What\'s the Difference Between NaN and None in Data Analysis?

Exploring the Distinctive Features of NaN and None

In the realm of data analysis, the distinction between NaN (Not-a-Number) and None is paramount. While both denote missing or undefined values, their subtle differences warrant clarification.

NaN, as its name implies, is reserved for numerical data types. It serves as a placeholder for values that cannot be represented as valid numbers. For instance, in pandas data frames, NaN represents missing values in numerical columns.

Unlike NaN, None is a Python keyword that signifies the absence of a value. It applies to both numerical and non-numerical data types. In pandas data frames, None is typically used to represent missing values in non-numerical columns such as strings or categorical data.

In the context of your code, pandas is consistent in employing NaN as a placeholder for missing values, irrespective of whether they occur in strings or numbers. This approach enhances consistency and facilitates data handling.

While NaN is stored efficiently in NumPy's float64 dtype, None falls under the less efficient object dtype. This discrepancy is attributed to the fact that NaN allows for vectorized operations, while None necessitates the use of the object type, which compromises efficiency in NumPy.

For checking the presence of missing values, it is recommended to utilize the isna and notna functions instead of numpy.isnan(). These functions are specifically designed to handle missing data and provide reliable results regardless of data type.

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