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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

How to Write ETL Logic in Python: Sample Code to Practice

Here's an example Python code that uses the mysql-connector library to connect to a MySQL database, extract data from a table, transform it, and load it as a JSON file. Here's an example:

Python ETL Sample Code

import mysql.connector

import json

# Connect to the MySQL database

cnx = mysql.connector.connect(user='username', password='password',



# Define a cursor to execute SQL queries

cursor = cnx.cursor()

# Define the SQL query to extract data

query = ("SELECT column1, column2, column3 FROM table_name")

# Execute the SQL query


# Fetch all rows from the result set

rows = cursor.fetchall()

# Transform the rows into a list of dictionaries

result = []

for row in rows:

    result.append({'column1': row[0], 'column2': row[1], 'column3': row[2]})

# Save the result as a JSON file

with open('output.json', 'w') as outfile:

    json.dump(result, outfile)

# Close the cursor and database connection



In this example, you will need to replace username, password, localhost, database_name, table_name, column1, column2, and column3 with the appropriate values for your MySQL database and table. 

The code will extract the data from the specified table, transform it into a list of dictionaries, and save it as a JSON file named output.json.


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