Numpy is a Python library for numerical computations, providing multidimensional arrays and routines for processing them. Key functions include np.arange for spacing values, np.random for random float generation, and various mathematical operations such as arithmetic and trigonometric functions. The library also supports advanced indexing methods, including boolean and fancy indexing, allowing for efficient data manipulation.

2.  NumPy is a Python package. It stands for
'Numerical Python'. It is a library consisting of
multidimensional array objects and a collection of
routines for processing of array.

3.  Open command prompt and set current working
directory as
C:UsersUserNameAppDataLocalProgramsPyt
honPython310Scripts>
 Then enter the following command
pip install numpy

4.  Python have strings, integer, float, boolean and complex.
 NumPy has some extra data types, and refer to data types
with following character:
i - integer M - datetime
b - boolean O - object
u - unsigned integer S - string
f - float U - unicode string
c - complex float
V - fixed chunk of memory for
other type (void)
m - timedelta

5.  np.arange() - used to get evenly spaced values
within a given interval.
numpy.arange([start, ]stop, [step, ]dtype=None)
 numpy.random.random() - used to generate
random floats number in the half-open interval
(0.0, 1.0).
numpy.random.random([size])

6.  NumPy is an N-dimensional array type called
ndarray.
 It describes the collection of items of the same
type.
numpy.array(object, dtype = None, copy = True,
order = None, subok = False, ndmin = 0)

7.  Contents of ndarray object can be accessed and
modified by indexing or slicing, just like Python's
in-built container objects.
 slice(start,stop,step)

8.  You can index specific values from
a NumPy array using another NumPy array of
Boolean values on one axis to specify the indices
you want to access.

9.  NumPy provides you with a convenient way to
index an array called fancy indexing.
 Fancy indexing allows you to index a numpy
array using the following:
 Another numpy array
 A Python list

10.  With the NumPy package, we can easily solve
many kinds of data processing tasks without
writing complex loops.
 NumPy contains a large number of various
mathematical operations arithmetic operations,
trigonometric functions, handling complex
numbers, etc.

11.  Arithmetic Operations:
1. numpy.add(ary1,ary2)
2. numpy.subtract(ary1,ary2)
3. numpy.multiply(ary1,ary2)
4. numpy.divide(ary1,ary2)
 Trigonometric Functions:
1. numpy.sin(ary*np.pi/180)
2. numpy.cos(ary*np.pi/180)
3. numpy.tan(ary*np.pi/180)

12.  The numpy.where() function returns the indices
of elements in an input array where the given
condition is satisfied.
numpy.where(condition[, x, y])
Here,
x – yield True value
y – yield False value

13.  Boolean array is a type of array (collection of values) that
can be used to represent logical ‘True’ or ‘False’ values
stored.
Declarations:
boolAry = np.array(objects,dtype=bool)
boolAry = condition using array
Logical Methods:
boolAry = np.logical_and(ary1,ary2)
boolAry = np.logical_or(ary1,ary2)
boolAry = np.logical_xor(ary1,ary2)
boolAry = np.logical_not(ary)

14.  numpy.sort() – Returns sorted copy of input array.
numpy.sort(ary, axis, kind, order)
 numpy.argsort() – Returns the indices that would sort
an array.
numpy.argsort(a, axis=-1, kind=None, order=None)
 numpy.lexsort() - Perform an indirect stable sort
using a sequence of keys.
numpy.lexsort(keys, axis=- 1)

15.  numpy.unique() - returns an array of unique
elements in the input array.
numpy.unique(arr, return_index, return_inverse,
return_counts)