Programming Articles - Page 2875 of 3368

One of the major concern of all IT companies in the security of there data. Multiple hashing techniques are there to project and check our data.What is HashHash is a function which takes variable length sequence of bytes as input and converts it to a fixed length sequence. However, to get your original data(input bytes) back is not easy. For example, x is your input and f is the f is the hashing function, then calculating f(x) is quick and easy but trying to obtain x again is a very time-consuming job.The return value from a hash function is called ... Read More

One of the major concern of all IT companies in the security of there data. Multiple hashing techniques are there to project and check our data.What is HashHash is a function which takes variable length sequence of bytes as input and converts it to a fixed length sequence. However, to get your original data(input bytes) back is not easy. For example, x is your input and f is the f is the hashing function, then calculating f(x) is quick and easy but trying to obtain x again is a very time-consuming job.The return value from a hash function is called ... Read More

Python pickle module is used for serializing and de-serializing python object structures. The process to converts any kind of python objects (list, dict, etc.) into byte streams (0s and 1s) is called pickling or serialization or flattening or marshalling. We can converts the byte stream (generated through pickling) back into python objects by a process called as unpickling.Why Pickle?: In real world sceanario, the use pickling and unpickling are widespread as they allow us to easily transfer data from one server/system to another and then store it in a file or database.Precaution: It is advisable not to unpickle data received ... Read More

In the terminal, if you want to make some texts appears in colored mode, there are numerous ways in python programming to achieve it.Using python modules1.termcolor module: It is the ANSII Color formatting for output in the terminal.import sys from termcolor import colored, cprint text1 = colored('Hello, Tutorialspoint!', 'blue', attrs=['reverse', 'blink']) print(text1) cprint('Hello, Python!', 'blue', 'on_white') print_red_on_blue = lambda x: cprint(x, 'red', 'on_blue') print_red_on_blue('Hello, from Data Science!') print_red_on_blue('Hello, Python!') for i in range(10):    cprint(i, 'green', end=' ') cprint("Attention!", 'blue', attrs=['bold'], file=sys.stderr)ResultRead More

Indexing of ndarrays can be done using the standard python x[obj] syntax, where x is the array and obj the selection.There are three kinds of indexing available −field accessbasic slicingadvanced indexingWhat kind of indexing will be there depends on obj. In this section, we are going to mainly concentrate on basic slicing & advanced indexing.We can divide advanced indexing into two parts −Integer array indexingBoolean indexingBasic SlicingPython basic concept of slicing is extended in basic slicing to n dimensions. As with python slice object which is constructed by giving a start, stop & step parameters to slice function. To get ... Read More

There are numerous ways to create a numpy array. Numpy provides two different kinds of ways to convert a ndarray to 1Darray: that is using the flatten() method and the other using the ravel() method.Example#Import required library, numpy import numpy as np #create an array from a list arr = np.array( [ (2, 7, 3, 4), (5, 6, 9, 1)]) #flatten_output print(arr.flatten()) #ravel_output print(arr.ravel())Output[2 7 3 4 5 6 9 1] [2 7 3 4 5 6 9 1]Now above we can see that both functions return the same list, so the question arises, why two methods for the same ... Read More

Sometimes there is a requirement to convert a string to a number (int/float) in data analysis. For each string, we can assign a unique integer value to differentiate string values.For this, we use the data in Comma Separated Values(CSV) files. Say we have an excel file containing CSV data as follow −CompanyIndustryRecommendationHDFC BankFinanceHoldApolloHealthcareBuyHeroAutomobileUnderperformYes BankFinanceHoldM&MAutomobileUnderperformFortisHealthcareBuyMarutiAutomobileUnderperformAbove is just a few lines from a large dataset, we need to give different recommendation .i.e. Buy, Hold, Underperform etc. integer values, which will link to our metadata. So for the above input, our expected output will be something like −CompanyIndustryRecommendationHDFC BankFinance2ApolloHealthcare1HeroAutomobile3Yes BankFinance2M&MAutomobile3FortisHealthcare1MarutiAutomobile3Here is a way ... Read More

Python memory management is straight forward. You don’t need to worry about memory management, as memory allocation and deallocation is automatic. one of the mechanisms of memory management is garbage collection. Let’s understand different aspects of garbage collection, Garbage collectionIt is the process by which shared computer memory is cleaned which is currently being put to use by a running program when that program no longer needs that memory. With garbage collection, that freed memory can be used by another program.There are two methods used by python for memory management −Reference countingGarbage collectionPython’s garbage collection is automatic but in some ... Read More

What is Regular Expression?In the real world, string parsing in most programming languages is handled by regular expression. Regular expression in a python programming language is a method used for matching text pattern.The “re” module which comes with every python installation provides regular expression support.In python, a regular expression search is typically written as:match = re.search(pattern, string)The re.search() method takes two arguments, a regular expression pattern and a string and searches for that pattern within the string. If the pattern is found within the string, search() returns a match object or None otherwise. So in a regular expression, given a ... Read More

Python is one of the preferred languages among coders for most of the competitive programming challenges. Most of the problems are easily computed in a reasonable time frame using python.For some of the complex problem, writing fast-enough python code is often a challenge. Below are some of the pythonic code constructs that help to improve the performance of your code in competitive coding −1. Strings concatenation: Do not use the below construct.str1 = "" some_list = ["Welcome ", "To ", "Tutorialspoint "] for x in some_list:    str1 += x print(str1)Above method gives huge time overhead.Instead, try to use this ... Read More