How can I use Snowpark to perform data wrangling tasks?

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How can I use Snowpark to perform data wrangling tasks?

Daniel Steinhold Asked question September 13, 2023
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Snowpark can be used to perform a variety of data wrangling tasks, such as:

  • Filtering: This is the process of selecting rows from a DataFrame based on a certain criteria. For example, you can filter a DataFrame to only include rows where the age column is greater than 18.
  • Sorting: This is the process of ordering the rows in a DataFrame based on a certain column. For example, you can sort a DataFrame by the name column in ascending order.
  • Aggregating: This is the process of summarizing the data in a DataFrame. For example, you can aggregate a DataFrame by the age column to find the average age.
  • Joining: This is the process of combining two or more DataFrames based on a common column. For example, you can join a DataFrame of customers with a DataFrame of orders to find the orders for each customer.

Here are some examples of how to use Snowpark to perform these data wrangling tasks:

  • Filtering: To filter a DataFrame using Snowpark, you can use the filter() method. The filter() method takes a predicate as its argument. The predicate is a Boolean expression that is evaluated for each row in the DataFrame. If the predicate evaluates to True, the row is included in the filtered DataFrame. For example, the following code filters a DataFrame to only include rows where the age column is greater than 18:

Python

df = session.readTable("mytable", "mydatabase")
filtered_df = df.filter(df["age"] > 18)

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  • Sorting: To sort a DataFrame using Snowpark, you can use the sort() method. The sort() method takes a column name as its argument. The DataFrame is sorted by the column in ascending order. For example, the following code sorts a DataFrame by the name column in ascending order:

Python

df = session.readTable("mytable", "mydatabase")
sorted_df = df.sort("name")

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  • Aggregating: To aggregate a DataFrame using Snowpark, you can use the agg() method. The agg() method takes a list of aggregation functions as its argument. The aggregation functions are applied to the DataFrame and the results are returned as a new DataFrame. For example, the following code calculates the average age for each gender in a DataFrame:

Python

df = session.readTable("mytable", "mydatabase")
aggregated_df = df.agg(df["age"].mean().groupBy("gender"))

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  • Joining: To join two DataFrames using Snowpark, you can use the join() method. The join() method takes the name of the other DataFrame as its argument. The DataFrames are joined on a common column. For example, the following code joins a DataFrame of customers with a DataFrame of orders on the customer_id column:

Python

customers_df = session.readTable("customers", "mydatabase")
orders_df = session.readTable("orders", "mydatabase")
joined_df = customers_df.join(orders_df, "customer_id")

Use code with caution.

These are just a few examples of how to use Snowpark to perform data wrangling tasks. Snowpark provides a rich set of APIs that can be used to perform a variety of data processing tasks.

Daniel Steinhold Changed status to publish September 13, 2023
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