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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Spark SQL Query how to write it in Ten steps

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Spark SQL example The post tells how to write SQL query in Spark and explained in ten steps.This example demonstrates how to use sqlContext.sql to create and load two tables and select rows from the tables into two DataFrames. The next steps use the DataFrame API to filter the rows for salaries greater than 150,000 from one of the tables and shows the resulting DataFrame. Then the two DataFrames are joined to create a third DataFrame. Finally the new DataFrame is saved to a Hive table. 1. At the command line, copy the Hue sample_07 and sample_08 CSV files to HDFS: $ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_07.csv /user/hdfs $ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_08.csv /user/hdfs where HUE_HOME defaultsto /opt/cloudera/parcels/CDH/lib/hue (parcel installation) or /usr/lib/hue (package installation). 2. Start spark-shell: $ spark-shell 3. Create Hive tables sample_07 and sample_08: scala> sqlContext.sql("CREATE TABLE sample_07 (code string