VISUALISAITIASIOON
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@@ -1,6 +1,7 @@
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# Visualisations for Data
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import mining_hq
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from numpy import count_nonzero
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@@ -20,19 +21,28 @@ games_fig_pre = sns.histplot(data = games_pre, x = "Year", palette = sns.color_p
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plt.show()
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plt.xticks(rotation = 90)
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games_fig2_pre = sns.histplot(data = games_pre, x = "Year", hue = "Genre", multiple = "stack", shrink = 0.65)
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games_fig2_pre = sns.histplot(data = games_pre, x = "Year", hue = "Genre", multiple = "stack", kde = True)
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plt.show()
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plt.xticks(rotation = 90)
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games_fig_dur = sns.barplot(data = games_dur, x = "Year", y = "NA_Sales")
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plt.xlabel("Years")
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plt.ylabel("Sales in North America (Canada, USA)")
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games_fig_dur = sns.histplot(data = games_dur, x = "Year", kde = True)
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plt.show()
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plt.xticks(rotation = 90)
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games_fig_pos = sns.barplot(data = games_pos, x = "Year", y = "NA_Sales")
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games_fig_pos = sns.histplot(data = games_pos, x = "Year")
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plt.show()
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plt.xticks(rotation = 90)
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crime_CA_fig = sns.barplot(data = crime_CA, x = "year", y = "incidents", estimator=count_nonzero)
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crime_CA_fig = sns.histplot(data = crime_CA, x = "year")
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plt.show()
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plt.xticks(rotation = 90)
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crime_US_fig = sns.histplot(data = crime_US, x = "report_year")
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plt.show()
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games_dur['Violent_US'] = crime_US['violent_crimes']
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games_dur['NA_Sales'] = games_dur['NA_Sales'].multiply(1000)
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plt.xticks(rotation = 90)
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games_violence_US = sns.relplot(data = games_dur, x = 'NA_Sales', y = 'Violent_US')
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plt.show()
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