Alternatives to Nullable Booleans in Pandas DataFrames

• Integer Arrays for Nullable Booleans (Experimental)
  Pandas offers an experimental IntegerArray that can represent integers with missing values. You can use this with the dtype argument during creation. These arrays use pandas.NA specifically for missing values, unlike standard NaN.
• Missing Boolean Values
  Pandas uses NaN (Not a Number) to represent missing data, which is typically a float type. This can be inconvenient when working with integer data types like booleans (True or False).

---

import pandas as pd

# Create a Series with boolean and missing values (using NaN)
data = [True, False, None, True]
s = pd.Series(data, dtype=bool)

# Check data types and missing values
print(s.dtype)  # Output: bool
print(s.isna())  # Output: shows True for the missing value (None)

# Operations with missing values might propagate NaN
result = s & pd.Series([True, False, True, None])
print(result)  # Output: 0    True
                 #        1    False
                 #        2     NaN
                 #        3     NaN
                 #        dtype: bool

import pandas as pd

# Create a Series with IntegerArray and missing values (using pandas.NA)
data = pd.array([True, False, pd.NA, True], dtype="Int8")
s = pd.Series(data)

# Check data type and missing values
print(s.dtype)  # Output: Int8 dtype
print(s.isna())  # Output: shows True for the missing value (pandas.NA)

# Operations treat missing values as missing (not NaN)
result = s & pd.Series([True, False, True, None])
print(result)  # Output: 0    True
                 #        1    False
                 #        2     NA
                 #        3     NA
                 #        dtype: Int8

---

1. Object dtype with String Encoding
   

• This method allows clear differentiation between missing values and actual boolean values (True/False).
• You can then encode True as "True", False as "False", and missing values as a specific string (e.g., "NA", "Unknown").
• This approach uses the object dtype for your boolean column.

Example

import pandas as pd

data = ["True", "False", None, "True"]
df = pd.DataFrame({"col": data})

# Define functions for converting between strings and booleans
def str_to_bool(value):
  if value == "True":
    return True
  elif value == "False":
    return False
  else:
    return None  # Handle missing values

def bool_to_str(value):
  if value is True:
    return "True"
  elif value is False:
    return "False"
  else:
    return None  # Handle missing values

# Apply functions to convert between data types
df["col"] = df["col"].apply(str_to_bool)

# Use the column for operations
result = df["col"] & pd.Series(["True", "False", "True", None])

# Optionally convert back to strings for specific needs
df["col"] = df["col"].apply(bool_to_str)

1. Custom Categorical Dtype
   

• This method ensures data consistency and allows filtering/aggregation based on categories.
• You can create a custom categorical dtype with categories like "True", "False", and "NA".

Example (using pd.api.types.CategoricalDtype)

import pandas as pd

data = ["True", "False", None, "True"]
categories = ["True", "False", "NA"]
df = pd.DataFrame({"col": pd.Categorical(data, categories=categories)})

# Use .cat accessor for filtering/aggregation
filtered_df = df[df["col"].cat.codes != -1]  # Filter out missing values (-1 code)

1. Integer Encoding (Similar to IntegerArray)
   

• This approach is space-efficient but requires custom logic for interpretation.
• Assign numerical values (e.g., 1 for True, 0 for False, -1 or None for missing).

Example

import pandas as pd

data = [True, False, None, True]
mapping = {"True": 1, "False": 0, None: -1}
df = pd.DataFrame({"col": [mapping.get(x) for x in data]})

# Define functions for encoding/decoding
def encode_bool(value):
  return mapping.get(value)

def decode_bool(value):
  if value == 1:
    return True
  elif value == 0:
    return False
  else:
    return None

# Apply functions to convert between data types (if needed)

# Operations treat missing values as -1 (or None)
result = df["col"] + pd.Series([1, 0, 1, None])  # Addition used for demonstration

Choosing the best alternative depends on your specific needs. Consider factors like:

• Functionality
  Categorical dtype allows advanced filtering/aggregation based on categories.
• Performance
  Integer encoding can be more efficient for large datasets.
• Readability
  String encoding might be easier to understand for users not familiar with the code.