Showing posts with the label python-dictionary

Featured Post

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

Python Subset: How to Get Subset of Dictionary

Here's a sample program to get the python subset. In this case, you'll find logic for dictionary subsets. Dictionary python To illustrate, I have taken a dictionary as below with keys and values. my_first_dict = { 'HP': 100 'IBM': 200 'NTT': 300 'ABC': 400 'GDF': 500 } I want to make a subset of values greater than 100 and less than 400. How can you achieve this? No worries, below, you will find the logic. Logic to get subset out of a dictionary I am using dictionary comprehension to achieve this. Syntax: sub_set = { key:value for key, value in my_first_dict.items() value >100 and value <400} Result References Python Programming: Using Problem-Solving Approach