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In this article, we learn about the Python NumPy Tutorial for Beginners. So you’ve learned the basics of Python and you’re looking for a more powerful way to analyse data? NumPy is what you need.NumPy is a module for Python that allows you to work with multidimensional arrays and matrices.

It’s perfect for scientific or mathematical calculations because it’s fast and efficient. In addition, NumPy includes support for signal processing and linear algebra operations. So if you need to do any mathematical operations on your data, NumPy is probably the library for you.

In this tutorial, we’ll show you how to use NumPy to its full potential. You’ll learn more about arrays as well as operate on them using mathematical functions.

NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed. In this Python Numpy Tutorial, we will be learning about NumPy in Python, What is NumPy in Python, Data Types in NumPy, and more.

NumPy in Python is a library that is used to work with arrays and was created in 2005 by Travis Oliphant. NumPy library in Python has functions for working in domain of Fourier transform, linear algebra, and matrices. Python NumPy is an open-source project that can be used freely. NumPy stands for Numerical Python.

Installing the NumPy library is a straightforward process. You can use pip to install the library.Go to the command line and type the following:

pip install numpy If you are using Anaconda distribution, then you can use conda to install NumPy. conda install numpy Once the installation is complete, you can verify it by importing the NumPy library in the python interpreter. One can use the numpy library by importing it as shown below. import numpy If the import is successful, then you will see the following output. >>> import numpy >>> numpy.__version__ '1.17.2'

NumPy is a library for the Python programming language, and it’s specifically designed to help you work with data.

With NumPy, you can easily create arrays, which is a data structure that allows you to store multiple values in a single variable.

In particular, NumPy arrays provide an efficient way of storing and manipulating data.NumPy also includes a number of functions that make it easy to perform mathematical operations on arrays. This can be really useful for scientific or engineering applications. And if you’re working with data from a Python script, using NumPy can make your life a lot easier.

Let us take a look at how to create NumPy arrays, copy and view arrays, reshape arrays, and iterate over arrays.

Arrays are different from Python lists in several ways. First, NumPy arrays are multi-dimensional, while Python lists are one-dimensional. Second, NumPy arrays are homogeneous, while Python lists are heterogeneous. This means that all the elements of a NumPy array must be of the same type. Third, NumPy arrays are more efficient than Python lists.NumPy arrays can be created in several ways. One way is to create an array from a Python list. Once you have created a NumPy array, you can manipulate it in various ways. For example, you can change the shape of an array, or you can index into an array to access its elements. You can also perform mathematical operations on NumPy arrays, such as addition, multiplication, and division.

One has to import the library in the program to use it. The module NumPy has an array function in it which creates an array.

**Creating an Array: **

```
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
```

```
Output:
[1 2 3 4 5]
```

We can also pass a tuple in the array function to create an array. 2

```
import numpy as np
arr = np.array((1, 2, 3, 4, 5))
print(arr)
```

The output would be similar to the above case.

**Dimensions- Arrays: **

**0-D Arrays: **

The following code will create a zero-dimensional array with a value 36.

```
import numpy as np
arr = np.array(36)
print(arr)
```

```
Output:
36
```

**1-Dimensional Array: **

The array that has Zero Dimensional arrays as its elements is a uni-dimensional or 1-D array.

The code below creates a 1-D array,

```
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
```

```
Output:
[1 2 3 4 5]
```

**Two Dimensional Arrays: **

2-D Arrays are the ones that have 1-D arrays as its element. The following code will create a 2-D array with 1,2,3 and 4,5,6 as its values.

```
import numpy as np
3
arr1 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr1)
```

```
Output:
[[1 2 3]
[4 5 6]]
```

**Three Dimensional Arrays: **

Let us see an example of creating a 3-D array with two 2-D arrays:

```
import numpy as np
arr1 = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) print(arr1)
```

```
Output:
[[[1 2 3]
[4 5 6]]
[[1 2 3]
[4 5 6]]]
```

To identify the dimensions of the array, we can use ndim as shown below:

```
import numpy as np
a = np.array(36)
d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(a.ndim)
print(d.ndim)
```

```
Output:
0
3
```

Using NumPy, a developer can perform the following operations −

- Mathematical and logical operations on arrays.
- Fourier transforms and routines for shape manipulation.
- Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

NumPy – A Replacement for MatLab

NumPy is often used along with packages like **SciPy** (Scientific Python) and **Matplotlib** (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.

It is open-source, which is an added advantage of NumPy.

The most important object defined in NumPy is an N-dimensional array type called **ndarray**. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.

Every item in a ndarray takes the same size as the block in the memory. Each element in ndarray is an object of the data-type object (called **dtype**).

Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types. The following diagram shows a relationship between ndarray, data-type object (dtype) and array scalar type −

An instance of ndarray class can be constructed by different array creation routines described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows-

**numpy.array **

It creates a ndarray from any object exposing an array interface, or from any method that returns an array.

**numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)**

The **ndarray** object consists of a contiguous one-dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block. The memory block holds the elements in row-major order (C style) or a column-major order (FORTRAN or MatLab style).

