<jupyter_start><jupyter_text>Milestone 5 - Data Analysis  Jarod  Research QuestionDoes the release of triple A multiplayer fps titles effect the player numbers of CS:GO?  Data Analysis From the SteamChartz dataset, I extracted the rows about Counter-Strike: Global Offensive, the most popular game on Steam, to process the data and wrangle the average players, gained average players from the previous month, and the four-month rolling gain average and the seven-month rolling gain average for every month recorded in the dataset. This data allows us to have a timeline of the average and the gain. All we need to do with the timeline is see if there are any dips in the average players that are not a part of a longer pre-existing trend and compare them to the release dates of triple-A titles like Call of Duty, Battlefield, Halo, Overwatch, and Valorant.From exploring the Dashboard, I can extract that only Overwatch and Valorant had significant losses in players, both of which fit into a similar niche that Counter-Strike: Global Offensive occupies. Valorant more closely competes with Counter-Strike: Global Offensive, as their gameplay is heavily inspired by it; this is further indicated by the roughly 186,000 players that drop Counter-Strike: Global Offensive in the two months leading up to the release of Valorant. While the release of Call of Duty and Battlefield can reduce the player numbers of Counter-Stike: Global Offensive, they do not frequently occur early on and, in general, are not substantial decreases in player numbers compared to the average players. Lastly, Halo didn’t seem to affect the player numbers of Counter-Strike: Global Offensive as the trend didn’t fluctuate when it was released. From these observations, I can conclude that while you can see decreases in players with the release of other games, most of them are insignificant compared to the total players. Furthermore, the Counter-Strike: Global Offensive community is a dedicated and isolated gaming community with few transient players, only losing many players due to competition in their niche(i.e. Valorant).  Harshal<jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
from project_functions2 import load_process_covid<jupyter_output><empty_output><jupyter_text>Plot<jupyter_code>df_covid_sorted = load_process_covid('../data/raw/SteamCharts.csv')
df_covid_sorted
sns.barplot(x ='Month', y= 'AvgGainPerGame', hue = 'Year', data = df_covid_sorted)<jupyter_output><empty_output><jupyter_text>Final Research Question Analysis (Harshal - Task 4)Research Question: How has Covid-19 affected the active users of games on steam?  I used the data set to find the average gain of players on steam across all games on the platform at the time to see what the AveageGainPerGame on steam is, to show that during the months when covid first started the surge of lockdowns across the world, that more players had joined during this time since everyone was at home and were not allowed to go outside.  I processed the raw dataset into specific points in time of the specific years so January, February, March of 2019, to show the pre-covid and January, February, March of 2020 to show during covid. I used a barplot to show the Months and AvgGainPerGame as x and y values for the plot, also groupingthe years 2019 and 2020 for each month so you can compare the differences in both AvgGainPerGame in that specific month.As you can see that during the months in the year 2020, the bars are above the x-axis, meaning they are in positive AvgGain, but when you compare it to 2019bars which mostly seem to be negative or significantly lower than the 2020 bars. This indicates many gains in players on the platform steamwas when covid was at large during its initial months of 2020. Showing that covid has impacted the steam platforms gamer counts due to many peoplefinding new modes of entertainment via video games!  Lance<jupyter_code>import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
from project_functions3 import load_csgo
sns.set_theme(style="ticks",
              font_scale=1.3, # This scales the fonts slightly higher
             )
plt.rc("axes.spines", top=False, right=False)<jupyter_output><empty_output><jupyter_text>Plot<jupyter_code>df_csgo = load_csgo('../data/raw/Steamcharts.csv', '../data/raw/Twitch_game_data.csv')
df_csgo
sns.barplot(x = 'Month', y = 'Avg_players', data = df_csgo, estimator=np.median)
sns.barplot(x = 'Month', y = 'Avg_viewers', data = df_csgo, estimator=np.median)<jupyter_output><empty_output>
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