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Python Articles
Page 791 of 855
How to scrape through Media Files in Python?
IntroductionIn a real world corporate business setting, most data may not be stored in text or Excel files. SQL-based relational databases such as Oracle, SQL Server, PostgreSQL, and MySQL are in wide use, and many alternative databases have become quite popular.The choice of database is usually dependent on the performance, data integrity, and scalability needs of an application.How to do it..In this example we will how to create a sqlite3 database. sqllite is installed by default with python installation and doesn't require any further installations. If you are unsure please try below. We will also import Pandas.Loading data from SQL ...
Read MoreProgram to find start indices of all anagrams of a string S in T in Python
Suppose we have two strings S and T, we have to find all the start indices of S's anagrams in T. The strings consist of lowercase letters only and the length of both strings S and T will not be larger than 20 and 100.So, if the input is like S = "cab" T = "bcabxabc", then the output will be [0, 1, 5, ], as the substrings "bca", "cab" and "abc".To solve this, we will follow these steps:Define a map m, n := size of s, set left := 0, right := 0, counter := size of pdefine an ...
Read MoreHow to select the largest of each group in Python Pandas DataFrame?
IntroductionOne of the most basic and common operations to perform during data analysis is to select rows containing the largest value of some columns within a group. In this post, I will show you how to find the largest of each group within a DataFrame.Problem..Let us understand the task first, assume you are given a movies dataset and requested to list the most popular film of each year based on popularity.How to do it..1.Preparing the data.Well Google is full of datasets. I often use kaggle.com to get the datasets I need for my data analysis. Feel free to login to ...
Read MoreProgram to find numbers represented as linked lists in Python
Suppose we have two singly linked list L1 and L2, each representing a number with least significant digits first, we have to find the summed linked list.So, if the input is like L1 = [5, 6, 4] L2 = [2, 4, 8], then the output will be [7, 0, 3, 1, ]To solve this, we will follow these steps:carry := 0res := a new node with value 0curr := reswhile L1 is not empty or L2 is not empty or carry is non-zero, dol0_val := value of L1 if L1 is not empty otherwise 0l1_val := value of L2 if ...
Read MoreHow to unpack using star expression in Python?
IntroductionOne of the basic limitation of unpacking is that you must know the length of the sequences you are unpacking in advance.How to do it..random_numbers = [0, 1, 5, 9, 17, 12, 7, 10, 3, 2] random_numbers_descending = sorted(random_numbers, reverse=True) print(f"Output *** {random_numbers_descending}")Output*** [17, 12, 10, 9, 7, 5, 3, 2, 1, 0]If I now wanted to find out the largest and second largest from the numbers, we will get an exception "too many values to unpack".print(f"Output *** Getting the largest and second largest") largest, second_largest = random_numbers_descendingOutput*** Getting the largest and second largest--------------------------------------------------------------------------- ValueError Traceback (most recent call last) ...
Read MoreHow to perform Calculations with Dictionaries in Python?
ProblemYou want to perform various calculations (e.g., minimum value, maximum value, sorting, etc.) on a dictionary of data.Solution.We will create a dictionary with tennis players and their grandslam titles.PlayerTitles = { 'Federer': 20, 'Nadal': 20, 'Djokovic': 17, 'Murray': 3, 'Theim' : 1, 'Zverev': 0 }1.We have a dictionary with player names and the grandslam titles won by each player. Now let us try to find out the player with least number of titles#type(PlayerTitles) print(f"Output *** The minimum value in the dictionary is {min(PlayerTitles)} ")Output*** The minimum value in the dictionary is Djokovic2. This is ...
Read MoreHow to compare two DataFrames in Python Pandas with missing values
IntroductionPandas uses the NumPy NaN (np.nan) object to represent a missing value. This Numpy NaN value has some interesting mathematical properties. For example, it is not equal to itself. However, Python None object evaluates as True when compared to itself.How to do it..Let us see some examples to understand how np.nan behaves.import pandas as pd import numpy as np # Python None Object compared against self. print(f"Output *** {None == None} ")Output*** True# Numpy nan compared against self. print(f"Output *** {np.nan == np.nan} ")Output*** False# Is nan > 10 or 1000 ? print(f"Output *** {np.nan > ...
Read MoreHow to use the Subprocess Module in Python?
Understanding Process -When you code and execute a program on Windows, MAC or Linux, your Operating System creates a process(single).It uses system resources like CPU, RAM, Disk space and also data structures in the operating system’s kernel. A process is isolated from other processes—it can’t see what other processes are doing or interfere with them.Note: This code has to be run on Linux like sytems. When executed on windows might throw exceptions.Goals of Operating System -The main twin goals of OS are to spread the work of the process fairly and be responsive to the user. These are acheived by ...
Read MoreHow to process iterators in parallel using ZIP
IntroductionList comprehensions make it easy to take a source list and get a derived list by applying an expression. For example, say that I want to multiply each element in a list with 5. Here, I do this by using a simple for loop.a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] multiply_by_5 = [] for x in a: multiply_by_5.append(x*5) print(f"Output *** {multiply_by_5}")Output*** [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]With a list comprehension, I can achieve the same outcome by specifying the expression and the input sequence to loop over.# List comprehension multiply_by_5 ...
Read MoreHow to process excel files data in chunks with Python?
IntroductionIt seems that the world is ruled by Excel. I've been surprised in my data engineering work to see how many of my colleagues are using Excel as a critical tool for making decisions. While I'm not a big fan of MS Office and their excel spread sheets, i will still show you a neat trick to handle large excel spread sheets effectively.How to do it..Before we jump into the program directly, let us understand few basics on dealing excel spreadsheets with Pandas.1. Installation. Go ahead and install openpyxl and xlwt. If you are unsure if it is installed or ...
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