**Teachers and Examiners** (CBSESkillEduction) collaborated to create the** Introduction to Packages Python Class 9 Notes**. All the important Information are taken from the **NCERT** Textbook **Artificial Intelligence (417)**.

**Contents**show

## Introduction to Packages Python Class 9 Notes

A package is simply a location where similar-type programmes, functions, or modules can be found. For diverse applications, there are a variety of free packages available (one of the benefits of Python being an open-source language).

The following are some of the readily available packages:

**NumPy**

A Python package for working with numerical arrays. It’s very useful for dealing with huge numerical databases and calculations.

**Open CV**

OpenCV is an image processing package that can deal explicitly with images and can be used for image manipulation and processing such as cropping, scaling, and editing, among other things.

**Matplotlib**

A software that aids in the visualisation of analytical data in graph form. It aids the user in better comprehending the data by allowing them to see it.

**NLTK**

Natural Language Tool Kit (NLTK) is a programme that aids in the processing of textual data. It is one of the most widely used Natural Lanague Processing packages.

**Pandas**

A Python library that aids in the processing of two-dimensional data tables. It comes in handy when working with data from Excel sheets or other databases.

**Package Installation**

Any package can be installed by directly writing the following command in the Anaconda

**Step 1 :** Open Anaconda Command Prompt

**Step 2 :** Type “conda install <package name>”

**Step 3 :** Press Enter

**Step 4 :** Proceed ([y]/n)?

**Step 5 :** Press “y”

**Working with a package**

To use a package, we must import it wherever it is needed in the script. In Python, there are several ways to import a differents version of package:

**import numpy**

Meaning: Import numpy into the file in order to use its features in the file where it was imported.

**import numpy as np**

Meaning: Import numpy and refer to it as np anywhere it’s used.

**from numpy import array**

Meaning: That is, only one functionality (array) from the entire numpy package is imported. While faster processing is achieved, the package’s usability is limited.

**from numpy import array as arr**

Meaning: Only one functionality (array) from the entire numpy package should be imported, and it should be referred to as arr whenever it is used.

**What is NumPy?**

NumPy, or Numerical Python, is the core Python library for performing mathematical and logical operations on arrays. When it comes to working with numbers, it is a widely utilised programme. NumPy provides a large range of arithmetic operations for working with numbers, making it easy to work with them. NumPy also works with arrays, which are just collections of data that are homogeneous.

An array is just a collection of many values of the same datatype. They can be numbers, characters, Booleans, and so on, but an array can only access one datatype at a time. The arrays used in NumPy are called ND-arrays (N-Dimensional Arrays) because NumPy has a function that allows you to create n-dimensional arrays in Python.

**An array can easily be compared to a list. Let us take a look how different they are: **

NumPy Arrays | List |
---|---|

Data collecting that is homogeneous. | Data collecting that is heterogeneous. |

Can only hold one sort of data, making datatypes inflexible. | Datatypes are flexible because they can hold multiple types of data. |

Cannot be initialised directly. It can only be used using the Numpy package. | Because it is part of the Python grammar, it can be immediately initialised. |

It is possible to perform direct numerical operations. For instance, dividing the entire array by three splits each element by three. | It is not possible to perform direct numerical operations. For instance, dividing the entire list by three will not split each entry by three. |

It is widely used in arithmetic. | It’s often used to handle data. |

Arrays consume less memory. | Lists are given extra memory. |

Illustration: To make a Numpy array named ‘A,’ follow these steps: A=numpy.array import numpy ([1,2,3,4,5,6,7,8,9,0]) | Illustration: A = [1,2,3,4,5,6,7,8,9,0] to make a list |

**Exploring NumPy!**

The NumPy package has a number of features and functions that assist us with arithmetic and logical operations.

**NumPy Arrays**

Arrays are a collection of datatypes that are all the same name and same datatype.

We can use NumPy to generate n-dimensional arrays (where n can be any integer) and use other mathematical functions on them.

Here are various methods for creating arrays with the NumPy package, assuming the NumPy package has previously been loaded.

Function | Code |
---|---|

Creating a Numpy Array | numpy.array([1,2,3,4,5]) |

Creating a 2-Dimensional zero array (4X3 – 4 rows and 3 columns) | numpy.zeros((4,3)) |

Creating an array with 5 random values | numpy.random.random(5) |

Creating a 2-Dimensional constant value array (3X4 – 3 rows and 4 columns) having all 6s | numpy.full((3,4),6) |

Creating a sequential array from 0 to 30 with gaps of 5 | numpy.arrange(0,30,5) |

**Let’s have a look at some of the operations that could be performed on this array:**

Function | Code |
---|---|

Adding 5 to each element | ARR + 5 |

Divide each element by 5 | ARR / 5 |

Squaring each element | ARR ** 5 |

Accessing 2nd element of the array (element count starts from 0) | ARR[1] |

Multiplying 2 arrays {consider BRR = numpy.array([6,7,8,9,0]) } | ARR * BRR |

As you can see, you can perform direct arithmetic operations on individual array members by manipulating the entire array variable.

Let us look at the functions which talk about the properties of an array:

Function | Code |
---|---|

Type of an array | type(ARR) |

Check the dimensions of an array | ARR.ndim |

Shape of an array | ARR.shape |

Size of an array | ARR.size |

Datatype of elements stored in the array | ARR.dtype |

**Some other mathematical functions available with NumPy are: **

Function | Code |
---|---|

Finding out maximum element of an array | ARR.max() |

Finding out row-wise maximum elements | ARR.max(axis = 1) |

Finding out column-wise minimum elements | ARR.min(axis = 0) |

Sum of all array elements | ARR.sum() |

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### Reference Textbook

The above **Introduction to Packages Python Class 9 Notes** was created using the NCERT Book and Study Material accessible on the CBSE ACADEMIC as a reference.

**Disclaimer** – 100% of the questions are taken from the CBSE textbook Introduction to Packages Python Class 9 Notes, our team has tried to collect all the correct Information from the textbook . If you found any suggestion or any error please contact us anuraganand2017@gmail.com.