Booleans with Or

When dealing with datasets, especially those involving vehicles, you often need to filter and analyze specific categories. Let's say you're working with a dataset that includes different types of electric vehicles: electric, hybrid electric, and plug-in hybrid electric. To focus on all electric vehicles, you need a method to select rows that match any of these types.

Boolean Operator or

Imagine you want to check if a vehicle is either electric or hybrid electric. In everyday language, you might say, "This vehicle is electric or hybrid electric." This statement holds true if:

• The vehicle is electric.
• The vehicle is hybrid electric.
• The vehicle is both electric and hybrid electric.

In Python, the or operator functions similarly. It returns True if at least one of the conditions it combines is True. Here are a couple of examples:

1. Combining 'electric' == 'electric' with 'biodiesel' == 'hybrid electric':

('electric' == 'electric') or ('biodiesel' == 'hybrid electric')
# Output: True, since the first comparison is True

2. Combining 'propane' == 'electric' with 'biodiesel' == 'hybrid electric':

('propane' == 'electric') or ('biodiesel' == 'hybrid electric')
# Output: False, since neither comparison is True

Applying Boolean Operator or in Pandas

In Pandas, you use the | symbol to perform the or operation. Let's say you have a mini DataFrame df with a fuel column that includes different fuel types:

import pandas as pd

df = pd.DataFrame({
    'fuel': ['electric', 'hybrid electric', 'plug-in hybrid electric', 'propane']
})

To create a Boolean mask that identifies rows where the fuel is either electric or hybrid electric, follow these steps:

1. Create individual masks for each condition:

is_electric = df['fuel'] == 'electric'
is_hybrid = df['fuel'] == 'hybrid electric'

2. Combine the masks using the | operator:

combined = is_electric | is_hybrid

Your resulting mask will look like this:

fuel	combined
electric	True
hybrid electric	True
plug-in hybrid electric	False
propane	False

Extending the Logic

You can combine more than two conditions. For instance, if you also want to include plug-in hybrid electric vehicles, add another condition:

is_plugin = df['fuel'] == 'plug-in hybrid electric'
combined = is_electric | is_hybrid | is_plugin

This expanded mask will include all three types of electric vehicles:

fuel	combined
electric	True
hybrid electric	True
plug-in hybrid electric	True
propane	False

Filtering the DataFrame

To filter your DataFrame to only include rows where the combined mask is True, you can pass the mask to the DataFrame:

filtered_df = df[combined]

Alternatively, you can construct the combined condition directly within the brackets:

filtered_df = df[is_electric | is_hybrid | is_plugin]

In either approach, you get a filtered DataFrame that looks like this:

fuel
electric
hybrid electric
plug-in hybrid electric

Conclusion

Boolean logic with or is a powerful tool in data analysis, allowing you to combine multiple conditions and filter datasets effectively. Whether you're dealing with electric vehicles or any other categories, understanding how to construct and use Boolean masks in Pandas will enable you to manipulate and analyze your data with precision. This process not only simplifies your data analysis tasks but also enhances the accuracy and relevance of your results.