✍Tips and Tricks in Python

What is the difference between NaN ,None, pd.nan and np.nan?

Photo by Dave Gandy under the Public Domain Dedication License

Warning: There is no magical formula or Holy Grail here, though a new world might open the door for you.

📈Python For Finance Series

1. Identifying Outliers
2. Identifying Outliers — Part Two
3. Identifying Outliers — Part Three
4. Stylized Facts
5. Feature Engineering & Feature Selection
6. Data Transformation
7. Fractionally Differentiated Features
8. Data Labelling
9. Meta-labeling and Stacking

TL;NR:

• First of all, there is no pd.nan, but do have np.nan.
• if a data is missing and showing NaN, be careful to use NaN ==np.nan. np.nan is not comparable to np.nan... directly.

np.nan == np.nan
False

NaN is used as a placeholder for missing data consistently in pandas, consistency is good. I usually read/translate NaN as “missing”. Also see the ‘working with missing data’ section in the docs.

Wes writes in the docs ‘choice of NA-representation’: