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SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

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 Here are the five top commonly asked SQL queries in the interviews. These you can expect in Data Analyst, or, Data Engineer interviews. Top SQL Queries for Interviews 01. Joins The commonly asked question pertains to providing two tables, determining the number of rows that will return on various join types, and the resultant. Table1 -------- id ---- 1 1 2 3 Table2 -------- id ---- 1 3 1 NULL Output ------- Inner join --------------- 5 rows will return The result will be: =============== 1  1 1   1 1   1 1    1 3    3 02. Substring and Concat Here, we need to write an SQL query to make the upper case of the first letter and the small case of the remaining letter. Table1 ------ ename ===== raJu venKat kRIshna Solution: ========== SELECT CONCAT(UPPER(SUBSTRING(name, 1, 1)), LOWER(SUBSTRING(name, 2))) AS capitalized_name FROM Table1; 03. Case statement SQL Query ========= SELECT Code1, Code2,      CASE         WHEN Code1 = 'A' AND Code2 = 'AA' THEN "A" | "A

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.


NumPy Python

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 parameter and returns the standard deviation of that given array. 
For example: 
import numpy as np 

arr = np.array([1, 2, 3, 4, 5]) 
std_dev = np.std(arr) 
print(std_dev) 

# Output: 1.5811388300841898

Max


To find the max Numpy Array, you can use the max() function from the Numpy library. 
For example, to find the max value in an array of numbers: 

import numpy as np 
arr = np.array([1, 3, 4, 6, 10]) 
print(np.max(arr)) 
This would output 10, which is the max value of the array.


Min


The easiest way to find the minimum value of a Numpy array is with the np.min() function. This function takes in a Numpy array and returns the minimum value in the array. 

Example: 
import numpy as np 
a = np.array([1, 5, 10, 100, 200]) 
min_val = np.min(a)
 print(min_val) 
# Output: 1

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