• Checking for Missing Values :

  • vg.isnull().sum(): This code snippet allows us to identify and quantify missing values in our dataset, providing insight into potential data quality issues that need to be addressed.

• Identifying Rows with Missing Values :

  • vg[vg.isnull().any(axis=1)]: By filtering the dataset to identify rows with missing values, we can investigate and address instances of incomplete data, ensuring the integrity of our analysis.

• Removing Rows with Missing Values:

  • vg.dropna(): Removing rows with missing values ensures that our dataset is clean and suitable for analysis, minimizing the risk of biases or inaccuracies in our results.

• Setting Index:

  • vg = vg.set_index('index'): Setting a meaningful index enhances the organization and accessibility of our dataset, facilitating smoother data retrieval and analysis.

• Converting Data Types:

  • vg['User_Score'] = vg['User_Score'].astype('float64'): This code ensures consistency in our dataset by converting the 'User_Score' column to a numerical format. It's essential for enabling accurate numerical operations and analysis.

• Date Manipulation:

  • vg['Year_of_Release'] = pd.to_datetime(vg['Year_of_Release'], format='%Y'):  By converting the 'Year_of_Release' column to datetime format, we standardize date representation, facilitating time-based analysis and ensuring data uniformity. Formatting is done to avoid repetition of same date value.

  • vg['Year_of_Release'] = vg['Year_of_Release'].dt.strftime('%Y'): This code further refines the 'Year_of_Release' column, displaying only the year component for improved readability and analysis.

• Data Filtering:

  • Filtering rows based on specific years of interest: vg1[vg1['Year_of_Release'].isin(['1996','1994','1985','1992','1988'])]:
    By filtering the dataset, we focus our analysis on specific years, enabling deeper insights into trends and patterns within our data.

• Identifying and Removing Duplicate Rows:

  • Identifying rows with duplicates vg1.duplicated().sum(): Detecting duplicate rows ensures data accuracy and reliability. It allows us to identify and address redundant entries, maintaining the integrity of our analysis.

  • Removing duplicate rows vg1 = vg1.drop_duplicates(): Removing duplicate rows: vg1 = vg1.drop_duplicates(): By eliminating duplicate records, we ensure that each entry in our dataset is unique. This step is crucial for preventing biases and inaccuracies in our analysis results.

Here i visualize the number of game releases and global sales using a line plot. The orange line represents the trend in the number of game releases each year, while the green line depicts the trend in global sales. The plot helps identify any correlations or patterns between game releases and sales over time, providing valuable insights into the gaming industry's dynamics.

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Here i calculate the total global sales for each genre and visualize the results using a horizontal bar plot. The length of each bar represents the total sales in millions, while the genre categories are displayed on the y-axis. This visualization helps identify the most popular genres in terms of global sales, providing insights into consumer preferences within the gaming market.

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Here i conduct an analysis of global sales trends by platform in the gaming industry. This code calculates the total global sales for each gaming platform and visualizes the results using a horizontal bar plot. The length of each bar represents the total sales in millions, while the platform categories are displayed on the y-axis. This visualization provides insights into the popularity and performance of different gaming platforms in terms of global sales.

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This code analyzes global sales trends over the years for specific gaming platforms, including PC, PS4, and Xbox (XB). It filters the dataset to include only the selected platforms and then visualizes the sales data using a line plot. Each line represents a platform, with markers indicating sales data points for each year. This visualization enables the comparison of sales trends across different platforms over time, offering insights into platform-specific market dynamics.

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This code analyzes regional sales trends across different regions over time in the gaming industry. It calculates the total sales for each region, including North America, Europe, Japan, and the rest of the world, and visualizes the results using a horizontal bar plot. The length of each bar represents the total sales in millions, while the region categories are displayed on the y-axis. This visualization provides insights into the distribution of gaming sales across various regions, highlighting key markets in terms of total sales.

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This code analyzes the market share trends of top publishers in the gaming industry over time. It groups the dataset by the year of release and publisher, calculates the total sales for each combination, and then computes the market share of each publisher relative to total sales for each year. The analysis focuses on the top 10 publishers based on total sales. Finally, it visualizes the market share trends over time using a line plot, with each line representing a publisher's market share, allowing for insights into the competitive landscape and publishers' performance over the years.

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This code conducts an analysis of publisher sales trends in the gaming industry by identifying the top 20 publishers based on global sales. It calculates the total global sales for each publisher, selects the top 20 publishers with the highest sales, and visualizes the results using a horizontal bar plot. The length of each bar represents the total sales in millions, while the publisher names are displayed on the y-axis. This visualization offers insights into the performance of publishers in terms of global sales, highlighting key players in the gaming market

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This code helps to visualize the sales trends of video games over time in different regions (North America, Europe, Japan, and other regions) and identify patterns and fluctuations in different markets. 

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This code dentify and visualize the most profitable video game genres based on total global sales. 

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This code helps to track and visualize the genre with the highest global sales each year, showing trends and shifts in genre popularity over time. 

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Below code segment visualizes the frequency distribution of user scores and critic scores for video games.

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Below codes used to calculate and visualize the average user and critic scores over the years, providing insights into the trends and changes in game evaluations over time. Also, check any correlation between them.

We normalize the 'User_Score' values to match the scale of 'Critic_Score' for better comparison and correlation analysis via - 

vg1['Normalized_User_Score'] = vg1['User_Score'] * 10 

Scatter Plot is used to visualize the relationship between Critic_Score and Normalized_User_Score, providing insights into the correlation between user and critic evaluations.

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