# Module for statistics

Here is my module for statistics:

import numpy as np
import scipy.stats as st

class Statistics():

def sterm_and_leaf(self,data):
graph={}
for i in data:
x=str(i)
if len(x)>1:
if int(x[0]) in graph.keys(): graph[int(x[0])].append(int(x[1:]))
else : graph[int(x[0])]=[int(x[1:])]
elif len(x)==1:
if 0 in graph: graph[0].append(int(x))
else : graph[0]=[int(x)]

for v in graph.values():  v.sort()

for k in sorted(graph.keys(),reverse=True):
print(k,':',' '.join([str(i) for i in graph[k]]))

return graph

def hystogram(self,data):

import matplotlib.pyplot as plt
plt.hist(data)

plt.xlabel("Value")
plt.ylabel("Frequency")

plt.show()

def trimean(self,data):
from numpy import percentile
return (percentile(data,25)+2*percentile(data,50)+percentile(data,75))/4

def trimmed_mean(self,data,percent):
sorted_data = sorted(data) #uncomment to use plain python sorted
n = len(sorted_data)
outliers = n*percent//100 #may want some rounding logic if n is small
trimmed_data = sorted_data[outliers: n-outliers]
#from numpy import percentile
#if percent<50: data_trimmed=[i for i in data if i>percentile(data,percent)and i<percentile(data,100-percent)]
#else: data_trimmed=[i for i in data if i<percentile(data,percent)and i>percentile(data,100-percent)]
return sum(trimmed_data)/float(len(trimmed_data))

def variance(self,data):
mean=sum(data)/float(len(data))
var=[(i-mean)**2 for i in data]
return round(sum(var)/float(len(var)-1),4)

def pearson_correlation_coefficient(self,X,Y):
mean_x=sum(X)/len(X)
mean_y=sum(Y)/len(Y)
x=[(i-mean_x) for i in X]
y=[j-mean_y for j in Y]
z=[x[i]*y[i] for i in range(len(x))]
return round(sum(z)/((sum([k**2 for k in x])*sum([v**2for v in y]))**0.5),3)

def confidence_interval(self,data,alpha):
import numpy as np
import scipy.stats as st
g=st.t.interval(alpha, len(data) - 1, loc=np.mean(data), scale=st.sem(data))
return([round(i) for i in g])

def scatter_plot(self,x,y):
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.show()

def get_points_from_scatter(self,x,y,n):
from pylab import ginput,plot,scatter

scatter(x,y)
z = ginput(n)

return z


Please look for full version on Github Any hint on improving the code? Module was created for statistical analysis of data: - finding mean, median, mode, trimmed mean - finding range, variance, standard deviation, correlation coefficient - various visual representations of data: histogram, stern-and-leaf display, scatter plot.

• Questions must include the code to be reviewed. Links to code hosted on third-party sites are permissible, but the most relevant excerpts must be embedded in the question itself. Please paste your code here instead of giving us a link to github – яүυк Jul 25 '17 at 18:28
• I inserted some code. The matter is that module is big – Белякова Анастасия Jul 26 '17 at 11:56