In a Pandas DataFrame, None, NaN, and NULL

In a Pandas DataFrame, None, NaN, and NULL are often used to represent missing values, but they are not exactly the same. Here’s a breakdown:

1. None:
   • None is the standard Python object used to denote the absence of a value.
   • In a Pandas DataFrame, if you assign a None to a cell, it will be automatically converted to NaN (in numeric or floating-point columns) when the DataFrame is constructed.
2. NaN (Not a Number):
   • NaN is a floating-point value used by libraries like NumPy to represent missing or undefined values, especially in numerical arrays.
   • In Pandas, NaN is used to represent missing values for all data types (not just floats).
   • NaN is defined by the IEEE floating-point standard and is specifically used in the context of numerical data.
   • Pandas relies heavily on NaN to indicate missing data, and many Pandas functions treat NaN values differently (e.g., in computations).
3. NULL:
   • NULL is not typically a Pandas-specific concept. It’s more commonly associated with SQL databases, where it represents a missing or undefined value.
   • If you see NULL in a DataFrame, it’s usually coming from a source like a database or CSV file. In Pandas, it is treated similarly to None or NaN and is usually converted to NaN when the data is read into a DataFrame.

Practical Differences in Pandas:

• When you read a CSV file with missing values, Pandas will automatically convert None, NaN, or NULL to NaN in the resulting DataFrame.
• Checking for Missing Values:
  • You can use pd.isna() or pd.isnull() to check for missing values, and these functions treat None, NaN, and NULL as equivalent.
  • You can use fillna() to replace missing values regardless of whether they were originally None, NaN, or NULL.