Python tip:
Use np.delete() to delete elements from an array.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
deleted_arr = np.delete(arr, [1, 3]) # Delete elements at indices 1 and 3
print(deleted_arr) # Output: [1 3 5]

Python tip:
Convert radians to degrees and vice versa using np.degrees() and np.radians().

import numpy as np
rad = np.pi / 2 # 90 degrees in radians
deg = np.degrees(rad)
print(f"Degrees: {deg}")
rad_again = np.radians(deg)
print(f"Radians: {rad_again}")

Python tip:
Use np.round(decimals=...) to specify the number of decimal places for rounding.

import numpy as np
arr = np.array([1.234, 5.678])
rounded_arr = np.round(arr, decimals=1)
print(rounded_arr) # Output: [1.2 5.7]

Python tip:
Replace elements satisfying a condition with another value using np.put().

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
np.put(arr, [0, 2], [100, 300]) # Replace element at index 0 with 100, at index 2 with 300
print(arr) # Output: [100  20 300  40  50]

Python tip:
Use np.vstack() and np.hstack() as shortcuts for common concatenations.

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
print(np.vstack((a, b)))
print(np.hstack((a, b)))

Python tip:
Use np.dstack() to stack arrays along the third axis.

import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
stacked_d = np.dstack((a, b)) # Resulting shape (2, 2, 2)
print(stacked_d)

Python tip:
Initialize an array using a function applied to its indices with np.fromfunction().

import numpy as np
def func(i, j):
    return i + j

arr = np.fromfunction(func, (3, 3), dtype=int)
print(arr)

Python tip:
Access a contiguous block of memory for performance using array.view().

import numpy as np
arr = np.array([1, 2, 3, 4])
# View as different dtype (e.g., bytes)
byte_view = arr.view(np.int8)
print(byte_view)

Python tip:
Use np.unwrap() to unwrap phase angles.

import numpy as np
phase = np.array([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi, 5*np.pi/2])
unwrapped_phase = np.unwrap(phase)
print(unwrapped_phase)

Python tip:
Compute the cumulative sum of array elements using np.cumsum().

import numpy as np
arr = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(arr)
print(cumulative_sum) # Output: [ 1  3  6 10]

Python tip:
Compute the cumulative product of array elements using np.cumprod().

import numpy as np
arr = np.array([1, 2, 3, 4])
cumulative_prod = np.cumprod(arr)
print(cumulative_prod) # Output: [ 1  2  6 24]

Python tip:
Check if an array contains any element that evaluates to true using arr.any().

import numpy as np
arr_true = np.array([False, True, False])
arr_false = np.array([False, False, False])
print(arr_true.any()) # Output: True
print(arr_false.any()) # Output: False

Python tip:
Check if all elements in an array evaluate to true using arr.all().

import numpy as np
arr_true = np.array([True, True, True])
arr_false = np.array([True, False, True])
print(arr_true.all()) # Output: True
print(arr_false.all()) # Output: False

Python tip:
Use np.pad() to add padding to an array.

import numpy as np
arr = np.array([1, 2, 3])
padded_arr = np.pad(arr, (1, 2), 'constant', constant_values=0) # Pad 1 before, 2 after with 0s
print(padded_arr) # Output: [0 1 2 3 0 0]

Python tip:
Use np.trim_zeros() to remove leading/trailing zeros from a 1D array.

import numpy as np
arr = np.array([0, 0, 1, 2, 0, 3, 0, 0])
trimmed_arr = np.trim_zeros(arr)
print(trimmed_arr) # Output: [1 2 0 3]

Python tip:
Use np.count_nonzero() to count the number of non-zero elements.

import numpy as np
arr = np.array([0, 1, 0, 2, 0, 3])
non_zeros = np.count_nonzero(arr)
print(non_zeros) # Output: 3

Python tip:
Access the real part of complex numbers using .real.

import numpy as np
complex_arr = np.array([1+2j, 3-4j])
print(complex_arr.real) # Output: [1. 3.]

Python tip:
Access the imaginary part of complex numbers using .imag.

import numpy as np
complex_arr = np.array([1+2j, 3-4j])
print(complex_arr.imag) # Output: [ 2. -4.]

Python tip:
Calculate the element-wise difference between a sorted array and itself with a specified lag using np.diff().

import numpy as np
arr = np.array([1, 2, 4, 7, 11, 16])
diff = np.diff(arr)
print(diff) # Output: [1 2 3 4 5]

Python tip:
Perform element-wise maximum of two arrays using np.maximum().

import numpy as np
a = np.array([1, 5, 2])
b = np.array([3, 2, 6])
max_elements = np.maximum(a, b)
print(max_elements) # Output: [3 5 6]

Python tip:
Perform element-wise minimum of two arrays using np.minimum().

import numpy as np
a = np.array([1, 5, 2])
b = np.array([3, 2, 6])
min_elements = np.minimum(a, b)
print(min_elements) # Output: [1 2 2]

Python tip:
Use np.where() with a condition to replace elements.

import numpy as np
arr = np.array([1, -2, 3, -4, 5])
positive_arr = np.where(arr < 0, 0, arr) # Replace negative numbers with 0
print(positive_arr) # Output: [1 0 3 0 5]

Python tip:
Create a sequence of numbers with specific number of samples from np.linspace() when generating values for plotting.

import numpy as np
x_vals = np.linspace(-np.pi, np.pi, 100) # 100 points between -pi and pi
y_vals = np.sin(x_vals)
print(y_vals.shape)

Python tip:
Use np.trim_zeros() to remove leading and trailing zeros from an array.

import numpy as np
arr = np.array([0, 0, 1, 2, 0, 3, 0, 0])
trimmed = np.trim_zeros(arr)
print(trimmed) # Output: [1 2 0 3]

Python tip:
Use np.delete() with axis to delete rows or columns from 2D arrays.

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Delete row at index 1
deleted_row = np.delete(matrix, 1, axis=0)
print(f"After deleting row 1:\n{deleted_row}")
# Delete column at index 0
deleted_col = np.delete(matrix, 0, axis=1)
print(f"After deleting col 0:\n{deleted_col}")

Python tip:
Use np.set_printoptions() to control how NumPy arrays are printed (e.g., precision, suppression).

import numpy as np
np.set_printoptions(precision=3, suppress=True)
arr = np.array([0.12345678, 100.0/3.0])
print(arr) # Output: [  0.123  33.333]
np.set_printoptions(edgeitems=2) # Show only first/last 2 items for long arrays
print(np.arange(20))

Python tip:
Avoid explicit Python loops when working with NumPy arrays for significant performance gains (vectorization).

import numpy as np
arr = np.arange(1_000_000)

# Slow (Python loop)
# result_loop = []
# for x in arr:
#     result_loop.append(x * 2)

# Fast (NumPy vectorized)
result_vec = arr * 2
print("Vectorized operation is much faster.")

Python tip:
Use arr.clip() as an instance method for np.clip().

import numpy as np
arr = np.array([1, 10, 3, 15, 6])
clipped_arr = arr.clip(3, 10)
print(clipped_arr)

Python tip:
Use np.append() to add values to the end of an array. It always returns a new array.

import numpy as np
arr = np.array([1, 2, 3])
appended_arr = np.append(arr, [4, 5])
print(appended_arr) # Output: [1 2 3 4 5]

Python tip:
Convert between column and row vectors using reshape() or np.newaxis.

import numpy as np
row_vec = np.array([1, 2, 3]) # (3,)
col_vec_1 = row_vec.reshape(-1, 1) # (3, 1)
col_vec_2 = row_vec[:, np.newaxis] # (3, 1)
print(col_vec_1)

Python tip:
Use np.moveaxis() to move an axis of an array to a new position.

import numpy as np
arr = np.zeros((1, 2, 3, 4)) # Example shape
# Move axis 0 to position 2
moved_arr = np.moveaxis(arr, 0, 2) # From (1,2,3,4) to (2,3,1,4)
print(moved_arr.shape)

Python tip:
Use np.swapaxes() to swap two axes of an array.

import numpy as np
arr = np.zeros((2, 3, 4))
swapped_arr = np.swapaxes(arr, 0, 1) # Swap axis 0 and 1
print(swapped_arr.shape) # From (2,3,4) to (3,2,4)

Python tip:
Use np.stack() to join arrays along a new axis.

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
stacked_new_axis = np.stack((a, b), axis=-1) # (2, 2)
print(stacked_new_axis)

Python tip:
Use np.tile() to repeat an array n times to create a larger array.

import numpy as np
a = np.array([1, 2])
tiled_a = np.tile(a, 3) # Repeat [1,2] three times -> [1,2,1,2,1,2]
print(tiled_a)

Python tip:
Use np.resize() to change the shape of an array. If new size is larger, it fills with repetitions.

import numpy as np
arr = np.array([1, 2])
resized_arr = np.resize(arr, (2, 3)) # Resizes to 2x3, filling with repetitions
print(resized_arr)

Python tip:
Convert angles from degrees to radians using np.deg2rad().

import numpy as np
degrees = np.array([0, 90, 180])
radians = np.deg2rad(degrees)
print(radians) # Output: [0.        1.57079633 3.14159265] (0, pi/2, pi)

Python tip:
Convert angles from radians to degrees using np.rad2deg().

import numpy as np
radians = np.array([0, np.pi/2, np.pi])
degrees = np.rad2deg(radians)
print(degrees) # Output: [  0.  90. 180.]

