Data pipelines are a crucial component of many data-driven applications. They allow us to extract data from various sources, transform and process it, and load it into a destination storage or system. Python has emerged as one of the most popular languages for building data pipelines. In this article, we will explore the basics of …
Problem Statement Link: LeetCode Complexity: Easy Given an array of integers nums which is sorted in ascending order, and an integer target, write a function to search target in nums. If target exists, then return its index. Otherwise, return -1. You must write an algorithm with O(log n) runtime complexity. Example 1: Input: nums = [-1,0,3,5,9,12], target = 9 Output: 4 Explanation: 9 exists in nums and its …
Problem Statement Link: LeetCode Complexity: Hard Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)). Example 1: Input: nums1 = [1,3], nums2 = [2] Output: 2.00000 Explanation: merged array = [1,2,3] and median is 2. Example 2: Input: nums1 = [1,2], nums2 = [3,4] Output: …
Handling missing values is an essential aspect of any data analysis or data engineering project. The presence of missing values can have a significant impact on the outcome of your analysis and the accuracy of your results. Therefore, it is crucial to give proper attention to handling this. In data engineering problem statements, missing values …
Cleaning and transforming data is an essential aspect of data analysis and Pandas provides a range of tools and methods to perform these tasks efficiently. In this section, you’ll learn how to work with Pandas to clean and transform data, from removing missing values and duplicates, to merging and aggregating data. We’ll demonstrate these techniques …
Numpy is a powerful library for mathematical computing and data manipulation in Python. It provides a robust and efficient array data structure for numerical computing, allowing for vectorized operations and efficient computation of mathematical operations on large datasets. With Numpy, you can perform a wide range of operations including statistical analysis, linear algebra, and signal …
Pandas is a powerful and popular library for data analysis and manipulation in Python. It provides easy-to-use data structures and data analysis tools to handle, clean, and process large datasets. With its rich features, pandas makes it easier to explore, manipulate and transform data, making it the go-to tool for data engineers and data scientists. …
Data manipulation and cleaning is a crucial aspect of data engineering, as it prepares raw data for further analysis and modeling. In Python, there are a variety of libraries and tools available that make this process efficient and effective. With libraries like NumPy, pandas, and Openpyxl, data engineers can perform various operations such as reading …
As we have discussed in previous blogposts, Python is a high-level programming language that has gained popularity in recent years due to its versatility and ease of use. It is an interpreted language, which means that it can be executed without the need for compilation, making it a popular choice for data engineers. Data engineering …
Tuple A tuple is a built-in data structure in Python that represents an ordered, immutable collection of elements. Here’s an example of how to create a tuple in Python: You can access elements in a tuple using indexing: Sets A set is a built-in data structure in Python that represents an unordered collection of unique …
