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May 2023 Update

The built-in Profiler feature has been removed with version 5.0.0. All existing Profiler cells will remain as read-only cells in your existing canvases. At this time you are no longer able to re-run those cells or create new Profiler cells.

Profiler Alternative

Check out ydata-profiling, formerly pandas-profiling. This package provides an exstensive data report for a given dataset, including:

  • Various statistical metrics
  • Visualizations of data distributions
  • Statistics on missing values
  • Correlations between variables, and more.

To get started, run the following commands to install and run the package, and its dependencies:

!pip install pandas-profiling
!pip install ydata-profiling
!pip install ipywidgets
from ydata_profiling import ProfileReport

# Create and display report
profile = ProfileReport(df, title="Profiling Report")

Check out the GitHub repo for more.

The profiler is a powerful tool allows you to quickly see the distributions and most relevant statistics in your data. To use the profiler, just add the dataset you want to analyze as an input dataframe.

At the top of the profiler, you can sort the columns by various properties, like the number of null values.

Currently, the profiler returns the following statistics:

Data TypeStatistics

  • Histogram
  • NaN entries (counts/percentage)
  • Infinity entries (counts/percentage)
  • Zeros entries (counts/percentage)
  • Min
  • Max
  • Mean
  • Standard Deviation

  • Histogram
  • NaN entries (counts/percentage)
  • Unique entries (counts/percentage)
  • Top-Mode (counts/percentage)
  • Bottom-Mode (counts/percentage)

  • Histogram
  • NaN entries (counts/percentage)
  • Min
  • Max
  • Mean