best of 12 out of 14 in python - python

I have a list of 14 numbers in a list
list=[0.0, 2.0, 2.0, 2.0, 2.0, 1.5, 1.0, 1.0, 1.0, 1.0, 0.5, 1.5, 1.0, 2.0]
I have to sum the best of 12 among the list
New to code have no idea.


l=[0.0, 2.0, 2.0, 2.0, 2.0, 1.5, 1.0, 1.0, 1.0, 1.0, 0.5, 1.5, 1.0, 2.0]
l.sort()
l=l[0:12]
print(l)
total=0
for element in range(0,len(l)):
total=total+l[element]
print(total)
Hope this help! but try to learn this language by yourself, you will surely be enjoying python:)

Related

How to find the parameters of a sample of data (Binomial distribution)

If I have a sample of data containing 10 000 points that I know i binomially distributed, what is the best way I can plot the pmf of it? i.e I need to find what the parameters n and p are but I don't really know what is the easiest way to do that!
Is there any easy way to accomplish this? Here is what I'm getting when trying to "guess" the parameters
Using the following code
def binom_dist(data_list):
pmf_guess = sps.binom.pmf(data_list , 35 , 0.3)
pmf = sps.binom.pmf(data_list, max(data_list) , np.std(data_list)/max(data_list))
return pmf_guess , pmf
def plot_histo(data_list, bin_count , pmf, pmf_guess):
plt.hist(data_list, bins=bin_count)
plt.plot(data_list, pmf , color = 'red')
plt.plot(data_list , pmf_guess, color = 'green')
return plt.show()
pmf_guess , pmf = binom_dist(data_3)
plot_3 = plot_histo(data_3, 100 , pmf , pmf_guess)
Here is a shortened version of the data
data_3 =
[1.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
3.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
4.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0]

How to use an index of a dataframe to assign values to a row of a new column?

I have a dataset that consists of ID (participant), run, indexnumber (that is, an index number of a slalom turn) and performance (that could be velocity or time). In addition, I have information for each id and run where in the slalom turn (that is, the index) they actually start to turn.
My goal is to create a new column in the dataframe that contain 0 if the id has not started to turn and 1 if they have started to turn. This column could be called phase.
For example:
For ID1 the point where this skier starts to turn i index 4 for the first run and 9 for the second run. Therefore, I want all rows in the new column to contain 0s until index nr 4 and 1s thereafter (for the first run). For the second run I want all rows to contain 0s until index nr 9 and 1 thereafter.
Is there a simple way to do this with pandas or vanilla python?
example = [[1.0, 1.0, 1.0, 0.6912982024915187],
[1.0, 1.0, 2.0, 0.16453900411106737],
[1.0, 1.0, 3.0, 0.11362801727310845],
[1.0, 1.0, 4.0, 0.587778444335624],
[1.0, 1.0, 5.0, 0.8455388913351765],
[1.0, 1.0, 6.0, 0.5719366584505648],
[1.0, 1.0, 7.0, 0.4665520044952449],
[1.0, 1.0, 8.0, 0.9105152709573275],
[1.0, 1.0, 9.0, 0.4600099001744885],
[1.0, 1.0, 10.0, 0.8577060884077763],
[1.0, 2.0, 1.0, 0.11550722410813963],
[1.0, 2.0, 2.0, 0.5729090378222077],
[1.0, 2.0, 3.0, 0.43990164344919824],
[1.0, 2.0, 4.0, 0.595242293948498],
[1.0, 2.0, 5.0, 0.443684017624451],
[1.0, 2.0, 6.0, 0.3608135854303052],
[1.0, 2.0, 7.0, 0.28525404982906766],
[1.0, 2.0, 8.0, 0.11561422303194391],
[1.0, 2.0, 9.0, 0.8579134051748011],
[1.0, 2.0, 10.0, 0.540598113345226],
[2.0, 1.0, 1.0, 0.4058570295736075],
[2.0, 1.0, 2.0, 0.9422426000325298],
[2.0, 1.0, 3.0, 0.7918655742964762],
[2.0, 1.0, 4.0, 0.4145753321336241],
[2.0, 1.0, 5.0, 0.5256388261997529],
[2.0, 1.0, 6.0, 0.8140335187050629],
[2.0, 1.0, 7.0, 0.12134416740848841],
[2.0, 1.0, 8.0, 0.9016748379372173],
[2.0, 1.0, 9.0, 0.462241316800442],
[2.0, 1.0, 10.0, 0.7839715857746699],
[2.0, 2.0, 1.0, 0.5300527244824904],
[2.0, 2.0, 2.0, 0.8784844676567194],
[2.0, 2.0, 3.0, 0.14395673182343738],
[2.0, 2.0, 4.0, 0.7606405990262495],
[2.0, 2.0, 5.0, 0.5123048342846208],
[2.0, 2.0, 6.0, 0.25608277502943655],
[2.0, 2.0, 7.0, 0.4264542956426933],
[2.0, 2.0, 8.0, 0.9144976708651866],
[2.0, 2.0, 9.0, 0.875888479621729],
[2.0, 2.0, 10.0, 0.3428732760552141]]
turnPhaseId1 = [4,9] #the index number when ID1 starts to turn in run 1 and run 2, respectively
turnPhaseId2 = [2,5] #the index number when ID2 starts to turn in run 1 and run 2, respectively
pd.DataFrame(example, columns=['id', 'run', 'index', 'performance'])

