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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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I have no inside knowledge of a potential stock listing of the popular anti-Whatsapp messaging app, Telegram. But I know this much, judging by most people I talk to, especially crypto investors, if Telegram ever went public, people would gobble it up. I know I would. I’m waiting for it. So is Sergei Sergienko, who claims he owns $800,000 of Telegram’s pre-initial coin offering (ICO) tokens. “If Telegram does a SPAC IPO, there would be demand for this issue. It would probably outstrip the interest we saw during the ICO. Why? Because as of right now Telegram looks like a liberal application that can accept anyone - right after WhatsApp and others have turn on the censorship,” he says.

Launched in 2013, Telegram allows users to broadcast messages to a following via “channels”, or create public and private groups that are simple for others to access. Users can also send and receive large data files, including text and zip files, directly via the app.The platform said it has more than 500m active users, and topped 1bn downloads in August, according to data from SensorTower.Python Codes from tw


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