Description:
Develop a tool that segments customers based on their purchase history and demographic data. Use pd.cut() and pd.qcut() to categorize customers into bins based on spending behavior and demographic characteristics. Employ pd.Categorical() for customer type classifications and pd.get_dummies() for converting categorical data into dummy variables. Implement pd.merge() to combine different datasets and pd.value_counts() to analyze the distribution of customer segments.
Description:
Create a dashboard that visualizes and analyzes sales performance data across various regions and time periods. Utilize pd.read_csv() or pd.read_excel() to import sales data. Apply DataFrame.groupby() and DataFrame.agg() to summarize sales figures by region and product. Use DataFrame.plot() for visualizations and DataFrame.rolling() to analyze trends over time. Implement pd.to_datetime() for handling date-related operations and DataFrame.resample() for aggregating data on different time scales.
Description:
Build a real-time financial data analyzer that processes and visualizes stock market data. Use pd.read_csv() for importing historical stock data and pd.date_range() to generate time periods. Apply pd.rolling() and pd.expanding() to calculate moving averages and other financial indicators. Use DataFrame.pivot_table() to analyze trends and correlations. Implement pd.cut() for binning stock price ranges and DataFrame.stack() for reshaping data for better visualization.
Description:
Develop a system to manage and analyze patient data, including medical history, treatments, and outcomes. Use pd.read_csv() to import patient records and pd.Categorical() for encoding categorical variables such as treatment types. Apply DataFrame.dropna() and DataFrame.fillna() for handling missing data. Utilize pd.merge() to combine datasets from different sources and pd.get_dummies() to convert categorical variables into numerical format for further analysis.
Description:
Create an inventory optimization system for a retail store that predicts stock needs and manages inventory levels. Use pd.read_csv() to load inventory and sales data. Apply pd.cut() and pd.qcut() to categorize inventory levels and sales performance. Implement DataFrame.groupby() and DataFrame.agg() to calculate inventory turnover rates. Use pd.plot() for visualizing stock trends and pd.get_dummies() for encoding categorical inventory data.