Beyond str.contains(na=False): Alternative Approaches for NaNs in Pandas

The str.contains method in pandas is used to check if a substring exists within a string in a Series (one-dimensional labeled array). However, it can raise errors or produce unexpected results when encountering NaN values.

Approaches to Ignoring NaNs:

1. na=False Argument (pandas v0.24.0 and later):

   • In newer pandas versions (v0.24.0 onwards), the str.contains method provides a na argument to specify how to treat NaNs.
   • Set na=False to consider NaNs as non-matches. This results in False being returned for Series elements containing NaN.

   import pandas as pd
   
   data = pd.Series(['apple', 'banana', np.nan, 'cherry'])
   result = data.str.contains('apple', na=False)
   print(result)  # Output: 0     True
                   #          1    False
                   #          2    False
                   #          3    False
                   #          dtype: bool
   

2. isna() and Conditional Logic (For All Pandas Versions):

   • This approach works universally across pandas versions.
   • Use isna() to identify NaN values in the Series.
   • Create a new Series with the desired logic (e.g., False for NaNs) using a conditional expression.

   import pandas as pd
   import numpy as np
   
   data = pd.Series(['apple', 'banana', np.nan, 'cherry'])
   is_nan = data.isna()
   result = data[~is_nan].str.contains('apple')  # Exclude NaNs first
   result = result.append(pd.Series([False]))  # Add False for the NaN element
   print(result)  # Output: 0     True
                   #          1    False
                   #          2    False
                   #          3    False
                   #          dtype: bool
   

Choosing the Right Approach:

• If you're using pandas v0.24.0 or later, the na=False argument is the most concise and convenient method.
• For compatibility across versions, or if you need more complex handling of NaNs, the isna() and conditional logic approach provides flexibility.

import pandas as pd

data = pd.Series(['apple', 'banana', np.nan, 'cherry'])
result = data.str.contains('apple', na=False)  # Set na=False to ignore NaNs
print(result)

# Output:
# 0     True
# 1    False
# 2    False  # NaN is considered False
# 3    False
# dtype: bool

import pandas as pd
import numpy as np

data = pd.Series(['apple', 'banana', np.nan, 'cherry'])
is_nan = data.isna()  # Identify NaN values

# Create a new Series with desired logic (False for NaNs)
result = data[~is_nan].str.contains('apple')  # Exclude NaNs first

# Add False for the NaN element to maintain Series length
result = result.append(pd.Series([False]))

print(result)

# Output:
# 0     True
# 1    False
# 2    False  # NaN is handled explicitly
# 3    False
# dtype: bool

• If your goal is to impute (fill in) missing values with the previous valid value before performing the search, you can use fillna(method='ffill') before str.contains. This is useful when NaNs represent missing data and you want to use the context from the previous element.

import pandas as pd

data = pd.Series(['apple', 'banana', np.nan, 'cherry'])
filled_data = data.fillna(method='ffill')  # Fill NaN with previous value
result = filled_data.str.contains('apple')
print(result)

# Output (may vary depending on the data):
# 0     True
# 1    False
# 2     True  # NaN replaced with 'apple'
# 3    False
# dtype: bool

Note: This approach might not be ideal if NaNs represent something different than missing data.

User-Defined Function (For Complex Handling):

• For highly customized logic or specific value handling of NaNs, you can create a user-defined function. This function can take the Series element as input and return True, False, or a different value based on your requirements.

import pandas as pd

def handle_nan(value):
    if pd.isna(value):
        return 'not_applicable'  # Return a specific value for NaNs
    else:
        return value.str.contains('apple')  # Perform the search for non-NaNs

data = pd.Series(['apple', 'banana', np.nan, 'cherry'])
result = data.apply(handle_nan)
print(result)

# Output (depending on your function):
# 0     True
# 1    False
# 2  not_applicable
# 3    False
# dtype: object