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

# In[4]:


import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as plt


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cv = pd.read_csv(r'D:\SIE598\Crimes2021.csv', parse_dates=['Date'])


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cv['Year']=pd.to_datetime(cv['Date']).dt.strftime('%Y')
cv['Month']=pd.to_datetime(cv['Date']).dt.strftime('%m')
cv['Day']=pd.to_datetime(cv['Date']).dt.strftime('%d')
cv['Hour']=pd.to_datetime(cv['Date']).dt.strftime('%H')

cv['Year']=cv['Date'].dt.year
cv['Month']=cv['Date'].dt.month
cv['Week'] = cv['Date'].dt.weekday_name
cv['Weeky'] = cv['Date'].dt.weekofyear
cv['Day']=cv['Date'].dt.day


# In[7]:


cv.Date


# In[8]:


#Generate and graphically present a summary of the counts of Primary Type of crime by month
cv2 = cv.groupby(['Primary Type','Month']).count().reset_index()
cv2=cv2.pivot(index='Primary Type', columns='Month', values='ID')
color = sns.color_palette("icefire", as_cmap=True)
cv2.plot.bar(stacked=True, cmap=color, figsize=(12,5), rot=80).legend(loc='center left', bbox_to_anchor=(1, 0.5))


# In[9]:


#Generate and graphically present a summary of the counts of Primary Type of crime by week
cv2 = cv.groupby(['Primary Type','Weeky']).count().reset_index()
cv2=cv2.pivot(index='Weeky', columns='Primary Type', values='ID')
color = sns.color_palette("icefire", as_cmap=True)

cv2.plot.bar(stacked=True,cmap=color, figsize=(12,10)).legend(loc='center left', bbox_to_anchor=(1, 0.5))


# In[10]:


#Generate and graphically present a summary of the counts of Primary Type of crime by day of week (e.g Monday  through Sunday)
cv2 = cv.groupby(['Primary Type','Week']).count().reset_index()
cv2=cv2.pivot(index='Primary Type', columns='Week', values='ID')

color = sns.color_palette("icefire", as_cmap=True)

cv2.plot.bar(stacked=True,cmap=color, figsize=(12,10), rot=80).legend(loc='center left', bbox_to_anchor=(1, 0.5))


# In[11]:


#Generate and graphically present a summary of counts of Primary Type of crime by hour of day.
cv2 = cv.groupby(['Primary Type','Hour']).count().reset_index()
cv2=cv2.pivot(index='Hour', columns='Primary Type', values='ID')
color = sns.color_palette("icefire", as_cmap=True)

cv2.plot.bar(stacked=True,cmap=color, figsize=(12,10)).legend(loc='center left', bbox_to_anchor=(1, 0.5))


# In[12]:


# There are a few temporal trends shown in the data - crimes overall are lower in the early hours in the morning and peak around midnight.
# Interestingly, criminal damage peaks around 9 and 12 AM. More dangerous crimes peak at night.
# Crime seems to be pretty consistent no matter what day of the week it is, with a sharp drop in February going in to March.