FAQ: Variable Types - Altering the Data Types of Pandas Dataframes

This community-built FAQ covers the “Altering the Data Types of Pandas Dataframes” exercise from the lesson “Variable Types”.

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This exercise can be found in the following Codecademy content:

Master Statistics with Python

FAQs on the exercise Altering the Data Types of Pandas Dataframes

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What is the difference between None and np.NaN? Does it matter which one I use in terms of best practices?

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One difference is the type. np.nan is float but None is NoneType.

>>> import numpy as np
>>> type(np.nan)
<class 'float'>
>>> type(None)
<class 'NoneType'>

One more difference to be noted is that np.nan's don’t compare equal, but None’s do.

>>> np.nan == np.nan
False
>>> None == None
True

For several reasons, Pandas uses np.nan to denote missing data. To create a Pandas Series with data type float and containing missing data, the missing data need to be denoted by np.nan instead of None. The data type of a Series containing None must be object.

>>> import pandas as pd
>>> pd.Series([np.nan])
0   NaN
dtype: float64
>>> pd.Series([None])
0    None
dtype: object
>>> pd.Series([1, None])  # Here, Pandas automatically converts None to np.nan
0    1.0
1    NaN
dtype: float64

The following User Guides may be helpful.

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