Convert 2d array to 1d array using list comprehension
How to square each element in a 2D array
How to square each element after flattening 2d array
Filter elements greater than 0.5 in a 2D array
How to convert predicted probabilities into predicted labels
Finding most prominent features or peak values in your dataset
Finding the minimum value helps identify the least significant or baseline values in your dataset
How to normalize features, centering them around their average
How to measure of central tendency, especially with skewed data or outlier
How calculating total counts or feature combinations in ML preprocessing
How to spread or variability in the dataset, essential for feature scaling
How to Variance quantifies how much the data points deviate from the mean
How to identify which feature is most or least significant in each sample
How Cumulative sums is helpful for time-series analysis
How percentile is used in outlier detection
Add 10 to each element of array
Convert 2d array to 1d array using list comprehension
array = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Using List Comprehension
flattened_array = [item for row in array for item in row]
print(flattened_array) # Output: [1, 2, 3, 4, 5, 6, 7, 8, 9]
Using a Nested for Loop
flattened_array = []
for row in array:
for item in row:
flattened_array.append(item)
print(flattened_array)
Output:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
Both methods will produce the same flattened array [1, 2, 3, 4, 5, 6, 7, 8, 9].
How to square each element in a 2D array
Given Example Data
Using the same 2D array example:
array = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Using List Comprehension
squared_array = [[element**2 for element in row] for row in array]
print(squared_array) # Output: [[1, 4, 9], [16, 25, 36], [49, 64, 81]]
Using a Nested for Loop
squared_array = []
for row in array:
squared_row = []
for element in row:
squared_row.append(element**2)
squared_array.append(squared_row)
print(squared_array)
Output:
[[1, 4, 9], [16, 25, 36], [49, 64, 81]]
Both methods will yield the same squared array [[1, 4, 9], [16, 25, 36], [49, 64, 81]].
How to square each element after flattening 2d array
Given Example Data
array = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Using List Comprehension
Flatten and then square each element:
flattened_array = [item for row in array for item in row]
squared_array = [item**2 for item in flattened_array]
print(squared_array) # Output: [1, 4, 9, 16, 25, 36, 49, 64, 81]
Using a Nested for Loop
# Flattening the array
flattened_array = []
for row in array:
for item in row:
flattened_array.append(item)
# Squaring each element
squared_array = []
for item in flattened_array:
squared_array.append(item**2)
print(squared_array) # Output: [1, 4, 9, 16, 25, 36, 49, 64, 81]
Filter elements greater than 0.5 in a 2D array
array = [[0.1, 0.6, 0.3], [0.7, 0.2, 0.9], [0.4, 0.8, 0.05]]
Using List Comprehension
filtered_elements = [element for row in array for element in row if element > 0.5]
print(filtered_elements) # Output: [0.6, 0.7, 0.9, 0.8]
Using a Nested for Loop
filtered_elements = []
for row in array:
for element in row:
if element > 0.5:
filtered_elements.append(element)
print(filtered_elements) #
Output:
[0.6, 0.7, 0.9, 0.8]
Both methods will produce the same filtered list [0.6, 0.7, 0.9, 0.8].
How to convert predicted probabilities into predicted labels
Given Example Data
Let's assume y_predicted is a list of lists, representing the predicted probabilities for each class in a multi-class classification problem:
import numpy as np
y_predicted = [
[0.1, 0.3, 0.6], # Prediction for the first sample
[0.8, 0.1, 0.1], # Prediction for the second sample
[0.2, 0.5, 0.3] # Prediction for the third sample
]
Using List Comprehension
y_predicted_labels = [np.argmax(i) for i in y_predicted]
print(y_predicted_labels) # Output: [2, 0, 1]
Using a for Loop
y_predicted_labels = []
for i in y_predicted:
y_predicted_labels.append(np.argmax(i))
print(y_predicted_labels) # Output: [2, 0, 1]
In both methods, np.argmax finds the index of the maximum value in each list (i.e., the predicted class), resulting in [2, 0, 1] for this example data.
Numpy aggreagate command
Given Example Data
import numpy as np
data = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
Finding most prominent features or peak values in your dataset
np.max() - Finding Maximum Values
In ML, finding the maximum value can help identify the most prominent features or peak values in your dataset.
Using List Comprehension:
max_values = [np.max(row) for row in data]
print(max_values) # Output: [3, 6, 9]
Explanation: The maximum value of each row is found, which could represent the strongest feature in each data sample.
Finding the minimum value helps identify the least significant or baseline values in your dataset
Finding the minimum value helps identify the least significant or baseline values in your dataset.
Using List Comprehension:
min_values = [np.min(row) for row in data]
print(min_values) # Output: [1, 4, 7]
Explanation: This identifies the smallest value in each row, useful for understanding the lower bound of feature ranges.
How to normalize features, centering them around their average
np.mean() - Calculating Mean
The mean is often used to normalize features, centering them around their average.
Using List Comprehension:
mean_values = [np.mean(row) for row in data]
print(mean_values) # Output: [2.0, 5.0, 8.0]
Explanation: Each row's average value represents the central tendency of the features in that data sample.
How to measure of central tendency, especially with skewed data or outlier
The median is a robust measure of central tendency, especially with skewed data or outliers.
Using List Comprehension:
median_values = [np.median(row) for row in data]
print(median_values) # Output: [2.0, 5.0, 8.0]
Explanation: This gives the middle value of each row, providing a measure of centrality less affected by extreme values.
How calculating total counts or feature combinations in ML preprocessing
.
Summing values can be useful for calculating total counts or feature combinations in ML preprocessing.
Using List Comprehension:
sum_values = [np.sum(row) for row in data]
print(sum_values) # Output: [6, 15, 24]
Explanation: The sum provides an aggregate measure of all features for each data sample.
How to spread or variability in the dataset, essential for feature scaling
Using List Comprehension:
std_values = [np.std(row) for row in data]
print(std_values) # Output: [0.816496580927726, 0.816496580927726, 0.816496580927726]
How to Variance quantifies how much the data points deviate from the mean
.
Variance quantifies how much the data points deviate from the mean.
Using List Comprehension:
variance_values = [np.var(row) for row in data]
print(variance_values) # Output: [0.6666666666666666, 0.6666666666666666, 0.6666666666666666]
How to identify which feature is most or least significant in each sample
Using List Comprehension:
argmax_indices = [np.argmax(row) for row in data]
argmin_indices = [np.argmin(row) for row in data]
print(argmax_indices) # Output: [2, 2, 2]
print(argmin_indices) # Output: [0, 0, 0]
Explanation: argmax points to the most significant feature (last column), and argmin to the least (first column) for each row.
How Cumulative sums is helpful for time-series analysis
Using List Comprehension:
cumulative_sums = [np.cumsum(row).tolist() for row in data]
print(cumulative_sums) # Output: [[1, 3, 6], [4, 9, 15], [7, 15, 24]]
.
How percentile is used in outlier detection
ercentiles help understand the distribution, often used in outlier detection.
Using List Comprehension:
percentile_75 = [np.percentile(row, 75) for row in data]
print(percentile_75) # Output: [2.5, 5.5, 8.5]
Explanation: The 75th percentile shows the value below which 75% of the data falls in each row.
Add 10 to each element of array
added_10_2d = [[item + 10 for item in row] for row in array_2d]
# Output: [[13, 18, 11], [14, 17, 15], [19, 12, 16]]
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