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The Quick and Easy Way to Analyze Numpy Arrays

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

Top Key Architecture Components in HIVE

5 architectural components present in Hadoop Hive: Shell: allows interactive queries like MySQL shell connected to a database – Also supports web and JDBC clients Driver: session handles, fetch, execute Compiler: parse, plan, optimize Execution engine: DAG of stages (M/R, HDFS, or metadata) Metastore: schema, location in HDFS, SerDe

Data Mode of Hive:
  • Tables
– Typed columns (int, float, string, date, boolean)
– Also, list: map (for JSON-like data)
  • Partitions
– e.g., to range-partition tables by date
  • Buckets
– Hash partitions within ranges (useful for sampling, join optimization)

HIVE Meta Store
  • Database: namespace containing a set of tables
  • Holds table definitions (column types, physical layout)
  • Partition data 
  • Uses JPOX ORM for implementation; can be stored in Derby, MySQL, many other relational databases
Physical Layout of HIVE
  • Warehouse directory in HDFS
– e.g., /home/hive/warehouse
  • Tables stored in subdirectories of warehouse
– Partitions, buckets form subdirectories of tables
  • Actual data stored in flat files
– Control char-delimited text, or SequenceFiles
– With custom SerDe, can use arbitrary format


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