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Summary Python and matplotlib Study Notes – Easy, Clear & Exam Ready

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These notes cover the complete basics and important concepts of Python programming, NumPy, and Matplotlib, explained in a simple, clear, and easy-to-understand format. The Python section includes all essential topics such as variables, data types, loops, functions, file handling, lists, tuples, dictionaries, and more. The NumPy section covers array creation, indexing, slicing, mathematical operations, functions, reshaping, broadcasting, and commonly used numerical computations. The Matplotlib section provides a beginner-friendly introduction to data visualization, including plotting line graphs, bar charts, scatter plots, histograms, customizing colors/styles, labels, titles, grids, subplots, and saving figures. These notes make it easy to understand how to visualize data using Python. These notes are perfect for students, beginners, and anyone preparing for exams, coding interviews, or data science courses. They include examples, explanations, and practice-friendly content to help you learn Python, NumPy, and Matplotlib quickly and effectively. Ideal for self-study, revision, assignments, and fast learning.

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MatPlotLib
A multiplatform data visualization library

, MatPlotLib
• Matplotlib is a multiplatform data
visualization library built on NumPy arrays,
and designed to work with the broader SciPy
stack.
• It was conceived by John Hunter in 2002,
originally as a patch to IPython for enabling
interactive MATLAB-style plotting via gnuplot
from the IPython command line.

, • This cross-platform, everything-to-everyone
approach has been one of the great strengths of
Matplotlib.
• It has led to a large userbase, which in turn has led
to an active developer base and Mat plotlib’s
powerful tools and ubiquity within the scientific
Python world.
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.style.use('classic')
x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
plt.show()

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