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‎bin/concept-of-the-week.txt‎

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content/kotlin/concepts/classes/classes.md
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content/uiux/concepts/quantitative-research/quantitative-research.md

‎content/numpy/concepts/ndarray/terms/byteswap/byteswap.md‎

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---
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Title: 'byteswap()'
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Description: 'Swaps the byte order of each element in a NumPy ndarray.'
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Subjects:
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- 'Computer Science'
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- 'Data Science'
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Tags:
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- 'Array'
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- 'Data'
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- 'NumPy'
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CatalogContent:
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- 'learn-python-3'
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- 'paths/data-science'
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---
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The **`byteswap()`** method reverses the byte order of every element in a NumPy array. This is used when converting data between systems with different endianness (byte-ordering conventions). The operation either returns a new array or modifies the original one in place when `inplace=True`.
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## Syntax
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```pseudo
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ndarray.byteswap(inplace=False)
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```
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**Parameters:**
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- `inplace` (optional): When set to `True`, the byte order of the existing array is swapped in place. When `False`, a new array with swapped bytes is returned.
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**Return value:**
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Returns a new ndarray with swapped byte order, unless `inplace=True`, in which case the original array is modified and returned.
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## Example 1
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In this example, the array's byte order is swapped to convert the data into the opposite endianness:
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```py
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import numpy as np
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arr = np.array([1, 256, 1024], dtype=np.int32)
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print("Original array:")
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print(arr)
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print("Original dtype:", arr.dtype)
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swapped = arr.byteswap()
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print("\nAfter byteswap():")
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print(swapped)
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print("Swapped dtype:", swapped.dtype)
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```
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The output of this code is:
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```shell
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Original array:
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[ 1 256 1024]
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Original dtype: int32
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After byteswap():
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[16777216 65536 262144]
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Swapped dtype: int32
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```
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## Example 2
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This example demonstrates `byteswap(inplace=True)` and shows how the original data is altered directly:
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```py
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import numpy as np
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arr = np.array([100, 200, 300], dtype=np.int32)
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print("Before inplace byteswap:", arr)
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arr.byteswap(inplace=True)
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print("After inplace byteswap:", arr)
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```
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The output of this code is:
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```shell
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Before inplace byteswap: [100 200 300]
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After inplace byteswap: [1677721600 -939524096 738263040]
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```
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## Codebyte Example
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Use the codebyte below to inspect how `byteswap()` affects a 2-D array and observe the internal memory representation change:
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```codebyte/python
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import numpy as np
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matrix = np.array([[1, 2], [3, 4]], dtype=np.int16)
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print("Original:")
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print(matrix)
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swapped = matrix.byteswap()
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print("\nByteswapped:")
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print(swapped)
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```
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## Frequently Asked Questions
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### 1. What is the function of byteswap() in Python?
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The `byteswap()` method reverses the byte order of every element in a NumPy array. It is commonly used when preparing data for systems with different endianness or when interpreting binary data from external sources.
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### 2. What are bytes and bytearrays in Python?
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A `bytes` object in Python is an immutable sequence of byte values, while a bytearray is a mutable version of the same concept. Both store raw binary data and are often used for file handling, networking, and low-level memory operations.
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### 3. How to shuffle a NumPy ndarray?
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A NumPy array can be shuffled using `np.random.shuffle()` for in-place row-wise shuffling or `np.random.permutation()` to return a shuffled copy. These functions randomize the order of elements while preserving the array’s structure.

