# 5 Best Ways to Calculate the Mean of an Array After Removing Some Elements in Python

π‘ Problem Formulation: A common issue in data analysis is the need to calculate the statistical mean of an array after excluding certain values. For instance, you may have an array of integers and want to remove the top and bottom 10% before calculating the mean to mitigate outlier influence. Consider an input array [1, 2, 3, 4, 100] and the desired output after removing the highest value (100) is a mean of 2.5.

## Method 1: Basic Iteration and Filtering

This method involves iterating over the array to filter out the elements not needed in the mean calculation, possibly based on a condition or threshold, and then finding the mean of the remaining elements. It’s intuitive and straightforward, making it easy for beginners to understand.

Here’s an example:

```def mean_of_filtered_array(array, filter_func):
filtered_array = [x for x in array if filter_func(x)]
return sum(filtered_array) / len(filtered_array)

# Example usage
arr = [1, 2, 3, 4, 100]
filtered_mean = mean_of_filtered_array(arr, lambda x: x < 50)
print(filtered_mean)
```

Output: 2.5

This code defines a function `mean_of_filtered_array()` that computes the mean of an array after applying a filter function to it. The given example filters out elements greater than 50 and then calculates the mean of the remaining array elements.

## Method 2: Using numpy library

NumPy is a powerful library for numerical computations in Python. This method is to use NumPy’s built-in features to remove elements, making the code more concise and performant for large arrays.

Here’s an example:

```import numpy as np

arr = np.array([1, 2, 3, 4, 100])
filtered_arr = arr[arr < 50]
mean = np.mean(filtered_arr)
print(mean)
```

Output: 2.5

The snippet employs NumPy’s array indexing to filter out elements and then uses the `np.mean()` function to calculate the mean. The result is efficient execution, especially beneficial for larger arrays.

## Method 3: Using pandas library

pandas is another library that shines in data manipulation. It offers robust tools to filter and calculate statistics on datasets, including arrays.

Here’s an example:

```import pandas as pd

arr = pd.Series([1, 2, 3, 4, 100])
filtered_arr = arr[arr < 50]
mean = filtered_arr.mean()
print(mean)
```

Output: 2.5

The code makes use of the pandas Series data structure to store the array. It filters out unwanted elements and calculates the mean using the `mean()` method built into pandas data structures, which is particularly useful when dealing with mixed data types and missing values.

## Method 4: Statistics Module

Python’s built-in `statistics` module can be used to calculate the mean after constructing a new list that omits the unwanted elements.

Here’s an example:

```import statistics

arr = [1, 2, 3, 4, 100]
filtered_arr = [x for x in arr if x < 50]
mean = statistics.mean(filtered_arr)
print(mean)
```

Output: 2.5

In this method, a list comprehension creates a filtered array excluding elements based on a condition, and the `statistics.mean()` function then computes the mean, which is very readable but less efficient than numpy for larger datasets.

## Bonus One-Liner Method 5: List Comprehension with sum() and len()

A python one-liner can also solve the problem using list comprehension, `sum()`, and `len()` functions. This method showcases the power of python’s succinct syntax for small scale computations.

Here’s an example:

```arr = [1, 2, 3, 4, 100]
mean = sum(x for x in arr if x < 50) / len([x for x in arr if x < 50])
print(mean)
```

Output: 2.5

This concise snippet filters the array and calculates the mean in a single line. It’s elegant, however, the list is traversed twice: once for `sum()` and once for `len()`, which can be inefficient for large arrays.

## Summary/Discussion

• Method 1: Basic Iteration. Simple and clear. Not always the most efficient for large arrays.
• Method 2: Using numpy. Quick and effective, especially on large datasets. Requires numpy installation.
• Method 3: Using pandas. Great for complex data manipulation. Overhead can be overkill for simple tasks.
• Method 4: Statistics Module. Built-in and readable. Performance may suffer with large data.
• Method 5: One-liner List Comprehension. Concise for small tasks. Inefficient for large arrays due to double traversal.