When working with data in Python, it is often necessary to calculate various statistical measures such as mean, median, and standard deviation. In this article, we will explore three different ways to add these values as new array columns in Python.

## Method 1: Using NumPy

NumPy is a powerful library for numerical computing in Python. It provides a wide range of mathematical functions, including those for calculating mean, median, and standard deviation. To add these values as new array columns, we can use the `numpy.mean()`

, `numpy.median()`

, and `numpy.std()`

functions.

```
import numpy as np
# Create a sample array
data = np.array([1, 2, 3, 4, 5])
# Calculate mean, median, and standard deviation
mean = np.mean(data)
median = np.median(data)
std_dev = np.std(data)
# Add the calculated values as new array columns
data_with_stats = np.column_stack((data, mean, median, std_dev))
print(data_with_stats)
```

This method is straightforward and efficient. However, it requires the installation of the NumPy library if not already present.

## Method 2: Using pandas

Pandas is a popular library for data manipulation and analysis in Python. It provides a DataFrame object that allows us to store and manipulate tabular data efficiently. To add mean, median, and standard deviation values as new array columns, we can use the `pandas.DataFrame`

class and its built-in functions.

```
import pandas as pd
# Create a sample DataFrame
data = pd.DataFrame({'col1': [1, 2, 3, 4, 5]})
# Calculate mean, median, and standard deviation
mean = data['col1'].mean()
median = data['col1'].median()
std_dev = data['col1'].std()
# Add the calculated values as new columns
data['mean'] = mean
data['median'] = median
data['std_dev'] = std_dev
print(data)
```

This method is particularly useful when working with large datasets and performing complex data manipulations. However, it requires the installation of the pandas library if not already present.

## Method 3: Using built-in functions

If you prefer to avoid external libraries, you can still calculate mean, median, and standard deviation values using built-in functions in Python. However, this method may be less efficient compared to the previous two methods.

```
# Create a sample list
data = [1, 2, 3, 4, 5]
# Calculate mean, median, and standard deviation
mean = sum(data) / len(data)
median = sorted(data)[len(data) // 2]
std_dev = (sum((x - mean) ** 2 for x in data) / len(data)) ** 0.5
# Add the calculated values as new list elements
data_with_stats = data + [mean, median, std_dev]
print(data_with_stats)
```

This method is the most basic and does not require any external libraries. However, it may be slower and less efficient, especially when dealing with large datasets.

In conclusion, the best option depends on your specific requirements and the size of your dataset. If you are working with large datasets and need advanced data manipulation capabilities, using pandas is recommended. If you prefer a lightweight solution or do not want to install additional libraries, using built-in functions can be a viable option. However, if performance is a critical factor, using NumPy is the most efficient choice.

## 8 Responses

Method 2 using pandas is the bomb! So much easier and efficient. Whos with me? 🙌🐼

Method 2 ftw! Pandas makes adding mean, median, and std deviation values a piece of cake. #PythonPower

Method 2 is definitely the way to go, pandas makes everything easier! 🐼

Wow, Method 3 seems so easy! Cant believe I wasted time on Method 1 and 2.

Method 3 FTW! Built-in functions are like the unsung heroes of Python programming. 🙌🐍

I couldnt agree more! Method 3 is a game-changer. Built-in functions truly make Python programming a breeze. They are the secret weapons we all need in our coding arsenal. 🙌🐍

Method 1 seems easier, but Method 3 is quicker. Which one do you prefer? #PythonStats

Method 2 using pandas is the bomb! So much easier and cleaner. #byebyebuiltinfunctions