# Calculating-the-Mean-Stack
Let's start with a program to calculate the mean of a set of numbers stored in a Python list.

The mean function in the Python statistics module works like this:

 
from statistics import mean

fluxes = [23.3, 42.1, 2.0, -3.2, 55.6]

m = mean(fluxes)

print(m)

23.96

or we could calculate the mean manually:
 
fluxes = [23.3, 42.1, 2.0, -3.2, 55.6]

m = sum(fluxes)/len(fluxes)

print(m)

using the built-in sum and len functions.

Use the standard library function (e.g. mean) if it exists rather than implementing your own, unless there is a good reason not to.

One good reason (at least temporarily) is learning!

We'll start with an easy question to check everything is working.

Write a function calculate_mean that calculates the mean of a list of numbers. Your function should take a single argument, the list of floats, and return the mean of that list

def calculate_mean(data):

  mean = sum(data)/len(data)
  
  return mean

Python lists are very flexible, but they are slow for big calculations.

NumPy arrays can store purely numerical data in much less space, and are much simpler and faster for calculations.

We can calculate the mean with a NumPy array instead of a list:

 
import numpy as np

fluxes = np.array([23.3, 42.1, 2.0, -3.2, 55.6])

m = np.mean(fluxes)

print(m)

You should get the same answer as you did before. This may not look simpler yet, but it will in the future.

NumPy has a great range of numerical functions. For example, to calculate the size of an array, and the standard deviation:

 
import numpy as np
fluxes = np.array([23.3, 42.1, 2.0, -3.2, 55.6])
print(np.size(fluxes)) # length of array
print(np.std(fluxes))  # standard deviation
The NumPy website has a full list of functions.


#The program loops over each line in the file, splitting the row into a list of values, and appending each row to data:

data = []

for line in open('data.csv'):

  data.append(line.strip().split(','))
  
print(data)

#The strip method removes whitespace (including the newline) from the ends of line. 
#The split method creates a list of strings using the ',' character as the separator between items.
#Now we can store the data in lists, we need to convert each item from a string to a float. We could do this using nested for loops:
data = []
for line in open('data.csv'):
  row = []
  for col in line.strip().split(','):
    row.append(float(col))
  data.append(row)

print(data)
#NumPy has a simpler asarray function to do this conversion:
import numpy as np

data = []
for line in open('data.csv'):
  data.append(line.strip().split(','))

data = np.asarray(data, float)
print(data)
#The NumPy loadtxt function can automatically read a CSV file into a NumPy array, including converting from string to numbers.

#Using our example file from the previous slide:
#Reading and converting to floats becomes a single statement:

 
import numpy as np
data = np.loadtxt('data.csv', delimiter=',')
print(data)
#The NumPy loadtxt function is simpler, faster, and less error-prone than our previous solution. Use it!