NumPy, short for "Numerical Python," is a popular open-source Python library for numerical and scientific computing. NumPy is a fundamental library for data manipulation and scientific computing in Python and is often used in conjunction with other libraries like SciPy, Matplotlib, and pandas.
It provides support for large, multi-dimensional arrays and matrices, along with a wide range of high-level mathematical functions to operate on these arrays. Its efficiency, multi-dimensional array support, and extensive mathematical functions make it a powerful tool for a wide range of applications, from data analysis and machine learning to simulations and scientific research.
This file contain main features, components, operations, functions of Numpy with examples such as
- installing numpy
- creating scalar,vector,matrix,tensor elements using np.array() function
- np.arange(), np.linspace(),np.ones(),np.zero(),np.identity(),np.random.random()
- Array Indexing
- Array Slicing
- Some array attributes like ndim,shape,size,itemsize,dtype,astype
- Array Operations
- Array functions:sum(),prod(),min(),max()
- static related operations like mean(),median(),std(),var()...
- trignometric operations like sin(),cos(),tan()....
- dot(),log(),exp()
- round(),floor(),ceil()
- Iterating through Arrays
- Array reshaping, stacking, splitting
- Broadcasting
- Working with mathematical formulas
- Working with missing values
- Plotting Graphs
- sort(),append(),concatenate(),unique(),expand_dims(),where()
- argmax(),argmin(),cumsum(),cumprod(),percentile(),histogram(),corrcoef()
- set functions and many more functions.......