Why Does Pandas Use NaN Instead of None for Missing Values?

Understanding the Distinction Between NaN and None in Pandas

When working with pandas to read data from a CSV file, it's essential to understand the difference between NaN and None, as they represent empty cells differently.

Difference Between NaN and None

• NaN (Not-A-Number): Used in pandas as a placeholder for missing data. It indicates that a value cannot be represented as a number.
• None: A Python keyword used to represent an empty value or the absence of a value. It is not specific to missing numerical data.

In pandas, NaN is assigned to empty cells because it allows for consistent representation of missing data across various data types, including floats and objects. This consistency simplifies operations involving missing data.

Why NaN Instead of None?

The primary reason for using NaN over None in pandas is efficiency. NaN can be stored as a float64 data type, which is more efficient than the object data type required for None. This efficiency advantage becomes more apparent when working with large datasets.

Checking for Empty Cells

To check for empty cells, use the isna or notna functions from pandas. These functions can be used with any data type and will return a boolean mask indicating missing values.

Sample Code:

<code class="python">import pandas as pd

df = pd.read_csv('data.csv')

# Check for missing values
missing_values = df.isna()</code>

The missing_values variable will be a boolean mask indicating missing values in the DataFrame.

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