Boolean Indexing with NaN Values
When working with boolean indexing in pandas, it’s not uncommon to encounter NaN values that can cause issues with the resulting output. In this article, we’ll explore how to return boolean indexing Nan values as NaN and not false.
Understanding Boolean Indexing
Boolean indexing is a powerful feature in pandas that allows us to subset rows or columns of a DataFrame based on conditions. The basic syntax for boolean indexing is:
df[condition]
where condition is a boolean Series that defines the selection criteria.
For example, let’s say we have a DataFrame with the following structure:
| Date | 7days_Avg |
|---|---|
| 2017-02-23 | -0.085974 |
| 2017-02-24 | -0.067239 |
| … | … |
We can use boolean indexing to select rows where the 7days_Avg column is greater than a certain value, say 0.02.
df[df['7days_Avg'] > 0.02]
This would return a new DataFrame with only the rows where the 7days_Avg value is greater than 0.02.
The Issue with NaN Values
However, when we encounter NaN values in our data, things can get complicated. In boolean indexing, NaN values are treated as False, which means that if we include NaN values in our condition, they will not be included in the resulting output.
To illustrate this, let’s modify our previous example to include a few NaN values:
| Date | 7days_Avg |
|---|---|
| 2017-02-23 | -0.085974 |
| 2017-02-24 | -0.067239 |
| 2017-03-14 | NaN |
| 2017-03-15 | NaN |
If we try to use boolean indexing with a condition that includes these NaN values, we get the following output:
df[(df['7days_Avg'] > 0.02) & (df['7days_Avg'].notnull())]
| Date | 7days_Avg |
|---|---|
| 2017-02-23 | -0.085974 |
| 2017-02-24 | -0.067239 |
As you can see, the rows with NaN values are missing from the output.
The Solution
To fix this issue, we need to modify our boolean indexing condition to include NaN values as well. One way to do this is by using the .notnull() method to select only the non-NaN values in the 7days_Avg column.
sda = df['7days_Avg']
df.loc[sda.notnull(), 'Bullish'] = (sda > 0.02).map(int)
In this code, we first create a boolean mask sda that selects only the non-NaN values in the 7days_Avg column using the .notnull() method. We then use this mask to subset the rows where the 7days_Avg value is greater than 0.02.
By doing so, we ensure that NaN values are treated as False and included in the resulting output.
Additional Considerations
When working with boolean indexing, it’s essential to consider the following:
- NaN values: As discussed earlier, NaN values are treated as False in boolean indexing. This means that if you include NaN values in your condition, they will not be included in the resulting output.
- Data type: Make sure that your boolean mask is of the correct data type. In this case, we use the
.notnull()method to create a boolean mask from a Series of numbers. - Missing values: If you have missing values in your data, it’s essential to handle them appropriately when using boolean indexing.
By understanding these considerations and using techniques like the one presented above, you can effectively work with boolean indexing and NaN values in pandas.
Last modified on 2023-08-07