The above constructor takes the following parameters −

Sr.No. | Parameter & Description |

1 | object Any object exposing the array interface method returns an array or any (nested) sequence. |

2 3 | dtype The desired data type of array, optionalcopyOptional. By default (true), the object is copied |

4 | orderC (row-major) or F (column-major) or A (any) (default) |

5 | subok By default, returned array forced to be a base class array. If true, sub-classes passed through |

6 | ndmin Specifies minimum dimensions of the resultant array |

Take a look at the following examples to understand better.

```
import numpy as np
a = np.array([1,2,3])
print a
```

The output is as follows –

[1, 2, 3]

```
# more than one dimensions
import numpy as np
a = np.array([[1, 2], [3, 4]])
print a
```

The output is as follows −

[[1, 2]

[3, 4]]

```
# minimum dimensions
import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print a
```

The output is as follows −

[[1, 2, 3, 4, 5]]

```
# dtype parameter
import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print a
```

The output is as follows −

[ 1.+0.j, 2.+0.j, 3.+0.j]

The **ndarray** object consists of a contiguous one-dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block. The memory block holds the elements in row-major order (C style) or a column-major order (FORTRAN or MatLab style).

Here is a list of the different Data Types in NumPy:

- bool_
- int_
- intc
- intp
- int8
- int16
- float_
- float64
- complex_
- complex64
- complex128

**bool_**

Boolean (True or False) stored as a byte

**int_**

Default integer type (same as C long; normally either int64 or int32)

**intc**

Identical to C int (normally int32 or int64)

**intp**

An integer used for indexing (same as C ssize_t; normally either int32 or int64)

**int8**

Byte (-128 to 127)

**int16**

Integer (-32768 to 32767)

**float_**

Shorthand for float64

**float64**

Double precision float: sign bit, 11 bits exponent, 52 bits mantissa

**complex_**

Shorthand for complex128

**complex64**

Complex number, represented by two 32-bit floats (real and imaginary components)

**complex128**

Complex number, represented by two 64-bit floats (real and imaginary components)

NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.

A data type object describes the interpretation of a fixed block of memory corresponding to an array, depending on the following aspects −

- Type of data (integer, float or Python object)
- Size of data
- Byte order (little-endian or big-endian)
- In case of structured type, the names of fields, data type of each field and part of the memory block taken by each field.
- If the data type is a subarray, its shape and data type

The byte order is decided by prefixing ‘<‘ or ‘>’ to the data type. ‘<‘ means that encoding is little-endian (least significant is stored in smallest address). ‘>’ means that encoding is big-endian (a most significant byte is stored in smallest address).

A dtype object is constructed using the following syntax −

numpy.dtype(object, align, copy)

The parameters are −

**Object**− To be converted to data type object**Align**− If true, adds padding to the field to make it similar to C-struct**Copy**− Makes a new copy of dtype object. If false, the result is a reference to builtin data type object

```
# using array-scalar type
import numpy as np
dt = np.dtype(np.int32)
print dt
```

The output is as follows −

int32

```
#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc.
import numpy as np
dt = np.dtype('i4')
print dt
```

The output is as follows −

int32

```
# using endian notation
import numpy as np
dt = np.dtype('>i4')
print dt
```

The output is as follows −

>i4

The following examples show the use of a structured data type. Here, the field name and the corresponding scalar data type is to be declared.

```
# first create structured data type
import numpy as np
dt = np.dtype([('age',np.int8)])
print dt
```

The output is as follows – [(‘age’, ‘i1’)]

```
# now apply it to ndarray object
import numpy as np
dt = np.dtype([('age',np.int8)])
a = np.array([(10,),(20,),(30,)], dtype = dt)
print a
```

The output is as follows –

[(10,) (20,) (30,)]

Each built-in data type has a character code that uniquely identifies it.

**‘b’**− boolean**‘i’**− (signed) integer**‘u’**− unsigned integer**‘f’**− floating-point**‘c’**− complex-floating point**‘m’**− timedelta**‘M’**− datetime**‘O’**− (Python) objects**‘S’, ‘a’**− (byte-)string**‘U’**− Unicode**‘V’**− raw data (void)

We will also discuss the various array attributes of NumPy.

This array attribute returns a tuple consisting of array dimensions. It can also be used to resize the array.

```
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
print a.shape
```

The output is as follows − (2, 3)

```
# this resizes the ndarray
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
a.shape = (3,2)
print a
```

The output is as follows -[[1, 2][3, 4] [5, 6]]

This array attribute returns the number of array dimensions.

```
# an array of evenly spaced numbers
import numpy as np
a = np.arange(24)
print a
```

The output is as follows −

[0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]

```
# this is one dimensional array
import numpy as np
a = np.arange(24)
a.ndim
# now reshape it
b = a.reshape(2,4,3)
print b
# b is having three dimensions
```

The output is as follows −

[[[ 0, 1, 2]

[ 3, 4, 5]

[ 6, 7, 8]

[ 9, 10, 11]]

[[12, 13, 14]

[15, 16, 17]

[18, 19, 20]

[21, 22, 23]]]

This array attribute returns the length of each element of array in bytes.

```
# dtype of array is int8 (1 byte)
import numpy as np
x = np.array([1,2,3,4,5], dtype = np.int8)
print x.itemsize
```

The output is as follows −

1

```
# dtype of array is now float32 (4 bytes)
import numpy as np
x = np.array([1,2,3,4,5], dtype = np.float32)
print x.itemsize
```

The output is as follows −

4

The ndarray object has the following attributes. Its current values are returned by this function.

