<jupyter_start><jupyter_text>Summary of Descriptive AnalysisWe began our study using the initial question:         The spread of miss information and political factors all impact the public view, leading some populations to show slow inclines of immunization. We hope to visualize the trends in partial and fully vaccinated amongst provinces in hopes to tag key sociopolicial events that may impact the number of individuals who choose to get the vaccine.    The limitation of this course was the nessicary collected data related to news events that we could then link to trends in our vaccination data. This resulted in a reformulation of the question to the following:    With the rise in COVID-19 fatalities and the incresed pressure on the medical systems within each province which provinces were slowest to rise, the question being which region within Canada had the fastest rollout plan, and which had the highest conversion from partially to fully vaccinated.    We have provided an indepth analysis notebook found within the same folder as this file. The analysis describes the steps taken to obtain the refind data, and the resluting graphs. All functions have been defined within python file project_functions1.py   Examining Trends in Vaccination Counts between RegionsIn this section we will describe the trending partially and fully vacinated data to begin building context for further discussion. The graphs below hold the total number of vaccines given across all manufactures provided by region. The graph moves with respect to time as the data is given from the period of Dec 2020 to Sept 2021. Each week can be seen to<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%reload_ext autoreload
%autoreload 2
import seaborn as sns
import project_functions1 as pf

vacx = pd.read_csv("../data/processed/JoeProcessed/normalized.csv")

display(pf.graph1(vacx))
display(pf.graph2(vacx))<jupyter_output>/Users/joegaspari/Documents/Documents/Year2/S1/DATA 301/GroupProj/project-projectgroup-48/notebooks/project_functions1.py:321: UserWarning: FixedFormatter should only be used together with FixedLocator<jupyter_text>Graphs 1 and 2:*Graph 1:* In graph one we see each region separated by color with respect to week and the number of partially vacinated. In graph 1 above, we see that the count data trends extremely similar for each month. This data shows that on average during the 24th week we see a crest in the number of partially vacinated in each province. *Graph 2:* In graph two we see the normalized values, where we took each regions data and normalized it to the population within that region. We see from this process that the data shifts significantly and we see the provinces of Yukon, Northwest Territories and Nunavut have a significant jump in weeks 6-8. We can make the assumption that based on the remote nature and susceptibility to outbreak these provinces experienced a more aggresive roll out plan.<jupyter_code>display(pf.graph3(vacx))
display(pf.graph4(vacx))<jupyter_output>/Users/joegaspari/Documents/Documents/Year2/S1/DATA 301/GroupProj/project-projectgroup-48/notebooks/project_functions1.py:297: UserWarning: FixedFormatter should only be used together with FixedLocator
  g = sns.scatterplot(x='week_end', y='sumTotFul', hue='Region', data=dataf, palette='pastel').set_xticklabels(range(39))<jupyter_text>Graphs 3 & 4:*Graph 3:* We begin to examine the trend towards fully vacinated and how the trends in data change between provinces. Without normalization the upper graph displays that all provinces follow a general upward trend towards fully vacinated. This trend coincide's with the downward trend seen in the partially vaccinated data. This makes sense as many citizens transitioned from partial to fully about 3 weeks after first dose.*Graph 4:* When the data becomes preportional to the population density within the region we see the same significance in the provinces of Nunavut, Northwest Territories, and Yukon. That being the rise in fully vaccinated occured in weeks 10-12, while large density provinces rose in weeks 25-29.<jupyter_code>display(pf.showCh(vacx))<jupyter_output><empty_output>
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