Python | Pandas Index.inferred_type

The Index.inferred_type method is a Pandas method that returns the type of data stored in the index. It can be used on any Pandas index, whether it is a series index or a DataFrame index. This method is particularly useful when dealing with large datasets where you want to know the type of data being stored in an index.

Syntax

Index.inferred_type

Parameters

None

Return Value

The Index.inferred_type method returns a string representing the inferred data type of the index. It can be one of the following values:

• "integer": the index contains integer values
• "floating": the index contains floating-point values
• "mixed": the index contains both integer and floating-point values
• "unicode": the index contains Unicode strings
• "complex": the index contains complex values
• "boolean": the index contains boolean values
• "datetime": the index contains datetime values
• "timedelta": the index contains timedelta values
• "categorical": the index contains categorical values
• "interval": the index contains interval values
• "period": the index contains period values

Examples

import pandas as pd

# create an integer index
int_index = pd.Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# get the inferred type of the index
print(int_index.inferred_type)
# output: integer

# create a mixed index
mixed_index = pd.Index([1, 2.0, 3, 4.5, 5, 6, 7.5, 8, 9, 10.0])

# get the inferred type of the index
print(mixed_index.inferred_type)
# output: mixed

# create a datetime index
date_index = pd.date_range("2020-01-01", periods=5)

# get the inferred type of the index
print(date_index.inferred_type)
# output: datetime

In the above example, we have created three different types of indexes and used the inferred_type method to get the type of data stored in each of them.

Conclusion

The Index.inferred_type method is a powerful tool that enables us to quickly determine the type of data being stored in an index. It can be particularly useful when dealing with large datasets where it is not always easy to visually inspect the data. The returned value can be used to validate that the data stored in the index conforms to our expectations, which in turn can help prevent errors and potential data inconsistencies.