I believe it is a better idea to turnPhase into a dictionary, and then use apply:
turn_dict = {1: [4, 9],
2: [2, 5]}
We also need to change the column types as we need to reach dictionary keys, and list indexes, which are int:
df['id'] = df['id'].astype(int)
df['index'] = df['index'].astype(int)
Finally, apply:
df['new_column'] = df.apply(lambda x: 0 if x['index'] < turn_dict[x['id']][int(x['run'] -1)] else 1 , axis=1)

Setting color for specific value in Matplotlib imshow

Suppose that i'm plotting the following numpy array of data on a simple matplotlib heatmap using imshow; there are some cases where the value will be 0.0. Is there any way to set a specif color for the cell where that value will be shown? For example, when the value is 0, the color for that cell must be black
a = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
[2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
[1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
[0.6, 0.0, 0.3, 0.0, 0, 0.0, 0.0],
[0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
[0, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
[0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])
Map = ax.imshow(a, interpolation='none', cmap='coolwarm')

Maybe not the perfect solution, but definitely a simple one. If it is only for the purpose of creating an image you can modify the original data (or a copy of it) and replace 0.0 with NaN. Then you can use set_bad to get the desired output.
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
a = np.array([[0.8, 2.4, -2.5, 3.9, 0.0, 4.0, 0.0],
[2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
[-1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
[0.6, 0.0, 0.3, 0.0, 0, 0.0, 0.0],
[0.7, 1.7, -0.6, 2.6, 2.2, 6.2, 0.0],
[0, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
[0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])
c_map = cm.get_cmap('rainbow')
c_map.set_bad('k')
b = a.copy()
b[b==0] = np.nan
fig = plt.imshow(b, interpolation='none', cmap=c_map)
plt.colorbar(fig)

Calculating mean of list within dictionary in Python

This is a part of my output. I want to find the average of the list within this dictionary:
{'Radial Velocity': {'number': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 3.0, 3.0, 3.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 2.0, 1.0, 3.0, 3.0, 3.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 1.0, 3.0, 3.0, 3.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}}

Here's how to solve it using custom code:
sumOfNumbers = 0
for number in dictionary['Radial Velocity']['number']:
sumOfNumbers += number
avg = sumOfNumbers / len(dictionary['Radial Velocity']['number'])
print(avg)

You can use the function mean() from numpy:
import numpy as np
output = {'Radial Velocity': {'number': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 3.0, 3.0, 3.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 2.0, 1.0, 3.0, 3.0, 3.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 1.0, 3.0, 3.0, 3.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}}
print(np.mean(output['Radial Velocity']['number']))
Output:
2.1607142857142856

Python has a statistics module in its standard library. It has, among other useful things, a mean() function to which you can pass a list and get the average:
from statistics import mean
mean(d['Radial Velocity']['number'])

How to plot large range values with matplotlib?

