Detective Controls
Understanding Missing Data
Missing data appears as NaN (Not a Number) or None in Pandas.
Common causes: data collection errors, optional fields, merging datasets.
Upload Your Data
No file selected
Detection Methods
Treatment Methods
Missing Data Statistics
0
Total Missing
0%
Missing %
0
Rows with Missing
0
Cols with Missing
# Pandas code will appear here
# Example: df.isna().sum()
Data Visualization
How to Use This Tool
1. Select a dataset with missing values or upload your own CSV/JSON file
2. Use detection methods to find missing values
3. Apply treatment methods to handle missing data
4. Observe changes in the DataFrame and statistics
DataFrame with Missing Values
Missing Values Heatmap
Missing Data Pattern
Missing Data Handling Strategies
Deletion: Remove rows/columns with missing values (dropna)
Imputation: Fill missing values with estimates (fillna)
Interpolation: Estimate values based on neighbors (interpolate)
Prediction: Use machine learning to predict missing values