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+#+date: 2020-07-20
+#+title: Data Exploration: Video Game Sales
+
+* Background Information
+
+This dataset (obtained from [[https://www.kaggle.com/gregorut/videogamesales/data][Kaggle]]) contains a list of video games with sales
+greater than 100,000 copies. It was generated by a scrape of vgchartz.com.
+
+Fields include:
+
+- Rank: Ranking of overall sales
+- Name: The game name
+- Platform: Platform of the game release (i.e. PC,PS4, etc.)
+- Year: Year of the game's release
+- Genre: Genre of the game
+- Publisher: Publisher of the game
+- NA_Sales: Sales in North America (in millions)
+- EU_Sales: Sales in Europe (in millions)
+- JP_Sales: Sales in Japan (in millions)
+- Other_Sales: Sales in the rest of the world (in millions)
+- Global_Sales: Total worldwide sales.
+
+There are 16,598 records. 2 records were dropped due to incomplete information.
+
+* Import the Data
+
+#+BEGIN_SRC python
+# Import the Python libraries we will be using
+import pandas as pd
+import numpy as np
+import seaborn as sns; sns.set()
+import matplotlib.pyplot as plt
+
+# Load the file using the path to the downloaded file
+file = r'video_game_sales.csv'
+df = pd.read_csv(file)
+df
+#+END_SRC
+
+#+CAPTION: Dataframe Results
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/01_dataframe-min.png]]
+
+* Explore the Data
+
+#+BEGIN_SRC python
+# With the description function, we can see the basic stats. For example, we can also see that the 'Year' column has some incomplete values.
+df.describe()
+#+END_SRC
+
+#+CAPTION: df.describe()
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/02_describe-min.png]]
+
+#+BEGIN_SRC python
+# This function shows the rows and columns of NaN values. For example, df[179,3] = nan
+np.where(pd.isnull(df))
+
+(array([179, ..., 16553], dtype=int64),
+ array([3, ..., 5], dtype=int64))
+#+END_SRC
+
+* Visualize the Data
+
+#+BEGIN_SRC python
+# This function plots the global sales by platform
+sns.catplot(x='Platform', y='Global_Sales', data=df, jitter=False).set_xticklabels(rotation=90)
+#+END_SRC
+
+#+CAPTION: Plot of Global Sales by Platform
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/03_plot-min.png]]
+
+#+BEGIN_SRC python
+# This function plots the global sales by genre
+sns.catplot(x='Genre', y='Global_Sales', data=df, jitter=False).set_xticklabels(rotation=45)
+#+END_SRC
+
+#+CAPTION: Plot of Global Sales by Genre
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/04_plot-min.png]]
+
+#+BEGIN_SRC python
+# This function plots the global sales by year
+sns.lmplot(x='Year', y='Global_Sales', data=df).set_xticklabels(rotation=45)
+#+END_SRC
+
+#+CAPTION: Plot of Global Sales by Year
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/05_plot-min.png]]
+
+#+BEGIN_SRC python
+# This function plots four different lines to show sales from different regions.
+# The global sales plot line is commented-out, but can be included for comparison
+df2 = df.groupby('Year').sum()
+years = range(1980,2019)
+
+a = df2['NA_Sales']
+b = df2['EU_Sales']
+c = df2['JP_Sales']
+d = df2['Other_Sales']
+# e = df2['Global_Sales']
+
+fig, ax = plt.subplots(figsize=(12,12))
+ax.set_ylabel('Region Sales (in Millions)')
+ax.set_xlabel('Year')
+
+ax.plot(years, a, label='NA_Sales')
+ax.plot(years, b, label='EU_Sales')
+ax.plot(years, c, label='JP_Sales')
+ax.plot(years, d, label='Other_Sales')
+# ax.plot(years, e, label='Global_Sales')
+
+ax.legend()
+plt.show()
+#+END_SRC
+
+#+CAPTION: Plot of Regional Sales by Year
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/06_plot-min.png]]
+
+* Investigate Outliers
+
+#+BEGIN_SRC python
+# Find the game with the highest sales in North America
+df.loc[df['NA_Sales'].idxmax()]
+
+Rank                     1
+Name            Wii Sports
+Platform               Wii
+Year                  2006
+Genre               Sports
+Publisher         Nintendo
+NA_Sales             41.49
+EU_Sales             29.02
+JP_Sales              3.77
+Other_Sales           8.46
+Global_Sales         82.74
+Name: 0, dtype: object
+
+# Explore statistics in the year 2006 (highest selling year)
+df3 = df[(df['Year'] == 2006)]
+df3.describe()
+#+END_SRC
+
+#+CAPTION: Descriptive Statistics of 2006 Sales
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/07_2006_stats-min.png]]
+
+#+BEGIN_SRC python
+# Plot the results of the previous dataframe (games from 2006) - we can see the year's results were largely carried by Wii Sports
+sns.catplot(x="Genre", y="Global_Sales", data=df3, jitter=False).set_xticklabels(rotation=45)
+#+END_SRC
+
+#+CAPTION: Plot of 2006 Sales
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/08_plot-min.png]]
+
+#+BEGIN_SRC python
+# We can see 4 outliers in the graph above, so let's get the top 5 games from that dataframe
+# The results below show that Nintendo had all top 5 games (3 on the Wii and 2 on the DS)
+df3.sort_values(by=['Global_Sales'], ascending=False).head(5)
+#+END_SRC
+
+#+CAPTION: Outliers of 2006 Sales
+[[https://img.0x4b1d.org/blog/20200720-data-exploration-video-game-sales/09_outliers-min.png]]
+
+* Discussion
+
+The purpose of exploring datasets is to ask questions, answer questions, and
+discover intelligence that can be used to inform decision-making. So, what have
+we found in this dataset?
+
+Today we simply explored a publicly-available dataset to see what kind of
+information it contained. During that exploration, we found that video game
+sales peaked in 2006. That peak was largely due to Nintendo, who sold the top 5
+games in 2006 and has a number of games in the top-10 list for the years
+1980-2020. Additionally, the top four platforms by global sales (Wii, NES, GB,
+DS) are owned by Nintendo.
+
+We didn't explore everything this dataset has to offer, but we can tell from a
+brief analysis that Nintendo seems to rule sales in the video gaming world.
+Further analysis could provide insight into which genres, regions, publishers,
+or world events are correlated with sales.