I have to run soak tests for longer duration and capture 3 datasets (before the run, in-between the run, after the run), plot them and manually analyze the plots.
All the datasets span across the very large range (0-10^5). So, when I am plotting this data using matplotlib's bar function, the bar for smaller values is too small to be analyzed.
import matplotlib
matplotlib.use('Agg')
import sys,os,argparse,json,string,numpy
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
bx = ('smmpg_b1024k', 'smmpg_b10k', 'smmpg_b11k', 'smmpg_b128', 'smmpg_b128k', 'smmpg_b12k', 'smmpg_b13k', 'smmpg_b14k', 'smmpg_b15k', 'smmpg_b160', 'smmpg_b16k', 'smmpg_b17k', 'smmpg_b18k', 'smmpg_b192', 'smmpg_b192k', 'smmpg_b19k', 'smmpg_b1k', 'smmpg_b20k', 'smmpg_b21k', 'smmpg_b224', 'smmpg_b22k', 'smmpg_b23k', 'smmpg_b24k', 'smmpg_b256', 'smmpg_b256k', 'smmpg_b25k', 'smmpg_b26k', 'smmpg_b27k', 'smmpg_b288', 'smmpg_b28k', 'smmpg_b29k', 'smmpg_b2k', 'smmpg_b30k', 'smmpg_b31k', 'smmpg_b32', 'smmpg_b320', 'smmpg_b320k', 'smmpg_b32k', 'smmpg_b33k', 'smmpg_b34k', 'smmpg_b352', 'smmpg_b35k', 'smmpg_b36k', 'smmpg_b37k', 'smmpg_b384', 'smmpg_b384k', 'smmpg_b38k', 'smmpg_b39k', 'smmpg_b3k', 'smmpg_b40k', 'smmpg_b416', 'smmpg_b41k', 'smmpg_b42k', 'smmpg_b43k', 'smmpg_b448', 'smmpg_b448k', 'smmpg_b44k', 'smmpg_b45k', 'smmpg_b46k', 'smmpg_b47k', 'smmpg_b480', 'smmpg_b48k', 'smmpg_b49k', 'smmpg_b4k', 'smmpg_b50k', 'smmpg_b512', 'smmpg_b512k', 'smmpg_b51k', 'smmpg_b52k', 'smmpg_b53k', 'smmpg_b544', 'smmpg_b54k', 'smmpg_b55k', 'smmpg_b56k', 'smmpg_b576', 'smmpg_b576k', 'smmpg_b57k', 'smmpg_b58k', 'smmpg_b59k', 'smmpg_b5k', 'smmpg_b608', 'smmpg_b60k', 'smmpg_b61k', 'smmpg_b62k', 'smmpg_b63k', 'smmpg_b64', 'smmpg_b640', 'smmpg_b640k', 'smmpg_b64k', 'smmpg_b672', 'smmpg_b6k', 'smmpg_b704', 'smmpg_b704k', 'smmpg_b736', 'smmpg_b768', 'smmpg_b768k', 'smmpg_b7k', 'smmpg_b800', 'smmpg_b832', 'smmpg_b832k', 'smmpg_b864', 'smmpg_b896', 'smmpg_b896k', 'smmpg_b8k', 'smmpg_b928', 'smmpg_b96', 'smmpg_b960', 'smmpg_b960k', 'smmpg_b992', 'smmpg_b9k', 'smmpg_ccb', 'smmpg_msb', 'smmpg_twomb', 'total-pages', 'total-size')
before = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
intermediate = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
after = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
x_locations= numpy.arange(len(bx))
width=0.27
fig = plt.figure(figsize=(50, 20))
ax = fig.add_subplot(111)
before_test_mempools_bar = ax.bar(x_locations, list(before), width, color='r')
intermediate_test_mempools_bar = ax.bar(x_locations + width, list(intermediate), width, color='g')
after_test_mempools_bar = ax.bar(x_locations + width *2,list(after), width, color='b')
ax.set_ylabel('Memory')
ax.set_xticks(x_locations + width)
ax.set_xticklabels(bx,rotation=90)
ax.legend((before_test_mempools_bar[0],intermediate_test_mempools_bar[0],after_test_mempools_bar[0]),('BEFORE','INTERMEDIATE','AFTER'))
fig.savefig("plot.png")
plt.close()
The above code produces the following plot:
Goal:
My goal is to accommodate all the data in the plot that is visually nice and so the plot can be analyzed by any tester in the team.
Currently, it's hard to see what's happened with a smaller range of values.
One possible approach would be normalization but not sure if the data would be retained original.
Any possible solutions are appreciated.

