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(:source lang=python:) import random y = [random.random()*100.0 for i in range(10)] print("Print y") print(y)

print("Sorted List") for i in range(len(y)):

    print("%.2f" % y[i])

def avg(x):

    return sum(x) / len(x)

print("Avg: " + str(avg(y))) print("Max: " + str(max(y))) print("Min: " + str(min(y))) z = sum(1 if i<50.0 else 0 for i in y) print("Number Below 50: " + str(z))

1. another method with NumPy

import numpy as np print(np.mean(y)) print(np.average(y)) print(np.std(y)) print(np.median(y)) (:sourceend:)

(:source lang=python:) def P_RK_IG(V, T, do_ideal_gas=False):

   R = 0.0821  # L-atm/K
   Pc = 37.2   # atm
   Tc = 132.5  # K

   a = 0.427 * pow(R,2) * pow(Tc,2.5) / Pc
   b = 0.0866 * R * Tc / Pc

   # Compute in atm
   P_ig = R * T / V
   P_rk = R * T / (V-b) - a/(V*(V+b)*pow(T,0.5))

   # Convert to Pascals
   if do_ideal_gas:
      return P_ig * 101325
   else:
      return P_rk * 101325

for T in range(490,511,10):

   V = 4.0
   while V < 8:
      print("----- Temperature: " + str(T) + " K")
      print("P_ig: " + str(P_RK_IG(V,T,True)) + " Pa")
      print("P_rk: " + str(P_RK_IG(V,T)) + " Pa")
      V = V + 2.0

(:sourceend:)

The following tutorial is an introduction to built-in Python functions such as average, standard deviation, maximum, minimum, and conditional counting.

Non-Ideal Gas Function Example

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/oX-kpj3fAZQ" frameborder="0" allowfullscreen></iframe> (:htmlend:)

Source Code

 def P_RK_IG(V, T, do_ideal_gas=False):
    R = 0.0821  # L-atm/K
    Pc = 37.2   # atm
    Tc = 132.5  # K

    a = 0.427 * pow(R,2) * pow(Tc,2.5) / Pc
    b = 0.0866 * R * Tc / Pc

    # Compute in atm
    P_ig = R * T / V
    P_rk = R * T / (V-b) - a/(V*(V+b)*pow(T,0.5))

    # Convert to Pascals
    if do_ideal_gas:
       return P_ig * 101325
    else:
       return P_rk * 101325

 for T in range(490,511,10):
    V = 4.0
    while V < 8:
       print("----- Temperature: " + str(T) + " K")
       print("P_ig: " + str(P_RK_IG(V,T,True)) + " Pa")
       print("P_rk: " + str(P_RK_IG(V,T)) + " Pa")
       V = V + 2.0

(:title Python Functions:) (:keywords big data, data analysis, Python, iPython notebook, spreadsheet, average, min, max, stdev, university course:) (:description Introduction to statistical functions in Python, implemented to analyze a data set:)

The following tutorial is an introduction to Python functions such as average, standard deviation, maximum, minimum, and conditional counting.

<iframe width="560" height="315" src="https://www.youtube.com/embed/ofWU-kNrWMk" frameborder="0" allowfullscreen></iframe>

Python Source Code

 import random
 y = [random.random()*100.0 for i in range(10)]
 print("Print y")
 print(y)

 print("Sorted List")
 for i in range(len(y)):
     print("%.2f" % y[i])

 def avg(x):
     return sum(x) / len(x)

 print("Avg: " + str(avg(y)))
 print("Max: " + str(max(y)))
 print("Min: " + str(min(y)))
 z = sum(1 if i<50.0 else 0 for i in y)
 print("Number Below 50: " + str(z))

 # another method with NumPy
 import numpy as np
 print(np.mean(y))
 print(np.average(y))
 print(np.std(y))
 print(np.median(y))

Data statistics can also be completed with a spreadsheet program like Microsoft Excel and MATLAB where the data sets are arrays or matrices instead of tables in a spreadsheet. Click on the appropriate link for additional information and source code.

 % clear session and screen
 clear all; clc
 % create a new column vector with 10 elements
 %  with random numbers between 0 and 100
 y = rand(10,1) * 100;
 disp(y)
 % basic statistics
 mean(y)
 max(y)
 min(y)
 std(y)
 % define a new anonymous function
 avg = @(x) sum(x)/length(x)
 % check new function
 disp('Average with avg function: ')
 avg(y)
 disp('Average with mean function: ')
 mean(y)
 % number of values below 50
 % boolean vector of 0 or 1 values
 disp(y<50)
 % display result
 disp('Number of values below 50: ')
 z = y<50;
 disp(sum(z))

(:title MATLAB Statistical Functions:) (:keywords big data, data analysis, Mathworks, MATLAB, statistics, average, min, max, stdev, university course:) (:description Introduction to statistical functions in MATLAB, implemented to analyze a data set:)

The following tutorial is an introduction to MATLAB functions such as average, standard deviation, maximum, minimum, and conditional counting.

<iframe width="560" height="315" src="https://www.youtube.com/embed/gHWkS2CyScs" frameborder="0" allowfullscreen></iframe>

MATLAB Source Code

Data statistics can also be completed with a spreadsheet program like Microsoft Excel and Python where the data sets are arrays or matrices instead of tables in a spreadsheet. Click on the appropriate link for additional information and source code.

 icount = sum(1 if i<50.0 else 0 for i in y)
 print("Numbers Below 50: " + str(icount))

Python Source Code

 import random
 y = [random.random()*100.0 for i in range(10)]
 print("Print y")
 print(y)

 print("Sorted List")
 for i in range(len(y)):
     print("%.2f" % y[i])

 def avg(x):
     return sum(x) / len(x)

 print("Avg: " + str(avg(y)))
 print("Max: " + str(max(y)))
 print("Min: " + str(min(y)))
 print("Number Below 50: " + str(sum(1 if i<50.0 else 0 for i in y)))

 # another method with NumPy
 import numpy as np
 print(np.mean(y))
 print(np.average(y))
 print(np.std(y))
 print(np.median(y))

Additional Tutorials

Data statistics can also be completed with a spreadsheet program like Microsoft Excel and MATLAB where the data sets are arrays or matrices instead of tables in a spreadsheet. Click on the appropriate link for additional information and source code.

(:title Python Functions:) (:keywords big data, data analysis, Python, iPython notebook, spreadsheet, average, min, max, stdev, university course:) (:description Introduction to statistical functions in Python, implemented to analyze a data set:)

The following tutorial is an introduction to Python functions such as average, standard deviation, maximum, minimum, and conditional counting.

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/ofWU-kNrWMk" frameborder="0" allowfullscreen></iframe> (:htmlend:)

Data statistics can also be completed with a spreadsheet program like Microsoft Excel and Python where the data sets are arrays or matrices instead of tables in a spreadsheet. Click on the appropriate link for additional information and source code.

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