Sr.No. | Attribute & Description |

1 | C_CONTIGUOUS (C)The data is in a single, C-style contiguous segment |

2 | F_CONTIGUOUS (F)The data is in a single, Fortran-style contiguous segment |

3 | OWNDATA (O)The array owns the memory it uses or borrows it from another object |

4 | WRITEABLE (W)The data area can be written to. Setting this to False locks the data, making it read-only |

5 | ALIGNED (A)The data and all elements are aligned appropriately for the hardware |

6 | UPDATEIFCOPY (U)This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array |

The following example shows the current values of flags.

```
import numpy as np
x = np.array([1,2,3,4,5])
print x.flags
```

The output is as follows −

C_CONTIGUOUS : True

F_CONTIGUOUS : True

OWNDATA : True

WRITEABLE : True

ALIGNED : True

UPDATEIFCOPY : False

A new **ndarray** object can be constructed by any of the following array creation routines or using a low-level ndarray constructor.

numpy.empty

It creates an uninitialized array of specified shape and dtype. It uses the following constructor −

numpy.empty(shape, dtype = float, order = ‘C’)

The constructor takes the following parameters.

Sr.No. | Parameter & Description |

1 | ShapeShape of an empty array in int or tuple of int |

2 | DtypeDesired output data type. Optional |

3 | Order‘C’ for C-style row-major array, ‘F’ for FORTRAN style column- |

The following code shows an example of an empty array.

```
import numpy as np
x = np.empty([3,2], dtype = int)
print x
```

The output is as follows −[[22649312 1701344351]

[1818321759 1885959276] [16779776 156368896]]

Returns a new array of specified size, filled with zeros.

numpy.zeros(shape, dtype = float, order = ‘C’)

The constructor takes the following parameters.

Sr.No. | Parameter & Description |

1 | ShapeShape of an empty array in int or sequence of int |

2 | DtypeDesired output data type. Optional |

3 | Order‘C’ for C-style row-major array, ‘F’ for FORTRAN style column-major array |

```
# array of five ones. Default dtype is float
import numpy as np
x = np.ones(5)
print x
```

The output is as follows −

[ 1. 1. 1. 1. 1.]

Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python’s in-built container objects.

As mentioned earlier, items in ndarray object follows zero-based index. Three types of indexing methods are available − **field access, basic slicing** and **advanced indexing**.

Basic slicing is an extension of Python’s basic concept of slicing to n dimensions. A Python slice object is constructed by giving **start, stop**, and **step** parameters to the built-in **slice** function. This slice object is passed to the array to extract a part of array.

```
import numpy as np
a = np.arange(10)
s = slice(2,7,2)
print a[s]
```

Its output is as follows −

[2 4 6]

n the above example, an **ndarray** object is prepared by **arange()** function. Then a slice object is defined with start, stop, and step values 2, 7, and 2 respectively. When this slice object is passed to the ndarray, a part of it starting with index 2 up to 7 with a step of 2 is sliced.

The same result can also be obtained by giving the slicing parameters separated by a colon : (start:stop:step) directly to the **ndarray** object.

```
import numpy as np
a = np.arange(10)
b = a[2:7:2]
print b
```

Here, we will get the same output − [2 4 6]

If only one parameter is put, a single item corresponding to the index will be returned. If a: is inserted in front of it, all items from that index onwards will be extracted. If two parameters (with: between them) is used, items between the two indexes (not including the stop index) with default step one are sliced.

```
# slice single item
import numpy as np
a = np.arange(10)
b = a[5]
print b
```

Its output is as follows −

5

```
# slice items starting from index
import NumPy as np
a = np.arange(10)
print a[2:]
```

Now, the output would be −

[2 3 4 5 6 7 8 9]

```
# slice items between indexes
import numpy as np
a = np.arange(10)
print a[2:5]
```

Here, the output would be −

[2 3 4]

The above description applies to multi-dimensional **ndarray** too.

It is possible to make a selection from ndarray that is a non-tuple sequence, ndarray object of integer or Boolean data type, or a tuple with at least one item being a sequence object. Advanced indexing always returns a copy of the data. As against this, the slicing only presents a view.

There are two types of advanced indexing − **Integer** and **Boolean**.

This mechanism helps in selecting any arbitrary item in an array based on its N-dimensional index. Each integer array represents the number of indexes into that dimension. When the index consists of as many integer arrays as the dimensions of the target ndarray, it becomes straightforward.