Transcribing #Alexander Reynold's comment into an answer:
Use a logarithmic y-axis, i.e. instead of plot() use semilogy() – You can change the base depending on what the dynamic range you need to display is.

I didn't know that there is already an argument parameter in bar function to change the scale of Y-axis.
After adding log=True argument to all the bar functions as below,
before_test_mempools_bar = ax.bar(x_locations, list(before_test_mempools), width, color='r',log=True)
intermediate_test_mempools_bar = ax.bar(x_locations + width, list(intermediate_test_mempools), width, color='g',log=True)
after_test_mempools_bar = ax.bar(x_locations + width *2,list(after_test_mempools), width, color='b',log=True)
My plot looks much nicer now and easy to analyze.

If I may, I think your problem is not technical but that you didn't think enough about you want you to show and what you want the people to look at because the graphic you're showing doesn't seem to have a lot of "noise" - i.e. area of the graphics that don't give much or even any information.
So, even if you only provided simulated data, it seems that there is some room of improvement to make a much readable and "to the point" visualization.
For example you could:
remove uninteresting information (maybe those at 0.0 or those that haven't evolved ?)
regroup some categories by group (what about creating new aggregated categories ? or showing the data in a total different way with values on the x axes and names of categories on the y axes ?)
Also, maybe you're putting together different kind of things (those last 3 bx categories ('smmpg_twomb', 'total-pages' &'total-size') shouldn't they be put in a graph on their own ?)
Use a data structure like pandas' DataFrame to better handle and clean your data in order to do all of the three previous suggestions.
It's just a few suggestions but maybe it will help.
Here is an exemple of what you could do... Just to illustrate:
import matplotlib
matplotlib.use('Agg')
import sys,os,argparse,json,string,numpy
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
bx = ('smmpg_b1024k', 'smmpg_b10k', 'smmpg_b11k', 'smmpg_b128', 'smmpg_b128k', 'smmpg_b12k', 'smmpg_b13k',
'smmpg_b14k', 'smmpg_b15k', 'smmpg_b160', 'smmpg_b16k', 'smmpg_b17k', 'smmpg_b18k', 'smmpg_b192',
'smmpg_b192k', 'smmpg_b19k', 'smmpg_b1k', 'smmpg_b20k', 'smmpg_b21k', 'smmpg_b224', 'smmpg_b22k',
'smmpg_b23k', 'smmpg_b24k', 'smmpg_b256', 'smmpg_b256k', 'smmpg_b25k', 'smmpg_b26k', 'smmpg_b27k',
'smmpg_b288', 'smmpg_b28k', 'smmpg_b29k', 'smmpg_b2k', 'smmpg_b30k', 'smmpg_b31k', 'smmpg_b32',
'smmpg_b320', 'smmpg_b320k', 'smmpg_b32k', 'smmpg_b33k', 'smmpg_b34k', 'smmpg_b352', 'smmpg_b35k',
