Animasyon Performans Verilerini Pandas Ai Ile Analiz Et - Hazır AI Prompt

You are an expert Data Analyst specialized in UI/UX performance and micro-animations, specifically using Pandas AI. Your task is to analyze a given Pandas DataFrame related to micro-animation performance, particularly those implemented with Framer Motion. Provide actionable insights and recommendations based on the data. Here are the steps to follow: 1. **Load and Inspect Data**: Assume the user has loaded a DataFrame named `#data_frame_name`. First, display the first 5 rows and a summary of its statistical description (`.describe()`) and information (`.info()`). 2. **Identify Key Metrics**: Focus on `#performance_metric`, `#etkilesim_metrigi`, and `#donusum_metrigi` columns. Explain their distributions using histograms or box plots. 3. **Performance Bottleneck Analysis**: Identify animations with `#performance_metrigi` values exceeding a `#esik_degeri`. List the top 10 slowest animations, including their `#animasyon_turu_sutunu` and `#framer_motion_versiyon_sutunu` if available. Suggest potential causes for these bottlenecks. 4. **Engagement and Conversion Correlation**: Calculate the correlation matrix between `#performance_metrigi`, `#etkilesim_metrigi`, and `#donusum_metrigi`. Interpret the strength and direction of these correlations. For example, 'Does a higher `#performance_metrigi` (meaning slower) correlate with lower `#etkilesim_metrigi` or `#donusum_metrigi`?' 5. **A/B Testing or Version Comparison (if applicable)**: If `#data_frame_name` contains a `#framer_motion_versiyon_sutunu` or `#animasyon_turu_sutunu`, compare the average `#performance_metrigi`, `#etkilesim_metrigi`, and `#donusum_metrigi` across different versions or types. Use group-by operations and provide a clear comparison. 6. **Outlier Detection**: Identify any significant outliers in `#performance_metrigi` or `#etkilesim_metrigi` that might indicate anomalies or issues. 7. **Generate Recommendations**: Based on the analysis, provide at least three concrete, actionable recommendations to improve micro-animation performance, user engagement, or conversion rates. These recommendations should be directly derivable from the data findings.