In the following example, one element of the specified column from each row of ndarray object is selected. Hence, the row index contains all row numbers, and the column index specifies the element to be selected.

```
import numpy as np
x = np.array([[1, 2], [3, 4], [5, 6]])
y = x[[0,1,2], [0,1,0]]
print y
```

Its output would be as follows −

[1 4 5]

The selection includes elements at (0,0), (1,1) and (2,0) from the first array.

In the following example, elements placed at corners of a 4X3 array are selected. The row indices of selection are [0, 0] and [3,3] whereas the column indices are [0,2] and [0,2].

Advanced and basic indexing can be combined by using one slice (:) or ellipsis (…) with an index array. The following example uses a slice for the advanced index for column. The result is the same when a slice is used for both. But advanced index results in copy and may have different memory layout.

This type of advanced indexing is used when the resultant object is meant to be the result of Boolean operations, such as comparison operators.

In this example, items greater than 5 are returned as a result of Boolean indexing.

```
import numpy as np
x = np.array([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],[ 9, 10, 11]])
print 'Our array is:'
print x
print '\n'
# Now we will print the items greater than 5
print 'The items greater than 5 are:'
print x[x > 5]
```

The output of this program would be −

Our array is:

[[ 0 1 2]

[ 3 4 5]

[ 6 7 8]

[ 9 10 11]]

The items greater than 5 are:

[ 6 7 8 9 10 11]

The term **broadcasting** refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations. Arithmetic operations on arrays are usually done on corresponding elements. If two arrays are of exactly the same shape, then these operations are smoothly performed.

```
import numpy as np
a = np.array([1,2,3,4])
b = np.array([10,20,30,40])
c = a * b
print c
```

Its output is as follows −[10 40 90 160]

If the dimensions of the two arrays are dissimilar, element-to-element operations are not possible. However, operations on arrays of non-similar shapes is still possible in NumPy, because of the broadcasting capability. The smaller array is **broadcast** to the size of the larger array so that they have compatible shapes.

Broadcasting is possible if the following rules are satisfied −

- Array with smaller
**ndim**than the other is prepended with ‘1’ in its shape. - Size in each dimension of the output shape is maximum of the input sizes in that dimension.
- An input can be used in calculation if its size in a particular dimension matches the output size or its value is exactly 1.
- If an input has a dimension size of 1, the first data entry in that dimension is used for all calculations along that dimension.

A set of arrays is said to be **broadcastable** if the above rules produce a valid result and one of the following is true −

- Arrays have exactly the same shape.
- Arrays have the same number of dimensions and the length of each dimension is either a common length or 1.
- Array having too few dimensions can have its shape prepended with a dimension of length 1, so that the above stated property is true.

The following figure demonstrates how array **b** is broadcast to become compatible with **a**.

NumPy package contains an iterator object **numpy.nditer**. It is an efficient multidimensional iterator object using which it is possible to iterate over an array. Each element of an array is visited using Python’s standard Iterator interface.

Let us create a 3X4 array using arrange() function and iterate over it using **nditer**.

Several routines are available in NumPy package for manipulation of elements in ndarray object. They can be classified into the following types −

Changing Shape

Sr.No. | Shape & Description |

1 | reshape: Gives a new shape to an array without changing its data |

2 | flatA 1-D iterator over the array |

3 | flatten: Returns a copy of the array collapsed into one dimension |

4 | ravel: Returns a contiguous flattened array |

Transpose Operations

Sr.No. | Operation & Description |

1 | transpose: Permutes the dimensions of an array |

2 | ndarray.T Same as self.transpose() |

3 | rollaxis: Rolls the specified axis backwards |

4 | swapaxes: Interchanges the two axes of an array |

Changing Dimensions

Sr.No. | Dimension & Description |

1 | broadcast: Produces an object that mimics broadcasting |

2 | broadcast_to: Broadcasts an array to a new shape |

3 | expand_dims: Expands the shape of an array |

4 | squeeze: Removes single-dimensional entries from the shape of an array |

Joining Arrays

Sr.No. | Array & Description |

1 | concatenate: Joins a sequence of arrays along an existing axis |

2 | stack: Joins a sequence of arrays along a new axis |

3 | hstack: Stacks arrays in sequence horizontally (column wise) |

4 | vstack: Stacks arrays in sequence vertically (row wise) |

Splitting Arrays

Sr.No. | Array & Description |

1 | split: Splits an array into multiple sub-arrays |

2 | hsplit: Splits an array into multiple sub-arrays horizontally (column-wise) |

3 | vsplit: Splits an array into multiple sub-arrays vertically (row-wise) |

Adding / Removing Elements

Sr.No. | Element & Description |

1 | resize: Returns a new array with the specified shape |

2 | append: Appends the values to the end of an array |

3 | insert: Inserts the values along the given axis before the given indices |

4 | delete: Returns a new array with sub-arrays along an axis deleted |

5 | unique: Finds the unique elements of an array |

NumPy – Binary Operators

Following are the functions for bitwise operations available in NumPy package.

