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Basic NumPy for beginners:

Creating a NumPy array: To create a NumPy array from a list or tuple, you can use the np.array() function. For example, the following code creates a NumPy array from a list of numbers:

import numpy as np

# Create a NumPy array from a list of numbers
numbers = [1, 2, 3, 4, 5]
numbers_array = np.array(numbers)

# Print the array
print(numbers_array)

output:
[1 2 3 4 5]

Basic mathematical operations: NumPy provides functions for performing mathematical operations on arrays, such as addition, subtraction, multiplication, and division. These operations can be performed element-wise, allowing for efficient computation on large datasets. For example, the following code adds two NumPy arrays element-wise:

import numpy as np

# Create two NumPy arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([6, 7, 8, 9, 10])

# Add the arrays element-wise
z = x + y

# Print the result
print(z)

output:
[ 7 9 11 13 15]

Indexing and slicing: NumPy arrays can be indexed and sliced just like lists. This allows you to access and manipulate specific elements or subarrays within an array. For example, the following code slices a NumPy array to extract the second and third elements:

import numpy as np

# Create a NumPy array
numbers = np.array([1, 2, 3, 4, 5])

# Slice the array to extract the second and third elements
subarray = numbers[1:3]

# Print the result
print(subarray)

output:
[2 3]

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Basic NumPy for beginners:

Creating a NumPy array: To create a NumPy array from a list or tuple, you can use the np.array() function. For example, the following code creates a NumPy array from a list of numbers:

import numpy as np

# Create a NumPy array from a list of numbers
numbers = [1, 2, 3, 4, 5]
numbers_array = np.array(numbers)

# Print the array
print(numbers_array)

output:
[1 2 3 4 5]

Basic mathematical operations: NumPy provides functions for performing mathematical operations on arrays, such as addition, subtraction, multiplication, and division. These operations can be performed element-wise, allowing for efficient computation on large datasets. For example, the following code adds two NumPy arrays element-wise:

import numpy as np

# Create two NumPy arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([6, 7, 8, 9, 10])

# Add the arrays element-wise
z = x + y

# Print the result
print(z)

output:
[ 7 9 11 13 15]

Indexing and slicing: NumPy arrays can be indexed and sliced just like lists. This allows you to access and manipulate specific elements or subarrays within an array. For example, the following code slices a NumPy array to extract the second and third elements:

import numpy as np

# Create a NumPy array
numbers = np.array([1, 2, 3, 4, 5])

# Slice the array to extract the second and third elements
subarray = numbers[1:3]

# Print the result
print(subarray)

output:
[2 3]

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There are multiple ways you can search for Telegram channels. One of the methods is really logical and you should all know it by now. We’re talking about using Telegram’s native search option. Make sure to download Telegram from the official website or update it to the latest version, using this link. Once you’ve installed Telegram, you can simply open the app and use the search bar. Tap on the magnifier icon and search for a channel that might interest you (e.g. Marvel comics). Even though this is the easiest method for searching Telegram channels, it isn’t the best one. This method is limited because it shows you only a couple of results per search.

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