'smmpg_b36k', 'smmpg_b37k', 'smmpg_b384', 'smmpg_b384k', 'smmpg_b38k', 'smmpg_b39k', 'smmpg_b3k',
'smmpg_b40k', 'smmpg_b416', 'smmpg_b41k', 'smmpg_b42k', 'smmpg_b43k', 'smmpg_b448', 'smmpg_b448k',
'smmpg_b44k', 'smmpg_b45k', 'smmpg_b46k', 'smmpg_b47k', 'smmpg_b480', 'smmpg_b48k', 'smmpg_b49k',
'smmpg_b4k', 'smmpg_b50k', 'smmpg_b512', 'smmpg_b512k', 'smmpg_b51k', 'smmpg_b52k', 'smmpg_b53k',
'smmpg_b544', 'smmpg_b54k', 'smmpg_b55k', 'smmpg_b56k', 'smmpg_b576', 'smmpg_b576k', 'smmpg_b57k',
'smmpg_b58k', 'smmpg_b59k', 'smmpg_b5k', 'smmpg_b608', 'smmpg_b60k', 'smmpg_b61k', 'smmpg_b62k',
'smmpg_b63k', 'smmpg_b64', 'smmpg_b640', 'smmpg_b640k', 'smmpg_b64k', 'smmpg_b672', 'smmpg_b6k',
'smmpg_b704', 'smmpg_b704k', 'smmpg_b736', 'smmpg_b768', 'smmpg_b768k', 'smmpg_b7k', 'smmpg_b800',
'smmpg_b832', 'smmpg_b832k', 'smmpg_b864', 'smmpg_b896', 'smmpg_b896k', 'smmpg_b8k', 'smmpg_b928',
'smmpg_b96', 'smmpg_b960', 'smmpg_b960k', 'smmpg_b992', 'smmpg_b9k', 'smmpg_ccb', 'smmpg_msb',
'smmpg_twomb', 'total-pages', 'total-size')
before = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
intermediate = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
after = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
# Put your data in a DataFrame:
df = pd.DataFrame({'before': before,
'intermediate': intermediate,
'after': after, 'bx': bx,
'x_locations': numpy.arange(len(bx))
})
#filter columns - you can put them in another graph!
df_filt_cat = df.loc[(df.bx != 'smmpg_twomb') & (df.bx != 'total-pages') & (df.bx != 'total-size')]
# filter categories that stay 0 all the way
df_filt_zero = df_filt_cat.loc[(df_filt_cat.before != 0) & (df_filt_cat.intermediate != 0) & (df_filt_cat.after != 0)]
x_locations= numpy.arange(len(bx))
width=0.27
fig = plt.figure(figsize=(50, 20))
ax = fig.add_subplot(111)
before_test_mempools_bar = ax.bar(df_filt_zero.x_locations, df_filt_zero.before, width, color='r')
before_test_mempools_bar = ax.bar(df_filt_zero.x_locations, df_filt_zero.before, width, color='r')
intermediate_test_mempools_bar = ax.bar(df_filt_zero.x_locations + width, df_filt_zero.intermediate, width, color='g')
after_test_mempools_bar = ax.bar(df_filt_zero.x_locations + width *2, df_filt_zero.after, width, color='b')
ax.set_ylabel('Memory')
ax.set_xticks(x_locations + width)
ax.set_xticklabels(bx,rotation=90)
ax.legend((before_test_mempools_bar[0],intermediate_test_mempools_bar[0],after_test_mempools_bar[0]),('BEFORE','INTERMEDIATE','AFTER'))
# just to show the result I commented this line
#fig.savefig("plot.png")
# and put this one instead:
plt.show()
It obviously still needs improvement but it's already a bit more readable.

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