Sr.No. | Operation & Description |

1 | bitwise_and: Computes bitwise AND operation of array elements |

2 | bitwise_or: Computes bitwise OR operation of array elements |

3 | invert: Computes bitwise NOT |

4 | right_shift: Shifts bits of binary representation to the right |

Quite understandably, NumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc.

NumPy has standard trigonometric functions which return trigonometric ratios for a given angle in radians.

**Example**

```
import numpy as np
a = np.array([0,30,45,60,90])
print 'Sine of different angles:'
# Convert to radians by multiplying with pi/180
print np.sin(a*np.pi/180)
print '\n'
print 'Cosine values for angles in array:'
print np.cos(a*np.pi/180)
print '\n'
print 'Tangent values for given angles:'
print np.tan(a*np.pi/180)
```

Here is its output −

Sine of different angles:

[ 0. 0.5 0.70710678 0.8660254 1. ]

Cosine values for angles in array:

[ 1.00000000e+00 8.66025404e-01 7.07106781e-01 5.00000000e-01

6.12323400e-17]

Tangent values for given angles:

[ 0.00000000e+00 5.77350269e-01 1.00000000e+00 1.73205081e+00

1.63312394e+16]

**arcsin, arcos,** and **arctan** functions return the trigonometric inverse of sin, cos, and tan of the given angle. The result of these functions can be verified by **numpy.degrees() function** by converting radians to degrees.

numpy.around()

This is a function that returns the value rounded to the desired precision. The function takes the following parameters.

numpy.around(a,decimals)

Where,

Sr.No. | Parameter & Description |

1 | aInput data |

2 | decimalsThe number of decimals to round to. Default is 0. If negative, the integer is rounded to position to the left of the decimal point |

NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc. from the given elements in the array. The functions are explained as follows −

numpy.amin() and numpy.amax()numpy.amin() and numpy.amax()

These functions return the minimum and the maximum from the elements in the given array along the specified axis.

```
import numpy as np
a = np.array([[3,7,5],[8,4,3],[2,4,9]])
print 'Our array is:'
print a
print '\n'
print 'Applying amin() function:'
print np.amin(a,1)
print '\n'
print 'Applying amin() function again:'
print np.amin(a,0)
print '\n'
print 'Applying amax() function:'
print np.amax(a)
print '\n’
print 'Applying amax() function again:'
print np.amax(a, axis = 0)
```

It will produce the following output −

Our array is:

[[3 7 5]

[8 4 3]

[2 4 9]]

Applying amin() function:

[3 3 2]

Applying amin() function again:

[2 4 3]

Applying amax() function:

9

Applying amax() function again:

[8 7 9]

numpy.ptp()

The **numpy.ptp()** function returns the range (maximum-minimum) of values along an axis.

```
import numpy as np
a = np.array([[3,7,5],[8,4,3],[2,4,9]])
print 'Our array is:'
print a
print '\n'
print 'Applying ptp() function:'
print np.ptp(a)
print '\n'
print 'Applying ptp() function along axis 1:'
print np.ptp(a, axis = 1)
print '\n'
print 'Applying ptp() function along axis 0:'
print np.ptp(a, axis = 0)
numpy.percentile()
```

Percentile (or a centile) is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall. The function **numpy.percentile()** takes the following arguments.

Where,

Sr.No. | Argument & Description |

1 | a: Input array |

2 | q: The percentile to compute must be between 0-100 |

3 | axis: The axis along which the percentile is to be calculated |

A variety of sorting related functions are available in NumPy. These sorting functions implement different sorting algorithms, each of them characterized by the speed of execution, worst-case performance, the workspace required and the stability of algorithms. Following table shows the comparison of three sorting algorithms.

kind | speed | worst case | work space | stable |

‘quicksort’ | 1 | O(n^2) | 0 | no |

‘mergesort’ | 2 | O(n*log(n)) | ~n/2 | yes |

‘heapsort’ | 3 | O(n*log(n)) | 0 | no |

numpy.sort()

The sort() function returns a sorted copy of the input array. It has the following parameters −

numpy.sort(a, axis, kind, order)

Where,

Sr.No. | Parameter & Description |

1 | aArray to be sorted |

2 | axisThe axis along which the array is to be sorted. If none, the array is flattened, sorting on the last axis |

3 | kindDefault is quicksort |

4 | orderIf the array contains fields, the order of fields to be sorted |

We have seen that the data stored in the memory of a computer depends on which architecture the CPU uses. It may be little-endian (least significant is stored in the smallest address) or big-endian (most significant byte in the smallest address).

numpy.ndarray.byteswap()

The **numpy.ndarray.byteswap()** function toggles between the two representations: bigendian and little-endian.

While executing the functions, some of them return a copy of the input array, while some return the view. When the contents are physically stored in another location, it is called **Copy**. If on the other hand, a different view of the same memory content is provided, we call it as **View**.

Simple assignments do not make the copy of array object. Instead, it uses the same id() of the original array to access it. The **id()** returns a universal identifier of Python object, similar to the pointer in C.

Furthermore, any changes in either gets reflected in the other. For example, the changing shape of one will change the shape of the other too.

