# Program to calculate moving average 
arr = [1, 2, 3, 7, 9] 
window_size = 3

i = 0
# Initialize an empty list to store moving averages 
moving_averages = [] 

# Loop through the array to consider 
# every window of size 3 
while i < len(arr) - window_size + 1: 
	
	# Store elements from i to i+window_size 
	# in list to get the current window 
	window = arr[i : i + window_size] 

	# Calculate the average of current window 
	window_average = round(sum(window) / window_size, 2) 
	
	# Store the average of current 
	# window in moving average list 
	moving_averages.append(window_average) 
	
	# Shift window to right by one position 
	i += 1

print(moving_averages)

[2.0, 4.0, 6.33]

# Program to calculate moving average using numpy 

import numpy as np 

arr = [1, 2, 3, 7, 9] 
window_size = 3

i = 0
# Initialize an empty list to store moving averages 
moving_averages = [] 

# Loop through the array t o 
#consider every window of size 3 
while i < len(arr) - window_size + 1: 

	# Calculate the average of current window 
	window_average = round(np.sum(arr[ 
	i:i+window_size]) / window_size, 2) 
	
	# Store the average of current 
	# window in moving average list 
	moving_averages.append(window_average) 
	
	# Shift window to right by one position 
	i += 1

print(moving_averages)

[2.0, 4.0, 6.33]

# Python program to calculate 
# simple moving averages using pandas 
import pandas as pd 

arr = [1, 2, 3, 7, 9] 
window_size = 3

# Convert array of integers to pandas series 
numbers_series = pd.Series(arr) 

# Get the window of series 
# of observations of specified window size 
windows = numbers_series.rolling(window_size) 

# Create a series of moving 
# averages of each window 
moving_averages = windows.mean() 

# Convert pandas series back to list 
moving_averages_list = moving_averages.tolist() 

# Remove null entries from the list 
final_list = moving_averages_list[window_size - 1:] 

print(final_list) 

[2.0, 4.0, 6.33]

# Program to calculate cumulative moving average 
# using numpy 

import numpy as np 

arr = [1, 2, 3, 7, 9] 

i = 1
# Initialize an empty list to store cumulative moving 
# averages 
moving_averages = [] 

# Store cumulative sums of array in cum_sum array 
cum_sum = np.cumsum(arr); 

# Loop through the array elements 
while i <= len(arr): 

	# Calculate the cumulative average by dividing 
	# cumulative sum by number of elements till 
	# that position 
	window_average = round(cum_sum[i-1] / i, 2) 
	
	# Store the cumulative average of 
	# current window in moving average list 
	moving_averages.append(window_average) 
	
	# Shift window to right by one position 
	i += 1

print(moving_averages)

[1.0, 1.5, 2.0, 3.25, 4.4]

# Python program to calculate 
# cumulative moving averages using pandas 
import pandas as pd 

arr = [1, 2, 3, 7, 9] 
window_size = 3

# Convert array of integers to pandas series 
numbers_series = pd.Series(arr) 

# Get the window of series of 
# observations till the current time 
windows = numbers_series.expanding() 

# Create a series of moving averages of each window 
moving_averages = windows.mean() 

# Convert pandas series back to list 
moving_averages_list = moving_averages.tolist() 

print(moving_averages_list) 

[1.0, 1.5, 2.0, 3.25, 4.4]

# Program to calculate exponential 
# moving average using formula 

import numpy as np 

arr = [1, 2, 3, 7, 9] 
x=0.5 # smoothening factor 

i = 1
# Initialize an empty list to 
# store exponential moving averages 
moving_averages = [] 

# Insert first exponential average in the list 
moving_averages.append(arr[0]) 

# Loop through the array elements 
while i < len(arr): 

	# Calculate the exponential 
	# average by using the formula 
	window_average = round((x*arr[i])+
						(1-x)*moving_averages[-1], 2) 
	
	# Store the cumulative average 
	# of current window in moving average list 
	moving_averages.append(window_average) 
	
	# Shift window to right by one position 
	i += 1

print(moving_averages)

[1, 1.5, 2.25, 4.62, 6.81]

# Python program to 
# calculate exponential moving averages 
import pandas as pd 

arr = [1, 2, 3, 7, 9] 

# Convert array of integers to pandas series 
numbers_series = pd.Series(arr) 

# Get the moving averages of series 
# of observations till the current time 
moving_averages = round(numbers_series.ewm( 
alpha=0.5, adjust=False).mean(), 2) 

# Convert pandas series back to list 
moving_averages_list = moving_averages.tolist() 

print(moving_averages_list) 

[1.0, 1.5, 2.25, 4.62, 6.81]