**π‘ Problem Formulation:** Given a dataset, often in the form of a list of lists or a 2D array, we need to find the row where the difference between the highest (maximum) and lowest (minimum) values is the smallest. This can be crucial for pinpointing uniform distributions of data or detecting anomalies. For example, we might be given input like `[[3, 10, 1], [4, 5, 6], [8, 9, 7]]`

, and we aim to identify the row with the smallest range, which in this case, is the second row with values [4, 5, 6] and output the row or the minimum difference.

## Method 1: Using a Custom Function

The first method involves creating a custom function that iterates through each row of the dataset, calculates the difference between the maximum and minimum values, and then returns the row with the smallest difference. The function `find_min_diff_row()`

is a complete and self-contained solution for this problem.

Here’s an example:

data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = sorted(data, key=lambda row: max(row) - min(row))[0] print(min_diff_row)

Output:

[4, 5, 6]

This succinct code sorts the dataset based on the range of values in each row and then selects the first element from the sorted list as the row with the smallest difference. It’s a smart use of Python’s powerful sort capabilities and is also quite readable.

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = min(data, key=lambda row: max(row) - min(row)) print(min_diff_row)

Output:

[4, 5, 6]

This one-liner creates a lambda function that computes the max-min range for each row within a list comprehension structure. The `min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

This method is another one-liner that leverages the sorted function. While similar to Method 4, it relies on sorting to achieve the result, which might offer better performance with certain data structures or preferences in terms of code style.

Here’s an example:

data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = sorted(data, key=lambda row: max(row) - min(row))[0] print(min_diff_row)

Output:

[4, 5, 6]

This succinct code sorts the dataset based on the range of values in each row and then selects the first element from the sorted list as the row with the smallest difference. It’s a smart use of Python’s powerful sort capabilities and is also quite readable.

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

import pandas as pd def find_min_diff_row_pd(data): df = pd.DataFrame(data) return df.loc[(df.max(axis=1) - df.min(axis=1)).idxmin()] data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row_pd(data) print(min_diff_row)

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

In this example, we convert the input data into a Pandas DataFrame and then calculate the range for each row. Using `idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

This Pythonic way uses list comprehension combined with the `min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = min(data, key=lambda row: max(row) - min(row)) print(min_diff_row)

Output:

[4, 5, 6]

This one-liner creates a lambda function that computes the max-min range for each row within a list comprehension structure. The `min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

This method is another one-liner that leverages the sorted function. While similar to Method 4, it relies on sorting to achieve the result, which might offer better performance with certain data structures or preferences in terms of code style.

Here’s an example:

data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = sorted(data, key=lambda row: max(row) - min(row))[0] print(min_diff_row)

Output:

[4, 5, 6]

This succinct code sorts the dataset based on the range of values in each row and then selects the first element from the sorted list as the row with the smallest difference. It’s a smart use of Python’s powerful sort capabilities and is also quite readable.

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

import numpy as np def find_min_diff_row_np(data): data_np = np.array(data) return data_np[np.argmin(np.ptp(data_np, axis=1))] data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row_np(data) print(min_diff_row)

Output:

[4 5 6]

This snippet converts the list of lists into a NumPy array and then uses `np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

Pandas provides high-level data structures and operates seamlessly with datasets. With Pandas, one can perform this operation using the DataFrame abstraction and built-in methods like `idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

import pandas as pd def find_min_diff_row_pd(data): df = pd.DataFrame(data) return df.loc[(df.max(axis=1) - df.min(axis=1)).idxmin()] data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row_pd(data) print(min_diff_row)

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

In this example, we convert the input data into a Pandas DataFrame and then calculate the range for each row. Using `idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

This Pythonic way uses list comprehension combined with the `min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = min(data, key=lambda row: max(row) - min(row)) print(min_diff_row)

Output:

[4, 5, 6]

This one-liner creates a lambda function that computes the max-min range for each row within a list comprehension structure. The `min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

This method is another one-liner that leverages the sorted function. While similar to Method 4, it relies on sorting to achieve the result, which might offer better performance with certain data structures or preferences in terms of code style.

