Enhanced Test Fixtures for Comprehensive Data Frame Testing: More on Mixed Types, Time zones and More #59746
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Here, multiple changes have been made to the script with an aim of enhancing the fixtures that are used in data manipulation and analysis in the test. First of all, the `datetime_frame` fixture was adjusted so that it generates a DataFrame with the index of DatetimeIndex type and four columns named ‘A’, ‘B’, ‘C’, ‘D’. This DataFrame now employs the use of np. random. default_rng(2). standard_normal to generate random float values which are more coherent and reproducible. The index is a daily index starting from “2000-01-01” with business day frequency which makes it a more realistic data for testing. The `float_string_frame` fixture was modified to have a new column contain constant string values alongside columns that contain float type values. This improvement makes it possible to consider the test scenarios when the data is represented both numerically and categorically in one DataFrame. To make the DataFrames more similar to real world data, a unique string is used as the index. The `mixed_float_frame` fixture was enhanced to contain different float types, including `float32`, `float64`, and `float16`. This change is useful to test operations where different float precisions are used and where cross types compatibility is necessary. For integer data the `mixed_int_frame` fixture has been enriched with columns of different integer kinds: `int32`, `uint64`, `uint8`, `int64`. This addition facilitates the testing functions that deal with different integer size and type to ensure that there is enough set of integer-based data to validate. Finally, the ‘timezone_frame’ fixture was incorporated to deal with time intervals of a different timezone. This fixture generates a DataFrame with some missing data, which is a common case in time zone transformations, and thus these data will be used in the next sections for testing purposes. In general, these changes improve the script’s capability of producing and evaluating different types of data and formats, thus increasing its effectiveness in different data analysis and manipulation tasks.
Improved Test Fixtures for Different Types of Data and Time Zones
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Going to assume this was AI generated like fastai/fastai#4048. Closing |
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Testing of various Data Frame operations and manipulations is now enhanced by a rich set of fixtures designed for various use cases.
This is because this fixture is very vital in testing of functions that involve time-based indexing and date manipulation. It is a random float generator with the values of which are indexed by the business dates thus making it possible to simulate the real-time data processing.
This fixture has a column of strings together with numerical values in it, to serve the purpose of a data type with string and numeric column. The Data Frame has a unique string index and an additional column which contains a constant string “bar” to test string operations and check the stability.
This setup allows to test how various float precision and their conversions are being dealt with in Data Frames for proper calculations and types management.
This fixture is quite essential when testing operations that involve different integer types and integer-related calculations.
It is especially helpful in testing time zone conversion and handling of missing data so that the time-based data gets processed properly in all the time zones.
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.