Introduction to numpy
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This document provides an introduction and overview of NumPy, a Python library used for numerical computing. It discusses NumPy's origins and capabilities, how to install NumPy on Linux, key NumPy concepts like the ndarray object, and how NumPy can be used with Matplotlib for plotting. Examples are given of common NumPy operations and functions for arrays, as well as plotting simple graphs with Matplotlib.
![Example
import numpy as np
a = np.array([1,2,3])
print a
import numpy as np
a = np.array([[1, 2], [3, 4]])
print a
import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print a
import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print a](https://image.slidesharecdn.com/introductiontonumpy-200510135746/85/Introduction-to-numpy-8-320.jpg)
![bar()
from matplotlib import pyplot as plt
x = [5,8,10]
y = [12,16,6]
x2 = [6,9,11]
y2 = [6,15,7]
plt.bar(x, y, align = 'center')
plt.bar(x2, y2, color = 'g', align = 'center')
plt.title('Bar graph')
plt.ylabel('Y axis')
plt.xlabel('X axis')
plt.show()](https://image.slidesharecdn.com/introductiontonumpy-200510135746/85/Introduction-to-numpy-12-320.jpg)
Introduction to numpy
- 2. Term paper- Programming II Submitted to: Dr. Dharmendra Singh Submitted by : Gaurav 19mslsbf03
- 3. Table of Contents Introduction Operations using NumPy NumPy – A Replacement for MatLab Installation of numpy package on Linux-Ubuntu Numpy-ndarray Numpy-matplotlib
- 4. Introduction • 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. • Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another package Numarray was also developed, having some additional functionalities. In 2005, Travis Oliphant created NumPy package by incorporating the features of Numarray into Numeric package. There are many contributors to this open source project.
- 5. Operations using NumPy 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 Mat−plotlib (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.
- 6. Installation of numpy package on Linux-Ubuntu ⁓$sudo apt-get install python-numpy python-scipy python- matplotlibipythonipythonnotebook python-pandas python-sympy python- nose Use this command in the Terminal of Ubuntu system to install numpy package
- 7. Numpy-ndarray • 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 an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).
- 8. Example import numpy as np a = np.array([1,2,3]) print a import numpy as np a = np.array([[1, 2], [3, 4]]) print a import numpy as np a = np.array([1, 2, 3,4,5], ndmin = 2) print a import numpy as np a = np.array([1, 2, 3], dtype = complex) print a
- 9. Numpy-Matplotlib 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()
- 10. 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,"ob") plt.show()
- 11. import numpy as np import matplotlib.pyplot as plt # Compute the x and y coordinates for points on a sine curve x = np.arange(0, 3 * np.pi, 0.1) y = np.sin(x) plt.title("sine wave form") # Plot the points using matplotlib plt.plot(x, y) plt.show() Sine Wave Plot
- 12. bar() from matplotlib import pyplot as plt x = [5,8,10] y = [12,16,6] x2 = [6,9,11] y2 = [6,15,7] plt.bar(x, y, align = 'center') plt.bar(x2, y2, color = 'g', align = 'center') plt.title('Bar graph') plt.ylabel('Y axis') plt.xlabel('X axis') plt.show()