# Why Numpy is faster in Python?

NumPy is a Python fundamental package which used for efficient manipulations and operations on High-level mathematical functions, Multi-dimensional arrays, Linear algebra, Fourier Transformations, Random Number Capabilities, etc. It provides tools for integrating C, C++ and Fortran code in Python. NumPy is mostly used in Python for scientific computing.

Let us look at the below program which compares NumPy Arrays and Lists in Python in terms of execution time.

 `# importing required packages ` `import` `numpy ` `import` `time ` ` `  `# size of arrays and lists ` `size ``=` `1000000`    ` `  `# declaring lists ` `list1 ``=` `range``(size) ` `list2 ``=` `range``(size) ` ` `  `# declaring arrays ` `array1 ``=` `numpy.arange(size)   ` `array2 ``=` `numpy.arange(size) ` ` `  `# list ` `initialTime ``=` `time.time() ` `resultantList ``=` `[(a ``*` `b) ``for` `a, b ``in` `zip``(list1, list2)] ` ` `  `# calculating execution time ` `print``(``"Time taken by Lists :"``,  ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` ` `  `# NumPy array ` `initialTime ``=` `time.time() ` `resultantArray ``=` `array1 ``*` `array2 ` ` `  `# calculating execution time  ` `print``(``"Time taken by NumPy Arrays :"``, ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) `

Output:

```Time taken by Lists : 1.1984527111053467 seconds
Time taken by NumPy Arrays : 0.13434123992919922 seconds```

From the output of the above program, we see that the NumPy Arrays execute very much faster than the Lists in Python. There is a big difference between the execution time of arrays and lists.

NumPy Arrays are faster than Python Lists because of the following reasons:

• An array is a collection of homogeneous data-types which are stored in contagious memory locations, on the other hand, a list in Python is collection of heterogeneous data types stored in non-contagious memory locations.
• The NumPy package breakdowns a task into multiple fragments, and then processes all the fragments parallelly.
• The NumPy package integrates C, C++ and Fortran codes in Python, these programming languages have very less execution time as compared to python.

Below is a program which compares the execution time of different operations on NumPy arrays and Python Lists:

 `# importing required packages ` `import` `numpy ` `import` `time ` `  `  ` `  `# size of arrays and lists ` `size ``=` `1000000`   `  `  `# declaring lists ` `list1 ``=` `[i ``for` `i ``in` `range``(size)] ` `list2 ``=` `[i ``for` `i ``in` `range``(size)] ` ` `  `# decalring arrays ` `array1 ``=` `numpy.arange(size) ` `array2 ``=` `numpy.arange(size) ` ` `  `# Concatenation ` `print``(``"\nConcatenation:"``) ` ` `  `# list ` `initialTime ``=` `time.time() ` `list1 ``=` `list1 ``+` `list2 ` ` `  `# calculating execution time ` `print``(``"Time taken by Lists :"``, ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` `  `  `# NumPy array ` `initialTime ``=` `time.time() ` `array ``=` `numpy.concatenate((array1, array2), ` `                          ``axis ``=` `0``) ` ` `  `# calculating execution time  ` `print``(``"Time taken by NumPy Arrays :"``,  ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` ` `  ` `  `# Dot Product ` `dot ``=` `0` `print``(``"\nDot Product:"``) ` ` `  `# list ` `initialTime ``=` `time.time() ` `for` `a, b ``in` `zip``(list1, list2): ` `        ``dot ``=` `dot ``+` `(a ``*` `b) ` `         `  `# calculating execution time ` `print``(``"Time taken by Lists :"``,  ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` `  `  `# NumPy array ` `initialTime ``=` `time.time() ` `array ``=` `numpy.dot(array1, array2) ` ` `  `# calculating execution time  ` `print``(``"Time taken by NumPy Arrays :"``, ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` ` `  ` `  `# Scalar Addtion  ` `print``(``"\nScalar Addtion:"``) ` ` `  `# list ` `initialTime ``=` `time.time() ` `list1 ``=``[i ``+` `2` `for` `i ``in` `range``(size)] ` ` `  `# calculating execution time ` `print``(``"Time taken by Lists :"``, ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` `  `  `# NumPy array ` `initialTime ``=` `time.time() ` `array1 ``=` `array1 ``+` `2` ` `  `# calculating execution time  ` `print``(``"Time taken by NumPy Arrays :"``,  ` `      ``(time.time() ``-` `initialTime),  ` `      ``"seconds"``) ` ` `  ` `  `# Deletion ` `print``(``"\nDeletion: "``) ` ` `  `# list ` `initialTime ``=` `time.time() ` `del``(list1) ` ` `  `# calculating execution time ` `print``(``"Time taken by Lists :"``, ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) ` `  `  `# NumPy array ` `initialTime ``=` `time.time() ` `del``(array1) ` ` `  `# calculating execution time  ` `print``(``"Time taken by NumPy Arrays :"``,  ` `      ``(time.time() ``-` `initialTime), ` `      ``"seconds"``) `

Output:

```Concatenation:
Time taken by Lists : 0.02946329116821289 seconds
Time taken by NumPy Arrays : 0.011709213256835938 seconds

Dot Product:
Time taken by Lists : 0.179551362991333 seconds
Time taken by NumPy Arrays : 0.004144191741943359 seconds

Time taken by Lists : 0.09385180473327637 seconds
Time taken by NumPy Arrays : 0.005884408950805664 seconds

Deletion:
Time taken by Lists : 0.01268625259399414 seconds
Time taken by NumPy Arrays : 3.814697265625e-06 seconds```

From the above program, we conclude that operations on NumPy arrays are executed faster than Python lists, moreover, the Deletion operation has the highest difference in execution time between an array and a list as compared to other operations in the program.

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