NumPy has **ndarray.view()** method which is a new array object that looks at the same data of the original array. Unlike the earlier case, change in dimensions of the new array doesn’t change dimensions of the original.

NumPy – Matrix Library

NumPy package contains a Matrix library **numpy.matlib**. This module has functions that return matrices instead of ndarray objects.

matlib.empty()

The **matlib.empty()** function returns a new matrix without initializing the entries. The function takes the following parameters.

numpy.matlib.empty(shape, dtype, order)

Where,

Sr.No. | Parameter & Description |

1 | shapeint or tuple of int defining the shape of the new matrix |

2 | DtypeOptional. Data type of the output |

3 | orderC or F |

```
import numpy.matlib
import numpy as np
print np.matlib.empty((2,2))
# filled with random data
```

It will produce the following output −

[[ 2.12199579e-314, 4.24399158e-314]

[ 4.24399158e-314, 2.12199579e-314]]

This function returns a matrix with 1 along the diagonal elements and the zeros elsewhere. The function takes the following parameters.

numpy.matlib.eye(n, M,k, dtype)

Where,

Sr.No. | Parameter & Description |

1 | nThe number of rows in the resulting matrix |

2 | MThe number of columns, defaults to n |

3 | kIndex of diagonal |

4 | dtypeData type of the output |

```
import numpy.matlib
import numpy as np
print np.matlib.eye(n = 3, M = 4, k = 0, dtype = float)
```

It will produce the following output −

[[ 1. 0. 0. 0.]

[ 0. 1. 0. 0.]

[ 0. 0. 1. 0.]]

Matplotlib is a plotting library for Python. It is used along with NumPy to provide an environment that is an effective open-source alternative for MatLab. It can also be used with graphics toolkits like PyQt and wxPython.

Matplotlib module was first written by John D. Hunter. Since 2012, Michael Droettboom is the principal developer. Currently, Matplotlib ver. 1.5.1 is the stable version available. The package is available in binary distribution as well as in the source code form on www.matplotlib.org.

Conventionally, the package is imported into the Python script by adding the following statement −

from matplotlib import pyplot as plt

Here **pyplot()** is the most important function in matplotlib library, which is used to plot 2D data. The following script plots the equation **y = 2x + 5**

**Example:**

```
import numpy as np
from matplotlib import pyplot as plt
x = np.arange(1,11)
y = 2 * x + 5
plt.title("Matplotlib demo")
plt.xlabel("x axis caption")
plt.ylabel("y axis caption")
plt.plot(x,y)
plt.show()
```

An ndarray object x is created from **np.arange() function** as the values on the **x axis**. The corresponding values on the **y axis** are stored in another **ndarray object y**. These values are plotted using **plot()** function of pyplot submodule of matplotlib package.

The graphical representation is displayed by **show()** function.

The above code should produce the following output −

Instead of the linear graph, the values can be displayed discretely by adding a format string to the **plot()** function. Following formatting characters can be used.

NumPy has a **numpy.histogram()** function that is a graphical representation of the frequency distribution of data. Rectangles of equal horizontal size corresponding to class interval called **bin** and **variable height** corresponding to frequency.

numpy.histogram()

The numpy.histogram() function takes the input array and bins as two parameters. The successive elements in bin array act as the boundary of each bin.

```
import numpy as np
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])
np.histogram(a,bins = [0,20,40,60,80,100])
hist,bins = np.histogram(a,bins = [0,20,40,60,80,100])
print hist
print bins
```

It will produce the following output −

[3 4 5 2 1]

[0 20 40 60 80 100]

plt()

Matplotlib can convert this numeric representation of histogram into a graph. The **plt() function** of pyplot submodule takes the array containing the data and bin array as parameters and converts into a histogram.

```
from matplotlib import pyplot as plt
import numpy as np
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])
plt.hist(a, bins = [0,20,40,60,80,100])
plt.title("histogram")
plt.show()
```

It should produce the following output –

I/O with NumPy

The ndarray objects can be saved to and loaded from the disk files. The IO functions available are −

**load()**and**save()**functions handle /numPy binary files (with**npy**extension)**loadtxt()**and**savetxt()**functions handle normal text files

NumPy introduces a simple file format for ndarray objects. This **.npy** file stores data, shape, dtype and other information required to reconstruct the ndarray in a disk file such that the array is correctly retrieved even if the file is on another machine with different architecture.

The **numpy.save()** file stores the input array in a disk file with **npy** extension.

```
import numpy as np
a = np.array([1,2,3,4,5])
np.save('outfile',a)
```

To reconstruct array from **outfile.npy**, use **load()** function.

```
import numpy as np
b = np.load('outfile.npy')
print b
```

It will produce the following output −

array([1, 2, 3, 4, 5])

The save() and load() functions accept an additional Boolean parameter **allow_pickles**. A pickle in Python is used to serialize and de-serialize objects before saving to or reading from a disk file.

savetxt()

The storage and retrieval of array data in simple text file format is done with **savetxt()** and **loadtxt()** functions.

```
import numpy as np
a = np.array([1,2,3,4,5])
np.savetxt('out.txt',a)
b = np.loadtxt('out.txt')
print b
```

It will produce the following output −

[ 1. 2. 3. 4. 5.]

