Game Data Analysis Part 1
shebom
python
a year ago
1.7 kB
8
Indexable
#https://drive.google.com/file/d/1BlMmC4c5a11jFB8hhKo03Vq2WYH1sZgV/view?usp=sharing #installing all the dependencies !pip install pandas matplotlib seaborn plotly from google.colab import drive drive.mount('/content/drive') import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px # Define a boolean variable is_true = True # Use the not operand to negate the boolean value is_not_true = not is_true # Print the results print("Original value:", is_true) print("Negated value:", is_not_true) # Load the dataset df = pd.read_csv('/game_data.csv') # Display basic information about the dataset print(df.info()) # Display 1 line space in between print('\n') # Display the first few rows of the dataset print(df.head()) # Display 1 line space in between print('\n') # Display the first few rows of the dataset print(df.tail()) df # Drop rows with missing values df = df.dropna() # Convert 'Year_of_Release' to integer df['Year'] = df['Year'].astype(int) # Group the DataFrame 'df' by 'Publisher' and sum the 'Global_Sales' for each publisher top_publishers = df.groupby('Publisher')['Global_Sales'].sum().nlargest(10) # Set up the figure size for the plot plt.figure(figsize=(10, 6)) # Create a bar plot using Seaborn, where x-values are the global sales values, y-values are the publisher names, # and use the 'viridis' color palette for the bars sns.barplot(x=top_publishers.values, y=top_publishers.index, palette='viridis') # Set the title of the plot plt.title('Top 10 Publishers by Global Sales') # Set labels for the x and y axes plt.xlabel('Global Sales (in millions)') plt.ylabel('Publisher') # Display the plot plt.show()
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