NumPy (Numerical Python) is a fundamental Python library for scientific computing and numerical operations. It provides support for large, multi-dimensional arrays and matrices, along with a vast collection of high-level mathematical functions to operate on these arrays efficiently.

Key Features of NumPy:

1. ndarray (N-dimensional array):

   • NumPy's core feature is the ndarray, a fast and memory-efficient array object for handling large datasets.
   • Supports vectorized operations (eliminating the need for loops).

2. Mathematical Functions:

   • Provides linear algebra, Fourier transforms, random number generation, and more.
   • Optimized for performance (written in C and Fortran).

3. Broadcasting:

   • Allows operations on arrays of different shapes.

4. Interoperability:

   • Works well with other libraries like Pandas, SciPy, Matplotlib, TensorFlow, and PyTorch.

5. Memory Efficiency:

   • Uses contiguous memory blocks, making it faster than Python lists.

Tutorial Source

Numpy Full Course

File List

• 00_numpy_intro.ipynb:
  • NumPy introduction
• 01_creating_arrays.ipynb:
  • NumPy array definition
  • Understanding Dimensions in Arrays
  • Create Array Using arange
  • Create Array Using linspace
  • Creating Array Using zeros
  • Creating Array Using ones
  • Get Array Size
  • Create Array Using random
• 03_slicing_and_indexing.ipynb:
  • Basic Indexing
  • Slicing Array
  • Advanced Indexing
  • Key Differences Between Indexing & Slicing
  • Notes on Slicing and Indexing
• 04_shaping_arrays.ipynb:
  • Array Shape concept
  • Check array shape using shape
  • Reshape an array using reshape
  • Flatten an array using flatten
  • Resize an array using resize
  • Transpose an array using transpose and ndarray.T attribute
  • Swap axes an array swapaxes
• 05_stacking_and_splitting.ipynb:
  • Stacking array using:
    • stack
    • vstack
    • dstack
  • Splitting array using:
    • split
    • vsplit
    • hsplit
    • dsplit
  • Real-World Applications of Stacking and Splitting Arrays in NumPy
• copying_array.ipynb:
  • Copying array using:
    • copy
    • np.copy
• 07_basic_math.ipynb:
  • Arithmetic Operations using :
    • add
    • subtract
    • multiply
    • divide
    • power
    • sqrt
    • sin
    • sbs
  • Aggregation Operations using:
    • sum
    • min
    • max
    • mean
    • std
    • argmax
  • Matrix Operations using:
    • dot
  • Cumulative Sum using:
    • cumsum
  • Logarithmic Operations using:
    • log
    • log2
    • log10
  • Greatest Common Divisor (GCD) using:
    • gcd.reduce
  • Least Common Multiple (LCM) using:
    • lcm.reduce
  • Rounding Functions using:
    • ceil
    • floor
• 08_numpy_and_panda.ipynb:
  • todo
• 09_statistic_functions.ipynb:
  • Calculate mean using mean
  • Calculate median using median
  • Calculate variance using var
  • Calculate standard deviation using std
  • Calculate minimun using min
  • Calculate maximum using max
  • Calculate product using prod
  • Calculate quantiles using quantile
  • Calculate correlation using corrcoef
  • Calculate covariance using cov
  • Create histogram using histogram and histogram2d
  • Calculate convolution using convolve
• 10_trigonometric_functions.ipynb:
  • Basic Trigonometric Functions:
    • sin
    • cos
    • tan
  • Inverse Trigonometric Functions:
    • arcsin
    • arccos
    • arctan
    • arctan2
  • Hyperbolic Trigonometric Functions:
    • sinh
    • cosh
    • tanh
  • Angle Conversion Functions:
    • deg2rad
    • rad2deg
• 11_matrix_functions.ipynb:
  • Matrix Creation:
    • numpy.array()
    • numpy.matrix()
    • numpy.eye()
    • numpy.zeros()
    • numpy.ones()
  • Matrix Operations:
    • numpy.dot()
    • numpy.transpose() or .T
    • numpy.linalg.inv()
    • numpy.linalg.det()
    • numpy.trace()
    • numpy.linalg.multi_dot
  • Eigenvalues & Eigenvectors:
    • numpy.linalg.eig()
    • numpy.linalg.eigvals()
  • Matrix Decomposition:
    • numpy.linalg.svd()
    • numpy.linalg.qr()
    • numpy.linalg.cholesky()
  • Solving Linear Equations:
    • numpy.linalg.solve()
    • numpy.linalg.lstsq()
  • Matrix Norms:
    • numpy.linalg.norm()
  • Matrix Exponentiation
    • numpy.linalg.matrix_power()
• 12_saving_and_loading.ipynb:
  • Saving NumPy Object using save
  • Loading Numpy Object using load
  • Saving Numpy Object as CSV using savetxt
  • Loading CSV file loadtxt
• 13_numpy_financial.ipynb:
  • Install numpy_financial library pip install numpy-financial
  • Calculates the future value of an investment using fv function
  • Calculates the present value of an investment using pv function
  • Calculates the payment for a loan using pmt function
  • Calculates the net present value of a cash flow series using npv function
  • Calculates the internal rate of return for a series of cash flows using irr function
  • Calculates the number of periods for an investment using nper function
  • Calculates the interest rate per period using ratefunction
• 14_comparison_functions.ipynb:
  • Comparison Functions:
    • numpy.equal or ==
    • numpy.not_equal or !=
    • numpy.greater or >
    • numpy.greater_equal or >=
    • numpy.less or <
    • numpy.less_equal or <=
    • numpy.array_equal
    • numpy.logical_and
    • numpy.logical_or
    • numpy.logical_not
    • numpy.isclose