We’d also recommend you to visit Great Learning Academy, where you will find a free NumPy course and 1000+ other courses. You will also receive a certificate after the completion of these courses. We hope that this Python NumPy Tutorial was beneficial and you are now better equipped.

**NumPy Copy vs View **

The difference between copy and view of an array in NumPy is that the view is merely a view of the original array whereas copy is a new array. The copy will not affect the original array and the chances are restricted to the new array created and many modifications made to the original array will not be reflected in the copy array too. But in view, the changes made to the view will be reflected in the original array and vice versa.

Let us understand with code snippets:

**Example of Copy:**

```
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
y = arr1.copy()
arr1[0] = 36
print(arr1)
print(y)
```

```
Output :
[42 2 3 4 5]
[1 2 3 4 5]
```

**Example of view: **

Notice the output of the below code; the changes made to the original array are also reflected in the view.

```
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
y= arr1.view()
arr1[0] = 36
print(arr1)
print(y)
```

```
Output:
[36 2 3 4 5]
[36 2 3 4 5]
```

**NumPy Array Shape **

The shape of an array is nothing but the number of elements in each dimension. To get the shape of an array, we can use a .shape attribute that returns a tuple indicating the number of elements.

```
import numpy as np
array1 = np.array([[2, 3, 4,5], [ 6, 7, 8,9]])
print(array1.shape)
```

`Output: (2,4) `

**NumPy Array Reshape **

**1-D to 2-D: **

Array reshape is nothing but changing the shape of the array, through which one can add or remove a number of elements in each dimension. The following code will convert a 1-D array into 2-D array. The resulting will have 3 arrays having 4 elements

```
import numpy as np
array_1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr1 = array_1.reshape(3, 4)
print(newarr1)
```

```
Output:
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
```

**1-D to 3-D: **

The outer dimension will contain two arrays that have three arrays with two elements each.

```
import numpy as np
array_1= np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr1 = array_1.reshape(2, 3, 2)
print(newarr1)
```

```
Output:
[[[ 1 2]
[ 3 4]
[ 5 6]]
[[ 7 8]
[ 9 10]
[11 12]]]
```

**Flattening arrays: **

Converting higher dimensions arrays into one-dimensional arrays is called flattening of arrays.

```
import numpy as np
arr1= np.array([[4,5,6], [7, 8, 9]])
newarr1 = arr1.reshape(-1)
print(newarr1)
```

```
Output :
[1 2 3 4 5 6]
```

**NumPy Array Iterating **

Iteration through the arrays is possible using for loop.

Example 1:

```
import numpy as np
arr1 = np.array([1, 2, 3])
for i in arr1:
print(i)
```

```
Output: 1
2
3
```

Example 2:

```
import numpy as np
arr = np.array([[4, 5, 6], [1, 2, 3]])
for x in arr:
print(x)
Output: [4, 5, 6]
[1, 2, 3]
```

Example3:

```
import numpy as np
array1 = np.array([[1, 2, 3], [4, 5, 6]])
for x in array1:
for y in x:
print(y)
```

Joining is an operation of combining one or two arrays into a single array. In Numpy, the arrays are joined by axes. The concatenate() function is used for this operation, it takes a sequence of arrays that are to be joined, and if the axis is not specified, it will be taken as 0.

```
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
finalarr = np.concatenate((arr1, arr2))
print(finalarr)
```

```
Output: [1 2 3 4 5 6]
```

The following code joins the specified arrays along the rows

```
import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
finalarr = np.concatenate((arr1, arr2), axis=1)
print(finalarr)
Output:
[[1 2 5 6]
[3 4 7 8]]
```

As we know, split does the opposite of join operation. Split breaks a single array as specified. The function array_split() is used for this operation and one has to pass the number of splits along with the array.

```
import numpy as np
arr1 = np.array([7, 8, 3, 4, 1, 2])
finalarr = np.array_split(arr1, 3)
print(finalarr)
Output: [array([7, 8]), array([3, 4]), array([1, 2])]
```

Look at an exceptional case where the no of elements is less than required and observe the output

Example :

```
import numpy as np
array_1 = np.array([4, 5, 6,1,2,3])
finalarr = np.array_split(array_1, 4)
print(finalarr)
Output : [array([4, 5]), array([6, 1]), array([2]), array([3])]
```

The array_split() will return an array containing an array as a split, we can access the elements just as we do in a normal array.

```
import numpy as np
array1 = np.array([4, 5, 6,7,8,9])
finalarr = np.array_split(array1, 3)
print(finalarr[0])
print(finalarr[1])
Output :
[4 5]
[6 7]
```

Splitting of 2-D arrays is also similar, send the 2-d array in the array_split()

```
import numpy as np
arr1 = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
finalarr = np.array_split(arr1, 3)
print(finalarr)
Output:
[array([[1, 2],
[3, 4]]), array([[5, 6],
[7, 8]]), array([[ 9, 10],
[11, 12]])]
```

**NumPy Array Search **

The where() method is used to search an array. It returns the index of the value specified in the where method.