Python tip:
Use np.around() for rounding, similar to np.round().

import numpy as np
arr = np.array([1.5, 2.7, 3.1])
around_arr = np.around(arr)
print(around_arr) # Output: [2. 3. 3.]

Python tip:
Use np.sqrt() for element-wise square root.

import numpy as np
arr = np.array([1, 4, 9, 16])
sqrt_arr = np.sqrt(arr)
print(sqrt_arr) # Output: [1. 2. 3. 4.]

Python tip:
Use np.power() for element-wise exponentiation.

import numpy as np
arr = np.array([2, 3, 4])
power_arr = np.power(arr, 2) # arr squared
print(power_arr) # Output: [ 4  9 16]

Python tip:
Use np.mod() for element-wise modulus.

import numpy as np
arr = np.array([10, 11, 12])
mod_arr = np.mod(arr, 3) # Remainder when divided by 3
print(mod_arr) # Output: [1 2 0]

Python tip:
Use np.absolute() for element-wise absolute value, same as np.abs().

import numpy as np
arr = np.array([-1, -2, 0, 3])
abs_arr = np.absolute(arr)
print(abs_arr) # Output: [1 2 0 3]

Python tip:
Use np.sign() to get the sign of each element (-1 for negative, 0 for zero, 1 for positive).

import numpy as np
arr = np.array([-5, 0, 3])
sign_arr = np.sign(arr)
print(sign_arr) # Output: [-1  0  1]

Python tip:
Use np.true_divide() for float division, even with integers.

import numpy as np
result = np.true_divide(7, 3)
print(result) # Output: 2.3333333333333335

Python tip:
Use np.floor_divide() for integer division (truncates towards negative infinity).

import numpy as np
result = np.floor_divide(7, 3)
print(result) # Output: 2
result_neg = np.floor_divide(-7, 3)
print(result_neg) # Output: -3

Python tip:
Use np.divmod() to get both quotient and remainder from division.

import numpy as np
quotient, remainder = np.divmod(10, 3)
print(f"Quotient: {quotient}, Remainder: {remainder}") # Output: Quotient: 3, Remainder: 1

Python tip:
Convert array elements to boolean using np.array.astype(bool).

import numpy as np
arr = np.array([0, 1, -5, 0.0, 0.1])
bool_arr = arr.astype(bool)
print(bool_arr) # Output: [False  True  True False  True]

Python tip:
Generate a sequence of values in a specific range and step for floating-point numbers using np.r_.

import numpy as np
seq = np.r_[0:10:0.5] # Start at 0, stop at 10, step 0.5
print(seq)

Python tip:
Convert a value to a NumPy array for consistent operations using np.asarray().

import numpy as np
my_list = [1, 2, 3]
arr = np.asarray(my_list)
print(arr)

Python tip:
Use np.unique(return_counts=True) to get unique elements and their frequencies.

import numpy as np
arr = np.array([1, 1, 2, 3, 2, 4, 1, 5])
unique_elements, counts = np.unique(arr, return_counts=True)
print(f"Unique elements: {unique_elements}")
print(f"Counts: {counts}")

Python tip:
Access rows and columns of 2D arrays directly by slicing.

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
first_row = matrix[0, :] # All columns of the first row
second_col = matrix[:, 1] # All rows of the second column
print(f"First row: {first_row}")
print(f"Second column: {second_col}")

Python tip:
Use np.diagflat() to create a diagonal array from a flattened input.

import numpy as np
arr = np.array([[1,2],[3,4]])
diag_matrix = np.diagflat(arr)
print(diag_matrix)

Python tip:
Get the trace of a square matrix (sum of main diagonal elements) using np.trace().

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
trace = np.trace(matrix)
print(trace) # Output: 15 (1 + 5 + 9)

Python tip:
Compute the Kronecker product of two arrays using np.kron().

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
kronecker_product = np.kron(a, b)
print(kronecker_product) # Output: [3 4 6 8]

Python tip:
Use np.linalg.matrix_power() to raise a square matrix to the (integer) power n.

import numpy as np
matrix = np.array([[1, 2], [3, 4]])
matrix_squared = np.linalg.matrix_power(matrix, 2)
print(matrix_squared)

Python tip:
Check for finite values (not NaN or Inf) using np.isfinite().

import numpy as np
arr = np.array([1, np.nan, np.inf, -np.inf, 5])
finite_arr = np.isfinite(arr)
print(finite_arr) # Output: [ True False False False  True]

Python tip:
Use np.around() to round to a specified number of decimals.

import numpy as np
val = 123.4567
rounded_val = np.around(val, decimals=2)
print(rounded_val) # Output: 123.46

Python tip:
Generate a Pascal matrix using np.pascal(). (Note: np.pascal() is not a standard NumPy function. If you need it, you'd implement it manually or use SciPy.)

