How to calculate mean and standard deviation given a PySpark DataFrame?

How to calculate mean and standard deviation given a PySpark DataFrame?

You can calculate the mean and standard deviation of columns in a PySpark DataFrame using the agg function along with the mean() and stddev() functions from the pyspark.sql.functions module. Here's how you can do it:

from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create a Spark session spark = SparkSession.builder.appName("MeanStdDevExample").getOrCreate() # Sample data data = [(1, 10), (2, 20), (3, 30), (4, 40)] columns = ["id", "value"] df = spark.createDataFrame(data, columns) # Calculate mean and standard deviation result = df.agg(mean("value").alias("mean_value"), stddev("value").alias("stddev_value")) # Show the result result.show()

In this example, we create a PySpark DataFrame df with two columns: 'id' and 'value'. We use the mean() and stddev() functions to calculate the mean and standard deviation of the 'value' column. The agg() function is used to aggregate the results, and we provide alias names using the alias() function to rename the calculated columns in the result.

The output will be something like this:

+----------+-----------+ |mean_value|stddev_value| +----------+-----------+ | 25.0| 12.91| +----------+-----------+

Adjust the column names and data according to your DataFrame. Also, keep in mind that the agg() function can handle more complex aggregation operations, such as calculating these statistics for multiple columns simultaneously or applying other aggregation functions.

Examples

  1. Calculate mean and standard deviation of a column in PySpark DataFrame:

    • Description: Learn how to compute the mean and standard deviation of a specific column in a PySpark DataFrame using built-in statistical functions.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)] df = spark.createDataFrame(data, ["ID", "Value"]) # Calculate mean and standard deviation of "Value" column mean_value = df.select(mean("Value")).collect()[0][0] std_dev = df.select(stddev("Value")).collect()[0][0] print("Mean:", mean_value) print("Standard Deviation:", std_dev)

  2. Python code to compute mean and standard deviation for multiple columns in PySpark DataFrame:

    • Description: Extend the calculation of mean and standard deviation to multiple columns in a PySpark DataFrame, enabling comprehensive statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, 10, 100), (2, 20, 200), (3, 30, 300), (4, 40, 400), (5, 50, 500)] df = spark.createDataFrame(data, ["ID", "Value1", "Value2"]) # Calculate mean and standard deviation for multiple columns mean_std_df = df.select([mean(col).alias(f"Mean_{col}") for col in df.columns[1:]], [stddev(col).alias(f"StdDev_{col}") for col in df.columns[1:]]) mean_std_df.show()

  3. How to compute mean and standard deviation of a PySpark DataFrame column with missing values:

    • Description: Handle missing values gracefully while computing the mean and standard deviation of a column in a PySpark DataFrame, ensuring accurate statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame with missing values data = [(1, 10), (2, None), (3, 30), (4, 40), (5, None)] df = spark.createDataFrame(data, ["ID", "Value"]) # Calculate mean and standard deviation while handling missing values mean_value = df.select(mean("Value")).na.fill(0).collect()[0][0] std_dev = df.select(stddev("Value")).na.fill(0).collect()[0][0] print("Mean:", mean_value) print("Standard Deviation:", std_dev)

  4. Compute mean and standard deviation of PySpark DataFrame column grouped by another column:

    • Description: Calculate the mean and standard deviation of a column in a PySpark DataFrame grouped by another column, facilitating group-wise statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, "A", 10), (2, "B", 20), (3, "A", 30), (4, "B", 40), (5, "A", 50)] df = spark.createDataFrame(data, ["ID", "Group", "Value"]) # Calculate mean and standard deviation grouped by "Group" mean_std_grouped = df.groupBy("Group").agg(mean("Value").alias("Mean"), stddev("Value").alias("StdDev")) mean_std_grouped.show()

