Suppose we have a 2D matrix of numbers, now for each zero in the given matrix and replace all values in its row and column with zero, and return the final matrix.So, if the input is like matrix, then the output will be matrix as the 0th, 2nd and 3rd rows contain 0 and the final matrix contains 0 in those rows. Similarly 0th, 1st and 2nd columns contain 0 and the final matrix contains 0 in those columns.To solve this, we will follow these steps:n := row count, m := column count res := make a matrix of size ... Read More

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 ... Read More

ProblemYou need to implement immutable data structures in Python.Introduction..Immutable data structures are very handy when you want to prevent multiple people modifying a piece of data in parallel programming at same time. Mutable data structures( e.g. Array) can be changed at any time while immutable data structures cannot be.How to do it..Let me show you step by step how to deal with immutable and mutable data structures.Example# STEP 01 - Create a Mutable array. # Define an array atp_players = ['Murray', 'Nadal', 'Djokovic'] print(f" *** Original Data in my array is - {atp_players}")*** Original Data in my array is ... Read More

ProblemYou want to create a compress files in python.IntroductionZIP files can hold the compressed contents of many other files. Compressing a file reduces its size on disk, which is useful when transferring it over the internet or between the systems using Control-m AFT or Connect direct or even scp.Python programs creates ZIP files using functions in the zipfile module.How to do it...1. We will be using zipfile and io packages. Install them with pip if any of the packages are missing on your system. If you are unsure, use pip freeze command to validate the packages.2. We will write a ... Read More

How to append new rows to DataFrame using a Template In Python Pandas.IntroductionBeing a data engineering specialist, i often end up creating more derived columns than rows as the role of creating and sending the data to me for analysis should be taken care of other database specialists. However, it is not true during all time.We have to create sample rows rather than waiting for data specialists team to send us the data. In this topic i will be showing the neat tricks for creating rows.How to do it..In this recipe, we will begin by appending rows to a small ... Read More

ProblemYou need to extract the HTML tables from a web page.IntroductionThe internet, and the World Wide Web (WWW), is the most prominent source of information today. There is so much information out there, it is just very hard to choose the content from so many options. Most of that information is retrievable through HTTP.But we can also perform these operations programmatically to retrieve and process information automatically.Python allows us to do this using its standard library an HTTP client, but the requests module helps in obtaining web pages information very easy.In this post, we will see how to parse through ... Read More

Sometimes during our data analysis, we need to look at the duplicate rows to understand more about our data rather than dropping them straight away.Luckily, in pandas we have few methods to play with the duplicates..duplciated()This method allows us to extract duplicate rows in a DataFrame. We will use a new dataset with duplicates. I have downloaded the Hr Dataset from link.import pandas as pd import numpy as np # Import HR Dataset with certain columns df = pd.read_csv("https://raw.githubusercontent.com/sasankac/TestDataSet/master/HRDataset.csv", usecols = ("Employee_Name""PerformanceScore", "Position", "CitizenDesc")) #Sort the values on employee name and make it permanent df.sort_values("Employee_Name"inplace = True) df.head(3)Employee_NamePositionCitizenDescPerformanceScore0AdinolfiProduction ... Read More

IntroductionPandas have a dual selection capability to select the subset of data using the Index position or by using the Index labels. Inthis post, I will show you how to "Select a Subset Of Data Using lexicographical slicing".Google is full of datasets. Search for movies dataset in kaggle.com. This post uses the movies data set from kaggle.How to do it1. Import the movies dataset with only the columns required for this example.import pandas as pd import numpy as np movies = pd.read_csv("https://raw.githubusercontent.com/sasankac/TestDataSet/master/movies_data.csv", index_col="title", usecols=["title", "budget", "vote_average", "vote_count"]) movies.sample(n=5)titlebudgetvote_averagevote_countLittle Voice06.661Grown Ups 2800000005.81155The Best Years of Our Lives21000007.6143Tusk28000005.1366Operation Chromite05.8292. I always recommend ... Read More

IntroductionPandas have a dual selection capability to select the subset of data using the Index position or by using the Index labels. Inthis post, I will show you how to “Select a Subset Of Data Using Index Labels” using the index label.Remember, Python dictionaries and lists are built-in data structures that select their data either by using the index label or byindex position. A dictionary’s key must be a string, integer, or tuple while a List must either use integers (the position) or sliceobjects for selection.Pandas have .loc and.iloc attributes available to perform index operations in their own unique ways. ... Read More

This article is aimed at developers who want to find the largest or smallest items with Python. I will show a few methods touse and will conclude the best method for you.Method – 1: Slice approach on a ListIf you are simply trying to find the single smallest or largest item i.e N = 1, it is faster to use min() and max().Let us begin by generating some random integers.import random # Create a random list of integers random_list = random.sample(range(1, 10), 9) random_listOutput[2, 4, 5, 1, 7, 9, 6, 8, 3] FINDING THE SMALLEST & LARGEST ITEM (N=1) # ... Read More