NumPy is a library for scientific computing in Python. It provides tools for working with arrays of data, including functions for mathematical operations, linear algebra, and random number generation.
👉🏻One of the key features of NumPy is its array data structure, which is similar to a list but allows for more efficient mathematical operations on large datasets. NumPy arrays can be created from existing data, such as lists or tuples, using the np.array() function.
👉🏻Once an array has been created, it can be manipulated using various NumPy functions. For example, the np.mean() function can be used to compute the mean of an array, and the np.random.rand() function can be used to generate random numbers.
👉🏻In addition to its array data structure, NumPy also provides a wide range of mathematical functions for working with arrays, such as linear algebra operations, statistical functions, and trigonometric functions. These functions can be applied to arrays element-wise, allowing for efficient computation on large datasets.
Overall, NumPy is a powerful library for working with arrays of data in Python, and is widely used in the fields of scientific computing, data science, and machine learning.
NumPy is a library for scientific computing in Python. It provides tools for working with arrays of data, including functions for mathematical operations, linear algebra, and random number generation.
👉🏻One of the key features of NumPy is its array data structure, which is similar to a list but allows for more efficient mathematical operations on large datasets. NumPy arrays can be created from existing data, such as lists or tuples, using the np.array() function.
👉🏻Once an array has been created, it can be manipulated using various NumPy functions. For example, the np.mean() function can be used to compute the mean of an array, and the np.random.rand() function can be used to generate random numbers.
👉🏻In addition to its array data structure, NumPy also provides a wide range of mathematical functions for working with arrays, such as linear algebra operations, statistical functions, and trigonometric functions. These functions can be applied to arrays element-wise, allowing for efficient computation on large datasets.
Overall, NumPy is a powerful library for working with arrays of data in Python, and is widely used in the fields of scientific computing, data science, and machine learning.
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How Does Bitcoin Work?
Bitcoin is built on a distributed digital record called a blockchain. As the name implies, blockchain is a linked body of data, made up of units called blocks that contain information about each and every transaction, including date and time, total value, buyer and seller, and a unique identifying code for each exchange. Entries are strung together in chronological order, creating a digital chain of blocks. “Once a block is added to the blockchain, it becomes accessible to anyone who wishes to view it, acting as a public ledger of cryptocurrency transactions,” says Stacey Harris, consultant for Pelicoin, a network of cryptocurrency ATMs. Blockchain is decentralized, which means it’s not controlled by any one organization. “It’s like a Google Doc that anyone can work on,” says Buchi Okoro, CEO and co-founder of African cryptocurrency exchange Quidax. “Nobody owns it, but anyone who has a link can contribute to it. And as different people update it, your copy also gets updated.”
