#!/usr/bin/env python
# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import statsmodels.api as sm
from numpy.random import randn
import matplotlib as mpl
import seaborn as sns

sns.set_color_palette("deep", desat=.6)
mpl.rc("figure", figsize=(8, 4))

def Compavg():
	markMax=[]
	markAvg=[]
	N = 5
	ind = np.arange(N)    
	width = 0.35 
	fig = plt.figure()
	ax = fig.add_subplot(111)      
	markMax.extend((data["Total"].max(),data["Total.1"].max(),data["Total.2"].max(),data["Total.3"].max(),data["Total.4"].max()))
	markAvg.extend((data["Total"].mean(),data["Total.1"].mean(),data["Total.2"].mean(),data["Total.3"].mean(),data["Total.4"].mean()))
	rects1 = ax.bar(ind, markMax, width, color='black')
	rects2 = ax.bar(ind+width, markAvg, width, color='green')
	ax.set_xlim(-width,len(ind)+width)
	ax.set_ylim(0,120)
	ax.set_ylabel('Marks')
	ax.set_title('Max, Mean and Your Marks')
	xTickMarks = ['Subject'+str(i) for i in range(1,6)]
	ax.set_xticks(ind+width)
	xtickNames = ax.set_xticklabels(xTickMarks)
	plt.setp(xtickNames, rotation=10, fontsize=10)
	ax.legend( (rects1[0], rects2[0]), ('Max', 'Mean') )
	plt.show()
	
def compSub():
	#max_data = np.r_[data["Total"]].max()
	#bins = np.linspace(0, max_data, max_data + 1)
	plt.hist(data["Total"],linewidth=0, alpha=.7)
	plt.hist(data['Total.1'],linewidth=0,alpha=.7)
	plt.hist(data['Total.2'],linewidth=0,alpha=.7)
	plt.hist(data['Total.3'],linewidth=0,alpha=.7)
	plt.hist(data['Total.4'],linewidth=0,alpha=.7)
	plt.title("Total marks Histogram")
	plt.xlabel("Value")
	plt.ylabel("Frequency")
	plt.show()

data=pd.read_csv("output11cs.csv")
df3=data[['Total','Total.1','Total.2','Total.3','Total.4','Total.5','Total.6','Total.7']]
data["Main Total"]=df3.sum(axis=1)
data = data.dropna()
data.reset_index(drop=True)
compSub()
Compavg()

Create asys.py