Python for Data Scientists - NumPy
Introduction We'll start our Python for Data Scientists series with NumPy , short for Numerical Python, which is the foundational package for scientific computing in Python. One of its primary purposes with regards to data analysis is as the primary container for data to be passed between algorithms. For numerical data, NumPy arrays are a much more efficient way of storing and manipulating data than the other built-in Python data structures. Also, libraries written in a lower-level language, such as C or Fortran, can operate on the data stored in a NumPy array without copying any data. Here are some of the things it provides: A fast and efficient multidimensional array object ndarray Functions for performing element-wise computations arrays Tools for reading and writing array-based data sets to disk Linear algebra operations, Fourier transform, and random number generation Tools for integrating connecting C, C++, and Fortran code to Python Knowing Numpy is fundam