import numpy as np
# This is a conceptual tip as np.pascal doesn't exist.
# You would usually implement it manually:
def pascal_matrix(n):
    matrix = np.zeros((n, n), dtype=int)
    for i in range(n):
        for j in range(n):
            if i == 0 or j == 0:
                matrix[i, j] = 1
            else:
                matrix[i, j] = matrix[i-1, j] + matrix[i, j-1]
    return matrix

print(pascal_matrix(4))

Python tip:
Use np.convolve() to compute the discrete linear convolution of two 1D arrays.

import numpy as np
a = np.array([1, 2, 3])
b = np.array([0, 1, 0.5])
convolution = np.convolve(a, b)
print(convolution) # Output: [0.  1.  2.5 4.  1.5]

Python tip:
Use np.polyval() to evaluate a polynomial at specific values.

import numpy as np
poly_coeffs = np.array([3, 0, 1]) # Represents 3x^2 + 0x + 1
x_values = np.array([0, 1, 2])
y_values = np.polyval(poly_coeffs, x_values)
print(y_values) # Output: [ 1  4 13] (3*0^2+1, 3*1^2+1, 3*2^2+1)

Python tip:
Use np.polyfit() to find the coefficients of a polynomial that best fits a set of data points.

import numpy as np
x = np.array([0, 1, 2, 3])
y = np.array([0, 0.8, 0.9, 0.1])
coefficients = np.polyfit(x, y, 2) # Fit a 2nd degree polynomial
print(coefficients)

Python tip:
Use np.clip() to limit values in an array to a specified range, as an instance method.

import numpy as np
arr = np.array([1, 10, 3, 15, 6])
clipped_arr = arr.clip(min=3, max=10)
print(clipped_arr)

Python tip:
Use np.squeeze() to remove single-dimensional entries from the shape of an array.

import numpy as np
arr = np.zeros((1, 3, 1, 4))
squeezed_arr = np.squeeze(arr) # Removes axes of length 1
print(squeezed_arr.shape) # Output: (3, 4)

Python tip:
Create a new array with an inserted axis using np.expand_dims().

import numpy as np
arr = np.array([1, 2, 3]) # Shape (3,)
expanded_arr = np.expand_dims(arr, axis=0) # Add a new axis at position 0
print(expanded_arr.shape) # Output: (1, 3)

Python tip:
Use np.ptp() (peak-to-peak) to find the range (max - min) of an array.

import numpy as np
arr = np.array([1, 5, 2, 8, 3])
peak_to_peak = np.ptp(arr)
print(peak_to_peak) # Output: 7 (8 - 1)

Python tip:
Use np.prod() to calculate the product of array elements.

import numpy as np
arr = np.array([1, 2, 3, 4])
product = np.prod(arr)
print(product) # Output: 24 (1 * 2 * 3 * 4)

Python tip:
Use np.allclose() to compare two arrays for equality within a tolerance.

import numpy as np
a = np.array([1.0, 2.0])
b = np.array([1.00000000001, 2.0])
print(np.allclose(a, b)) # Output: True

Python tip:
Use np.array_split() to split an array into N approximately equal sub-arrays.

import numpy as np
arr = np.arange(7)
split_arr = np.array_split(arr, 3) # Split into 3 parts
print(split_arr)

#NumPyTips #PythonNumericalComputing #ArrayManipulation #DataScience #MachineLearning #PythonTips #NumPyForBeginners #Vectorization #LinearAlgebra #StatisticalAnalysis

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

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#Python #CProgramming #PerformanceOptimization #Coding

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As software development becomes increasingly complex, developers are turning to AI tools that can manage, understand and automate large portions of the coding workflow. Among the most promising innovations in this space is Plandex AI, an open-source terminal-based coding agent designed for real-world, large-scale projects. Unlike simple AI coding assistants that handle small snippets, Plandex ...

#PlandexAI #AutonomousCoding #LargeScaleDevelopment #AICoding #OpenSourceAI #CodeAutomation

📌 The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example)

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-11 | ⏱️ Read time: 10 min read

This article charts the evolution of the data scientist's role through three distinct eras: traditional machine learning, deep learning, and the current age of large language models (LLMs). Using a single, practical use case, it illustrates how the approach to problem-solving has shifted with each technological generation. The piece serves as a guide for practitioners, clarifying when to leverage classic algorithms, complex neural networks, or the latest foundation models, helping them select the most appropriate tool for the task at hand.

#DataScience #MachineLearning #DeepLearning #LLM

📌 How to Build Agents with GPT-5

🗂 Category: AGENTIC AI

🕒 Date: 2025-11-11 | ⏱️ Read time: 8 min read

Learn how to use GPT-5 as a powerful AI Agent on your data.

#DataScience #AI #Python