Showing posts with the label Pickle and Unpickle

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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 Understand Pickling and Unpickling in Python

Here are the Python pickling and unpickling best examples and the differences between these two. These you can use to serialize and deserialize the python data structures. The concept of writing the total state of an object to the file is called  pickling,  and to read a Total Object from the file is called  unpickling. Pickle and Unpickle The process of writing the state of an object to the file (converting a class object into a byte stream) and storing it in the file is called pickling. It is also called object serialization . The process of reading the state of an object from the file ( converting a byte stream back into a class object) is called unpickling.  It is an inverse operation of pickling. It is also called object deserialization .  The pickling and unpickling can implement by using a pickling module since binary files support byte streams. Pickling and unpickling should be possible using binary files. Data types you can pickle Integers Booleans Complex numbers Floats Nor