with Pandas and Spark

**Spark** is a fast, general purpose, distributed data processing system. It is easy to use, easy to integrate, and can analyze data in the following general categories:

— Import data from a variety of sources
— Transform the data into a model that is easier to use
— Discover, understand, and analyze data

But you may have questions around the various data analysis capabilities that Spark has to offer. For example:

— How can I deal with large volumes of data?
— What data sources are available?
— How do I make Spark work with existing data?

You can access the full capabilities of Spark through the **Keras Data Science API**. With the API, you can call **Pandas** data manipulations from your Spark job and then you can analyze the resulting data in Spark.

There are two ways to integrate Spark and Pandas.

— `SparkSession.sparkContext.parallelize()`: You can convert the Pandas data frame to a `RDD` and then convert it to a DataFrame with `DataFrame.fromPandas()`.
— `pandas.to_sql()`: You can pass a SQL query to `DataFrame.toPandas()` and then use Spark SQL.

In the remainder of this lab, you’ll work through an example dataset and learn how to manipulate that data.

# Lab Instructions

You’ll use Pandas and Spark
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