The below code will return a tuple indicating that element 4 is at 3,5 and 6

```
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5, 4, 4])
y = np.where(arr1 == 4)
print(y)
Output : (array([3, 5, 6]),)
```

Numpy- Also known as numerical Python, is a library used for working with arrays. It is also a general-purpose array-processing package that provides comprehensive mathematical functions, linear algebra routines, Fourier transforms, and more.

NumPy aims to provide less memory to store the data compared to python list and also helps in creating n-dimensional arrays. This is the reason why NumPy is used in Python.

NumPy in python is defined as a fundamental package for scientific computing that helps in facilitating advanced mathematical and other types of operations on large numbers of data.

NumPy is a python library mainly used for working with arrays and to perform a wide variety of mathematical operations on arrays.NumPy guarantees efficient calculations with arrays and matrices on high-level mathematical functions that operate on these arrays and matrices.

Go through the below points and decide whether to use NumPy or Pandas, here we go:

- NumPy and Pandas are the most used libraries in Data Science, ML and AI.
- NumPy and Pandas are used to save n number of lines of Codes.
- NumPy and Pandas are open source libraries.
- NumPy is used for fast scientific computing and Pandas is used for data manipulation, analysis and cleaning.

NumPy | Pandas |

Numpy creates an n-dimensional array object. | Pandas create DataFrame and Series. |

Numpy array contains data of same data types | Pandas is well suited for tabular data |

Numpy requires less memory | Pandas required more memory compared to NumPy |

NumPy supports multidimensional arrays. | Pandas support 2 dimensional arrays |

Numpy array is formed by all the computations performed by the NumPy library. This is a powerful N-dimensional array object with a central data structure and is a collection of elements that have the same data types.

NumPy is a Python library that is partially written in Python and most of the parts are written in C or C++. And it also supports extensions in other languages, commonly C++ and Fortran.

NumPy is an open-source Python library that is mainly used for data manipulation and processing in the form of arrays.NumPy is easy to learn as it works fast, works well with other libraries, has lots of built-in functions, and lets you do matrix operations.

NumPy is a fundamental Python library that gives you access to powerful mathematical functions. If you’re looking to dive deep into scientific computing and data analysis, then NumPy is definitely the way to go.

On the other hand, pandas is a data analysis library that makes it easy to work with tabular data. If your focus is on business intelligence and data wrangling, then pandas are the library for you.

In the end, it’s up to you which one you want to learn first. Just be sure to focus on one at a time, and you’ll be mastering NumPy in no time!

Original article source at: https://www.mygreatlearning.com

1624291780

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!

⭐️ Contents ⭐

⌨️ (0:00) Introduction

⌨️ (1:45) Installing Python & PyCharm

⌨️ (6:40) Setup & Hello World

⌨️ (10:23) Drawing a Shape

⌨️ (15:06) Variables & Data Types

⌨️ (27:03) Working With Strings

⌨️ (38:18) Working With Numbers

⌨️ (48:26) Getting Input From Users

⌨️ (52:37) Building a Basic Calculator

⌨️ (58:27) Mad Libs Game

⌨️ (1:03:10) Lists

⌨️ (1:10:44) List Functions

⌨️ (1:18:57) Tuples

⌨️ (1:24:15) Functions

⌨️ (1:34:11) Return Statement

⌨️ (1:40:06) If Statements

⌨️ (1:54:07) If Statements & Comparisons

⌨️ (2:00:37) Building a better Calculator

⌨️ (2:07:17) Dictionaries

⌨️ (2:14:13) While Loop

⌨️ (2:20:21) Building a Guessing Game

⌨️ (2:32:44) For Loops

⌨️ (2:41:20) Exponent Function

⌨️ (2:47:13) 2D Lists & Nested Loops

⌨️ (2:52:41) Building a Translator

⌨️ (3:00:18) Comments

⌨️ (3:04:17) Try / Except

⌨️ (3:12:41) Reading Files

⌨️ (3:21:26) Writing to Files

⌨️ (3:28:13) Modules & Pip

⌨️ (3:43:56) Classes & Objects

⌨️ (3:57:37) Building a Multiple Choice Quiz

⌨️ (4:08:28) Object Functions

⌨️ (4:12:37) Inheritance

⌨️ (4:20:43) Python Interpreter

📺 The video in this post was made by freeCodeCamp.org

The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

**Lambda function in python**: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

**Syntax: x = lambda arguments : expression**

Now i will show you some python lambda function examples:

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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

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Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more *readable* and *efficient* way like a pro.

**Swapping value in Python**

Instead of creating a **temporary** variable to hold the value of the one while **swapping**, you can do this instead

```
>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
```

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Description

Hello and welcome to brand new series of wiredwiki. In this series i will teach you guys all you need to know about python. This series is designed for beginners but that doesn’t means that i will not talk about the advanced stuff as well.

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First time Python programmers

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Basic knowledge

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know basic programming concept and skill

build 6 text-based application using python

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