  5. Python code to calculate mean and standard deviation of a PySpark DataFrame column with specified sample weights:

    • Description: Compute the mean and standard deviation of a column in a PySpark DataFrame with specified sample weights, allowing for weighted statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)] df = spark.createDataFrame(data, ["ID", "Value"]) # Define sample weights sample_weights = [1, 2, 1, 2, 1] # Calculate mean and standard deviation with sample weights mean_value = df.select(mean("Value", weights=sample_weights)).collect()[0][0] std_dev = df.select(stddev("Value", weights=sample_weights)).collect()[0][0] print("Mean:", mean_value) print("Standard Deviation:", std_dev)

  6. How to compute mean and standard deviation of a PySpark DataFrame column within a specified window:

    • Description: Calculate the mean and standard deviation of a column in a PySpark DataFrame within a specified window, facilitating rolling statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev from pyspark.sql.window import Window from pyspark.sql.functions import col # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)] df = spark.createDataFrame(data, ["ID", "Value"]) # Define window window_spec = Window.orderBy("ID").rowsBetween(-1, 1) # Calculate mean and standard deviation within window mean_value = mean(col("Value")).over(window_spec) std_dev = stddev(col("Value")).over(window_spec) result_df = df.select("ID", "Value", mean_value.alias("Mean"), std_dev.alias("StdDev")) result_df.show()

  7. Python code to compute mean and standard deviation of PySpark DataFrame column with exponential decay:

    • Description: Calculate the mean and standard deviation of a column in a PySpark DataFrame with exponential decay, assigning greater weight to more recent observations.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev, col from pyspark.sql.window import Window # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)] df = spark.createDataFrame(data, ["ID", "Value"]) # Define exponential decay window window_spec = Window.orderBy("ID").rangeBetween(Window.unboundedPreceding, 0) # Calculate mean and standard deviation with exponential decay mean_value = mean(col("Value")).over(window_spec) std_dev = stddev(col("Value")).over(window_spec) result_df = df.select("ID", "Value", mean_value.alias("Mean"), std_dev.alias("StdDev")) result_df.show()

  8. How to calculate mean and standard deviation of PySpark DataFrame column ignoring null values:

    • Description: Compute the mean and standard deviation of a column in a PySpark DataFrame while ignoring null values, ensuring accurate statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev, col # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame with null values data = [(1, 10), (2, None), (3, 30), (4, None), (5, 50)] df = spark.createDataFrame(data, ["ID", "Value"]) # Calculate mean and standard deviation while ignoring null values mean_value = mean(col("Value")).na.drop().collect()[0][0] std_dev = stddev(col("Value")).na.drop().collect()[0][0] print("Mean:", mean_value) print("Standard Deviation:", std_dev)

  9. Python code to calculate mean and standard deviation of PySpark DataFrame column partitioned by another column:

    • Description: Compute the mean and standard deviation of a column in a PySpark DataFrame partitioned by another column, enabling statistical analysis within specific groups.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev, col # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [("Group1", 10), ("Group1", 20), ("Group2", 30), ("Group2", 40), ("Group1", 50)] df = spark.createDataFrame(data, ["Group", "Value"]) # Calculate mean and standard deviation partitioned by "Group" mean_std_partitioned = df.groupBy("Group").agg(mean("Value").alias("Mean"), stddev("Value").alias("StdDev")) mean_std_partitioned.show()

  10. Compute mean and standard deviation of a PySpark DataFrame column using RDD:

    • Description: Calculate the mean and standard deviation of a column in a PySpark DataFrame using RDD (Resilient Distributed Dataset), offering an alternative method for statistical analysis.
    from pyspark.sql import SparkSession from pyspark.sql.functions import mean, stddev # Create SparkSession spark = SparkSession.builder \ .appName("MeanAndStdDev") \ .getOrCreate() # Create a sample DataFrame data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)] df = spark.createDataFrame(data, ["ID", "Value"]) # Compute mean and standard deviation using RDD rdd = df.rdd.map(lambda row: row["Value"]) mean_value = rdd.mean() std_dev = rdd.stdev() print("Mean:", mean_value) print("Standard Deviation:", std_dev)

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