toolkit, Release 1.3.2 Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that locations available in the air_quality (left) table, i.e. FR04014, BETR801 and London Westminster, end up in the resulting table. The merge function supports multiple join options similar to database-style Timestamp objects useful? Let’s illustrate the added value with some example cases. What is the start and end date of the time series data set we are working with? In [9]: air_quality["datetime"].min(), air_quality["datetime"]

Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Running . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Running Google BigQuery Integration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Running the vbench performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 pandas.Series.dt.is_month_end . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 pandas.Series.dt.is_quarter_start

Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Running . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Running Google BigQuery Integration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Running the vbench performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260 pandas.Series.dt.is_month_end . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260 pandas.Series.dt.is_quarter_start