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| author | Christian Cleberg <hello@cleberg.net> | 2024-03-04 22:34:28 -0600 |
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| committer | Christian Cleberg <hello@cleberg.net> | 2024-03-04 22:34:28 -0600 |
| commit | 797a1404213173791a5f4126a77ad383ceb00064 (patch) | |
| tree | fcbb56dc023c1e490df70478e696041c566e58b4 /content/blog/2020-07-20-video-game-sales.md | |
| parent | 3db79e7bb6a34ee94935c22d7f0e18cf227c7813 (diff) | |
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initial migration to test org-mode
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diff --git a/content/blog/2020-07-20-video-game-sales.md b/content/blog/2020-07-20-video-game-sales.md deleted file mode 100644 index bff90a8..0000000 --- a/content/blog/2020-07-20-video-game-sales.md +++ /dev/null @@ -1,181 +0,0 @@ -+++ -date = 2020-07-20 -title = "Data Exploration: Video Game Sales" -description = "Exploring and visualizing data with Python." -+++ - -# Background Information - -This dataset (obtained from -[Kaggle](https://www.kaggle.com/gregorut/videogamesales/data)) 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 - -```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 -``` - - - -# Explore the Data - -```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() -``` - - - -```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)) -``` - -# Visualize the Data - -```python -# This function plots the global sales by platform -sns.catplot(x='Platform', y='Global_Sales', data=df, jitter=False).set_xticklabels(rotation=90) -``` - - - -```python -# This function plots the global sales by genre -sns.catplot(x='Genre', y='Global_Sales', data=df, jitter=False).set_xticklabels(rotation=45) -``` - - - -```python -# This function plots the global sales by year -sns.lmplot(x='Year', y='Global_Sales', data=df).set_xticklabels(rotation=45) -``` - - - -```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() -``` - - - -# Investigate Outliers - -```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() -``` - - - -```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) -``` - - - -```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) -``` - - - -# 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. |