I have normal python code to plot the graphs for csv data(data
visualisation).But I need code in django and graphs should display on
dashboard...
--
You received this message because you are subscribed to the Google Groups
"Django users" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to django-users+unsubscr...@googlegroups.com.
To post to this group, send email to django-users@googlegroups.com.
Visit this group at https://groups.google.com/group/django-users.
To view this discussion on the web visit
https://groups.google.com/d/msgid/django-users/1f9488c2-3cb1-4d24-8969-7b320f7ad907%40googlegroups.com.
For more options, visit https://groups.google.com/d/optout.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import calendar
import numpy as np
import time
crimes=pd.read_csv(r"D:\python\Project\data.csv")
crimes.head(2)
crimes.describe()
crimes.drop(['Unnamed: 0', 'Case Number','Block','IUCR', 'X Coordinate', 'Y Coordinate','Updated On', 'FBI Code', 'Beat','Ward','Community Area', 'Location','Latitude','Longitude','District'], inplace=True, axis=1)
crimes.head(4)
crimes=crimes[(crimes['Year']==2016)|(crimes['Year']==2015)]
crimes.describe()
f=plt.figure(1)
sns.countplot(x='Year',data=crimes)
plt.ylabel('No of Crimes')
f.show()
f1=plt.figure(2)
crimes['Date'] = pd.to_datetime(crimes['Date'],format='%m/%d/%Y %I:%M:%S %p')
crimes['Month']=(crimes['Date'].dt.month).apply(lambda x: calendar.month_abbr[x])
crimes.head(4)
crimes['Month'] = pd.Categorical(crimes['Month'] , categories=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], ordered=True)
months=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
crimes.groupby('Month')['ID'].count().plot(marker='o')
plt.xticks(np.arange(12),months)
plt.ylabel('No of Crimes')
f1.show()
f3=plt.figure(3)
crimes["Weekday"] = crimes['Date'].dt.weekday_name
crimes['Weekday'] = pd.Categorical(crimes['Weekday'],categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)
crimes.head()
crimes.groupby('Weekday')['ID'].count().plot(marker='o',label='Crimes')
plt.ylabel('No of Crimes')
f3.show()
f4=plt.figure(4)
sns.countplot(x='Primary Type',data=crimes,order=crimes['Primary Type'].value_counts().index)
plt.xticks(rotation='vertical')
plt.ylabel('No of Crimes')
f4.show()
f5=plt.figure(5)
temp=crimes.groupby('Location Description')['ID'].count().sort_values(ascending=False)
temp=temp[:10]
temp
temp.plot(kind='bar',color='green')
plt.ylabel('No of Crimes')
f5.show()
f7=plt.figure(6)
sns.countplot(x='Arrest',data=crimes)
plt.ylabel('No of Crimes')
f7.show()
f9=plt.figure(7)
sns.set(rc={'figure.figsize':(12,6)})
sns.countplot(x='Primary Type',hue='Arrest',data=crimes,order=crimes['Primary Type'].value_counts().index)
plt.xticks(rotation='vertical')
plt.ylabel('No of Crimes')
f9.show()
f10=plt.figure(8)
arrest=crimes[crimes['Arrest']==True]
arrest.info()
arrest.groupby('Month')['ID'].count().plot(legend=True,label='Arrests',marker='o',figsize=(8,6))
crimes.groupby('Month')['ID'].count().plot(legend=True,label='Crimes',marker='o')
plt.ylabel('No of Crimes')
plt.xticks(np.arange(12),months)
f10.show()
f11=plt.figure(9)
arrest.groupby('Month')['ID'].count()
crimes.groupby('Month')['ID'].count()
top_crime=crimes[(crimes['Primary Type']=='THEFT')|(crimes['Primary Type']=='BATTERY')|(crimes['Primary Type']=='CRIMINAL DAMAGE')|(crimes['Primary Type']=='NARCOTICS')|(crimes['Primary Type']=='ASSAULT')]
temp=top_crime.pivot_table(values='ID', index='Month', columns='Year', aggfunc=np.size)
sns.heatmap(temp)
f11.show()
f13=plt.figure(10)
temp= top_crime.pivot_table(values='ID', index='Primary Type',columns=top_crime['Date'].dt.hour, aggfunc=np.size)
sns.heatmap(temp)
plt.xlabel('Hours of the day')
plt.ylabel('Type of Crime')
f13.show()