‎content/pandas/concepts/groupby/terms/size/size.md‎

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---
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Title: 'size()'
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Description: 'Returns a Series containing the size (row count) of each group.'
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Subjects:
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- 'Computer Science'
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- 'Data Science'
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Tags:
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- 'Data Structures'
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- 'Pandas'
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CatalogContent:
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- 'learn-python-3'
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- 'paths/data-science'
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---
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The **`size()`** method in pandas returns the number of rows or elements in each group created by the `groupby()` [function](https://www.codecademy.com/resources/docs/pandas/built-in-functions). It provides a quick way to determine group sizes without applying an aggregation function.
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## Syntax
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```pseudo
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DataFrameGroupBy.size()
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```
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**Parameters:**
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The `size()` method doesn't take any parameters.
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**Return value:**
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The `size()` method returns a Series containing the size (row count) of each group created by `groupby()`.
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## Example 1: Counting Rows by Group
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In this example, a [DataFrame](https://www.codecademy.com/resources/docs/pandas/dataframe) of employees is grouped by their department, and `size()` counts how many employees belong to each department:
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```py
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import pandas as pd
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data = {
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'Department': ['HR', 'IT', 'HR', 'Finance', 'IT', 'Finance'],
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'Employee': ['John', 'Sara', 'Mike', 'Anna', 'Tom', 'Chris']
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}
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df = pd.DataFrame(data)
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group_sizes = df.groupby('Department').size()
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print(group_sizes)
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```
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The output of this code is:
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```shell
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Department
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Finance 2
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HR 2
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IT 2
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dtype: int64
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```
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## Example 2: Using Multiple Grouping Columns
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In this example, `size()` counts the number of members in each combination of team and shift within a dataset:
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```py
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import pandas as pd
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data = {
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'Team': ['A', 'A', 'B', 'B', 'B', 'C'],
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'Shift': ['Day', 'Night', 'Day', 'Night', 'Day', 'Day'],
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'Name': ['John', 'Sara', 'Mike', 'Anna', 'Tom', 'Chris']
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}
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df = pd.DataFrame(data)
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group_sizes = df.groupby(['Team', 'Shift']).size()
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print(group_sizes)
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```
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The output of this code is:
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```shell
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Team Shift
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A Day 1
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Night 1
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B Day 2
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Night 1
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C Day 1
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dtype: int64
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```
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## Codebyte Example: Counting Transactions Per Product
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In this example, `size()` is used to count how many sales transactions occurred for each product in a store dataset:
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```codebyte/python
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import pandas as pd
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sales = pd.DataFrame({
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'Product': ['Apple', 'Banana', 'Apple', 'Orange', 'Banana', 'Banana', 'Apple'],
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'Customer': ['A', 'B', 'C', 'A', 'D', 'E', 'F']
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})
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counts = sales.groupby('Product').size()
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print(counts)
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```
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## Frequently Asked Questions
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### 1. What is the pandas `groupby().size()` method?
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`groupby().size()` returns the number of rows in each group created by `groupby()`.
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### 2. What is the purpose of `groupby()` in pandas?
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`groupby()` splits data into groups based on selected column values to enable aggregation and summarization.
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### 3. What does `NaN` stand for in pandas?
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`NaN` stands for Not a Number and indicates missing or undefined data.

‎content/pytorch/concepts/tensor-operations/terms/log2/log2.md‎

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---
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Title: '.log2()'
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Description: 'Computes the base-2 logarithm of each element in the input tensor and returns a new tensor with the results.'
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Subjects:
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- 'Code Foundations'
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- 'Computer Science'
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Tags:
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- 'Elements'
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- 'Methods'
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- 'PyTorch'
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- 'Tensors'
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CatalogContent:
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- 'learn-python-3'
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- 'paths/data-science'
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---
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The **`.log2()`** method in PyTorch returns a new [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) by computing the logarithm base 2 of each element in the input tensor. This operation is useful in numerous data-science and machine-learning workflows where values are interpreted on a log scale (e.g., information theory, binary magnitude comparisons).
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## Syntax
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```pseudo
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torch.log2(input, *, out=None) → Tensor
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```
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**Parameters:**
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- `input` (Tensor): The tensor whose elements are to be transformed by base-2 logarithm.
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- `out` (Tensor, optional): A tensor to store the output; must have the same shape as input if provided.
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**Return value:**
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Returns a new tensor of the same shape as `input` where each element is `log₂(input[i])`.
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## Example 1: Basic Usage of `.log2()`
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In this example, the base-2 logarithm is computed for a tensor containing powers of 2:
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```py
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import torch
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# Define a tensor
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input_tensor = torch.tensor([2.0, 4.0, 8.0, 16.0, 32.0])
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# Compute base-2 logarithm
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output_tensor = torch.log2(input_tensor)
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print(output_tensor)
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```
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The output of this code is:
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```shell
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tensor([1., 2., 3., 4., 5.])
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```
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## Example 2: Applying `.log2()` on Random Values
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In this example, a tensor with random positive values is transformed using base-2 logarithm to analyze data on a log scale:
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```py
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import torch
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# Generate a tensor of random positive values
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data = torch.rand(5) * 10 + 1
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# Apply log2 transformation
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log_tensor = torch.log2(data)
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print(data)
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print(log_tensor)
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```
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The output of this code is:
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```shell
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tensor([10.5500, 9.2777, 10.9371, 1.3551, 5.2609])
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tensor([3.3992, 3.2138, 3.4512, 0.4384, 2.3953])
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```

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