Basic Introduction to Python Programming

Basic Introduction to Python Programming

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  PYTHON LANGUAGE Dr. B. Subashini, AssistantProfessor of Computer Applications, V.V.Vanniaperumal College for Women, Virudhunagar
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  BRIEF HISTORY OFPYTHON • Invented in the Netherlands, early 90s by Guido van Rossum • Named after Monty Python • Open sourced from the beginning • Considered a scripting language, but is much more • Scalable, object oriented and functional from the beginning • Used by Google from the beginning • Increasingly popular
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  PYTHON • Python canbe used on a server to create web applications. • Python can be used alongside software to create workflows. • Python can connect to database systems. It can also read and modify files. • Python can be used to handle big data and perform complex mathematics. • Python can be used for rapid prototyping, or for production-ready software development.
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  WHY PYTHON? • Pythonworks on different platforms (Windows, Mac, Linux, Raspberry Pi, etc). • Python has a simple syntax similar to the English language. • Python has syntax that allows developers to write programs with fewer lines than some other programming languages. • Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick. • Python can be treated in a procedural way, an object-oriented way or a functional way.
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  VERSION • The mostrecent major version of Python is Python 3 • 3.12.0 is the latest version of Python released on October 2, 2023 • However, Python 2 being updated with security, is still quite popular
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  PYTHON SYNTAX • Pythonwas designed for readability, and has some similarities to the English language with influence from mathematics. • Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses. • Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose.
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  COMMENTS • Comments canbe used to explain Python code. • Comments can be used to make the code more readable. • Comments can be used to prevent execution when testing code. • Example: • #This is a comment print("Hello, World!")
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  VARIABLES • Variables arecontainers for storing data values. • Python has no command for declaring a variable. • A variable is created the moment just first assign a value to it. • x = 5 y = "John" print(x) print(y)
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  VARIABLE NAMES • Avariable name must start with a letter or the underscore character • A variable name cannot start with a number • A variable name can only contain alpha-numeric characters and underscores (A-z, 0-9, and _ ) • Variable names are case-sensitive (age, Age and AGE are three different variables) • A variable name cannot be any of the Python Keywords.
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  BUILT IN DATATYPES Text Type: str Numeric Types: int, float, complex Sequence Types: list, tuple, range Mapping Type: dict Set Types: set, frozenset Boolean Type: bool Binary Types: bytes, bytearray, memoryview None Type: NoneType
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  NUMPY • Stands forNumerical Python • Is the fundamental package required for high performance computing and data analysis • NumPy is so important for numerical computations in Python is because it is designed for efficiency on large arrays of data. • It provides ndarray for creating multiple dimensional arrays. • Internally stores data in a contiguous block of memory, independent of other built-in Python objects, use much less memory than built-in Python sequences.
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  NUMPY NDARRAY VSLIST • Standard math functions for fast operations on entire arrays of data without having to write loops • NumPy Arrays are important because they enable to express batch operations on data without writing any for loops. We call this vectorization. • One of the key features of NumPy is its N-dimensional array object, or ndarray, which is a fast, flexible container for large datasets in Python. • Whenever you see “array,” “NumPy array,” or “ndarray” in the text, with few exceptions they all refer to the same thing: the ndarray object. • NumPy-based algorithms are generally 10 to 100 times faster (or more) than their pure Python counterparts and use significantly less memory. import numpy as np my_arr = np.arange(1000000)
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  NDARRAY • ndarray isused for storage of homogeneous data • i.e., all elements the same type • Every array must have a shape and a dtype • Supports convenient slicing, indexing and efficient vectorized computation import numpy as np data1 = [6, 7, 5, 8, 0, 1] arr1 = np.array(data1) print(arr1)
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  CREATING NDARRAYS Using listof lists import numpy as np data2 = [[1, 2, 3, 4], [5, 6, 7, 8]] #list of lists arr2 = np.array(data2) print(arr2.ndim) #2 print(arr2.shape) # (2,4)
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  array = np.array([[0,1,2],[2,3,4]]) [[01 2] [2 3 4]] array = np.zeros((2,3)) [[0. 0. 0.] [0. 0. 0.]] array = np.ones((2,3)) [[1. 1. 1.] [1. 1. 1.]] array = np.arange(0, 10, 2) [0, 2, 4, 6, 8] array = np.eye(3) [[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]] array = np.random.randint(0, 10, (3,3)) [[6 4 3] [1 5 6] [9 8 5]] CREATING NDARRAYS
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  ARITHMETIC WITH NUMPY ARRAYS •Any arithmetic operations between equal-size arrays applies the operation element-wise: arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr) [[1 2 3] [4 5 6]] print(arr * arr) [[ 1 4 9] [16 25 36]] print(arr - arr) [[0 0 0] [0 0 0]]
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  ARITHMETIC WITH NUMPY ARRAYS •Arithmetic operations with scalars propagate the scalar argument to each element in the array: arr = np.array([[1., 2., 3.], [4., 5., 6.]]) print(arr) [[1. 2. 3.] [4. 5. 6.]] print(arr **2) [[ 1. 4. 9.] [16. 25. 36.]]
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  ARITHMETIC WITH NUMPY ARRAYS •Comparisons between arrays of the same size yield boolean arrays: arr = np.array([[1., 2., 3.], [4., 5., 6.]]) print(arr) [[1. 2. 3.] [4. 5. 6.]] arr2 = np.array([[0., 4., 1.], [7., 2., 12.]]) print(arr2) [[ 0. 4. 1.] [ 7. 2. 12.]] print(arr2 > arr) [[False True False] [ True False True]]
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  INDEXING AND SLICING •One-dimensional arrays are simple; on the surface they act similarly to Python lists: arr = np.arange(10) print(arr) # [0 1 2 3 4 5 6 7 8 9] print(arr[5]) #5 print(arr[5:8]) #[5 6 7] arr[5:8] = 12 print(arr) #[ 0 1 2 3 4 12 12 12 8 9]
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  INDEXING AND SLICING •An important first distinction from Python’s built-in lists is that array slices are views on the original array. • This means that the data is not copied, and any modifications to the view will be reflected in the source array. arr = np.arange(10) print(arr) # [0 1 2 3 4 5 6 7 8 9] arr_slice = arr[5:8] print(arr_slice) # [5 6 7] arr_slice[1] = 12345 print(arr) # [ 0 1 2 3 4 5 12345 7 8 9] arr_slice[:] = 64 print(arr) # [ 0 1 2 3 4 64 64 64 8 9]
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  INDEXING • In atwo-dimensional array, the elements at each index are no longer scalars but rather one-dimensional arrays: • Thus, individual elements can be accessed recursively. But that is a bit too much work, so we can pass a comma-separated list of indices to select individual elements. • So these are equivalent: arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) print(arr2d[2]) # [7 8 9] print(arr2d[0][2]) # 3 print(arr2d[0, 2]) #3