EX.NO:1 INSTALLATION OF PACKAGES
NUMPY:
Numpy is a general-purpose array-processing package. It provides a high-performance
multidimensional array object, and tools for working with these arrays. It is the fundamental
package for scientific computing with Python. Besides its obvious scientific uses, Numpy can
also be used as an efficient multi-dimensional container of generic data.
Features:
High-performance N-dimensional array object.
It contains tools for integrating code from C/C++ and FORTRAN.
It contains a multidimensional container for generic data.
Additional linear algebra, Fourier transforms, and random number capabilities.
It consists of broadcasting functions.
It had data type definition capability to work with varied databases.
Sample Program:
import numpy as np
a=np.array([1,2,3])
print(a)
Output:
[1 2 3]
SCIPY:
SciPy is a python library that is useful in solving many mathematical equations and
algorithms. It is designed on the top of Numpy library that gives more extension of finding
scientific mathematical formulae like Matrix Rank, Inverse, polynomial equations, LU
Decomposition, etc. Using its high level functions will significantly reduce the complexity of the
code and helps in better analyzing the data. SciPy is an interactive Python session used as a data-
,processing library that is made to compete with its rivalries such as MATLAB, Octave, R-
Lab,etc. It has many user-friendly, efficient and easy-to-use functions that helps to solve
problems like numerical integration, interpolation, optimization, linear algebra and statistics.
Sample Program:
from scipy import constants
print(constants.pi)
Output:
3.141592653589793
JUPYTER:
The IPython Notebook concept was expanded upon to allow for additional programming
languages and was therefore renamed "Jupyter". "Jupyter" is a loose acronym meaning Julia,
Python and R, but today, the notebook technology supports many programming languages. An
IDE normally consists of at least a source code editor, build automation tools and a
debugger. Jupyter Notebook is an IDE for Python that allows its users to create documents
containing both rich text and code. It also supports the programming languages Julia, and R.
Jupyter Notebook allows users to compile all aspects of a data project in one place
making it easier to show the entire process of a project to your intended audience. Through the
web-based application, users can create data visualizations and other components of a project to
share with others via the platform.
,
,
NUMPY:
Numpy is a general-purpose array-processing package. It provides a high-performance
multidimensional array object, and tools for working with these arrays. It is the fundamental
package for scientific computing with Python. Besides its obvious scientific uses, Numpy can
also be used as an efficient multi-dimensional container of generic data.
Features:
High-performance N-dimensional array object.
It contains tools for integrating code from C/C++ and FORTRAN.
It contains a multidimensional container for generic data.
Additional linear algebra, Fourier transforms, and random number capabilities.
It consists of broadcasting functions.
It had data type definition capability to work with varied databases.
Sample Program:
import numpy as np
a=np.array([1,2,3])
print(a)
Output:
[1 2 3]
SCIPY:
SciPy is a python library that is useful in solving many mathematical equations and
algorithms. It is designed on the top of Numpy library that gives more extension of finding
scientific mathematical formulae like Matrix Rank, Inverse, polynomial equations, LU
Decomposition, etc. Using its high level functions will significantly reduce the complexity of the
code and helps in better analyzing the data. SciPy is an interactive Python session used as a data-
,processing library that is made to compete with its rivalries such as MATLAB, Octave, R-
Lab,etc. It has many user-friendly, efficient and easy-to-use functions that helps to solve
problems like numerical integration, interpolation, optimization, linear algebra and statistics.
Sample Program:
from scipy import constants
print(constants.pi)
Output:
3.141592653589793
JUPYTER:
The IPython Notebook concept was expanded upon to allow for additional programming
languages and was therefore renamed "Jupyter". "Jupyter" is a loose acronym meaning Julia,
Python and R, but today, the notebook technology supports many programming languages. An
IDE normally consists of at least a source code editor, build automation tools and a
debugger. Jupyter Notebook is an IDE for Python that allows its users to create documents
containing both rich text and code. It also supports the programming languages Julia, and R.
Jupyter Notebook allows users to compile all aspects of a data project in one place
making it easier to show the entire process of a project to your intended audience. Through the
web-based application, users can create data visualizations and other components of a project to
share with others via the platform.
,
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