Main

## Main.PythonFunctions History

May 03, 2019, at 01:36 PM by 45.56.3.173 -
Changed lines 13-37 from:
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))
to:
(: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))

# 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:
)
Changed lines 50-74 from:
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
to:
(: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:)

Deleted lines 36-37:

!!!!Additional Tutorials
Changed lines 5-6 from:
The following tutorial is an introduction to Python functions such as average, standard deviation, maximum, minimum, and conditional counting.
to:
The following tutorial is an introduction to built-in Python functions such as average, standard deviation, maximum, minimum, and conditional counting.
Added lines 42-75:
!!!! 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

Changed lines 1-6 from:
(: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.
to:
(: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.
Changed line 8 from:
<iframe width="560" height="315" src="https://www.youtube.com/embed/gHWkS2CyScs" frameborder="0" allowfullscreen></iframe>
to:
<iframe width="560" height="315" src="https://www.youtube.com/embed/ofWU-kNrWMk" frameborder="0" allowfullscreen></iframe>
Changed lines 11-38 from:
!!!!MATLAB 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
))
to:
!!!!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
))
Changed line 40 from:
Data statistics can also be completed with a spreadsheet program  like [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/PythonFunctions|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.
to:
Data statistics can also be completed with a spreadsheet program  like [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/MatlabFunctions|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.
Added lines 13-37:
% 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))
Changed lines 1-6 from:
(: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.
to:
(: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.
Changed line 8 from:
<iframe width="560" height="315" src="https://www.youtube.com/embed/ofWU-kNrWMk" frameborder="0" allowfullscreen></iframe>
to:
<iframe width="560" height="315" src="https://www.youtube.com/embed/gHWkS2CyScs" frameborder="0" allowfullscreen></iframe>
Changed lines 11-37 from:
!!!!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)))
icount = sum(1 if i<50.0 else 0 for i in y)
print("Numbers Below 50: " + str(icount))

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

to:
!!!!MATLAB Source Code

Changed line 16 from:
Data statistics can also be completed with a spreadsheet program  like [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/MatlabFunctions|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.
to:
Data statistics can also be completed with a spreadsheet program  like [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/PythonFunctions|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.
Changed lines 28-29 from:
print("Number Below 50: " + str(sum(1 if i<50.0 else 0 for i in y)))
to:
icount = sum(1 if i<50.0 else 0 for i in y)
print("Numbers Below 50: " + str(icount
))
Added lines 10-37:

!!!!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
Changed line 11 from:
Data statistics can also be completed with a spreadsheet program  like [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/PythonFunctions|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.
to:
Data statistics can also be completed with a spreadsheet program  like [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/MatlabFunctions|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.
Added lines 1-30:
(: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 [[Main/ExcelFunctions|Microsoft Excel]] and [[Main/PythonFunctions|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.

----

(:html:)
<div id="disqus_thread"></div>
<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
var disqus_shortname = 'apmonitor'; // required: replace example with your forum shortname

/* * * DON'T EDIT BELOW THIS LINE * * */
(function() {
var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
dsq.src = 'https://' + disqus_shortname + '.disqus.com/embed.js';
(document.getElementsByTagName('head') || document.getElementsByTagName('body')).appendChild(dsq);
})();
</script>
<noscript>Please enable JavaScript to view the <a href="https://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>
<a href="https://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>
(:htmlend:)