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

def find_min_diff_row(data): return min(data, key=lambda x: max(x) - min(x)) data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row(data) print(min_diff_row)

Output:

[4, 5, 6]

This code snippet defines the `find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

NumPy is a powerful library for numerical computations. In this method, we use NumPy’s array functionality and vectorized operations to find the row with the minimum difference efficiently. The function `np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

import numpy as np def find_min_diff_row_np(data): data_np = np.array(data) return data_np[np.argmin(np.ptp(data_np, axis=1))] data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row_np(data) print(min_diff_row)

Output:

[4 5 6]

This snippet converts the list of lists into a NumPy array and then uses `np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

Pandas provides high-level data structures and operates seamlessly with datasets. With Pandas, one can perform this operation using the DataFrame abstraction and built-in methods like `idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

import pandas as pd def find_min_diff_row_pd(data): df = pd.DataFrame(data) return df.loc[(df.max(axis=1) - df.min(axis=1)).idxmin()] data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row_pd(data) print(min_diff_row)

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

In this example, we convert the input data into a Pandas DataFrame and then calculate the range for each row. Using `idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

This Pythonic way uses list comprehension combined with the `min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

def find_min_diff_row(data): return min(data, key=lambda x: max(x) - min(x)) data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row(data) print(min_diff_row)

Output:

[4, 5, 6]

This code snippet defines the `find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

NumPy is a powerful library for numerical computations. In this method, we use NumPy’s array functionality and vectorized operations to find the row with the minimum difference efficiently. The function `np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

import numpy as np def find_min_diff_row_np(data): data_np = np.array(data) return data_np[np.argmin(np.ptp(data_np, axis=1))] data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row_np(data) print(min_diff_row)

Output:

[4 5 6]

This snippet converts the list of lists into a NumPy array and then uses `np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

Pandas provides high-level data structures and operates seamlessly with datasets. With Pandas, one can perform this operation using the DataFrame abstraction and built-in methods like `idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

def find_min_diff_row(data): return min(data, key=lambda x: max(x) - min(x)) data = [[3, 10, 1], [4, 5, 6], [8, 9, 7]] min_diff_row = find_min_diff_row(data) print(min_diff_row)

Output:

[4, 5, 6]

This code snippet defines the `find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

NumPy is a powerful library for numerical computations. In this method, we use NumPy’s array functionality and vectorized operations to find the row with the minimum difference efficiently. The function `np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

`np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

`np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

`np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.

Output:

[4, 5, 6]

`find_min_diff_row`

function, which takes a dataset as input. It utilizes the built-in `min()`

function with a `key`

parameter that applies a lambda function to each row to compute the difference between the maximum and minimum values. It then returns the row with the smallest difference.

## Method 2: Using NumPy

`np.ptp()`

is used, which returns the range (max – min) of values along an axis.

Here’s an example:

Output:

[4 5 6]

`np.ptp()`

to calculate the range for each row. The index of the row with the smallest range is found using `np.argmin()`

, and the corresponding row is returned. The vectorized nature of NumPy makes this method efficient and concise.

## Method 3: Using Pandas

`idxmin()`

. This method is particularly suited for large datasets and CSV files.

Here’s an example:

Output:

0 4 1 5 2 6 Name: 1, dtype: int64

`idxmin()`

, we get the index of the row with the smallest range. Finally, we use `loc[]`

to access and print the row from the DataFrame.

## Method 4: Using List Comprehension and Min Function

`min()`

function. It’s a one-liner that directly computes the row with the minimum difference without creating a separate function or importing libraries.

Here’s an example:

Output:

[4, 5, 6]

`min()`

function then returns the row with the smallest range. It’s quick and easy to read, but might be less efficient for larger datasets.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted Function

Here’s an example:

Output:

[4, 5, 6]

## Summary/Discussion

**Method 1:**Custom Function. Strengths: Easy to understand; customizable. Weaknesses: Potentially less efficient with large datasets.**Method 2:**Using NumPy. Strengths: Fast and efficient for large datasets; vectorized operations. Weaknesses: Requires an external library.**Method 3:**Using Pandas. Strengths: High-level operations; great for CSV or database interaction. Weaknesses: Overhead for small tasks; external library required.**Method 4:**List Comprehension and Min Function. Strengths: Pythonic and concise. Weaknesses: Not as efficient for large datasets.**Method 5:**List Comprehension and Sorted Function. Strengths: Readable one-liner; utilizes sorting efficiency. Weaknesses: Sorting can be less efficient for very large datasets.