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Table Of Contents

  • What’s New
  • Installation
  • Frequently Asked Questions (FAQ)
  • Package overview
  • 10 Minutes to Pandas
  • Cookbook
  • Intro to Data Structures
  • Essential Basic Functionality
  • Indexing and Selecting Data
  • Computational tools
  • Working with missing data
  • Group By: split-apply-combine
  • Merge, join, and concatenate
  • Reshaping and Pivot Tables
  • Time Series / Date functionality
  • Plotting with matplotlib
  • Trellis plotting interface
  • IO Tools (Text, CSV, HDF5, ...)
  • Enhancing Performance
  • Sparse data structures
  • Caveats and Gotchas
  • rpy2 / R interface
  • Related Python libraries
  • Comparison with R / R libraries
  • API Reference
    • Input/Output
    • General functions
      • Data manipulations
      • Top-level missing data
      • Top-level dealing with datetimes
      • Standard moving window functions
      • Standard expanding window functions
      • Exponentially-weighted moving window functions
    • Series
    • DataFrame
    • Panel
  • Release Notes

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pandas.core.common.isnull¶

pandas.core.common.isnull(obj)¶

Detect missing values (NaN in numeric arrays, None/NaN in object arrays)

Parameters :

arr : ndarray or object value

Object to check for null-ness

Returns :

isnulled : array-like of bool or bool

Array or bool indicating whether an object is null or if an array is given which of the element is null.

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