Glossary

Key Points

Introduction	Pandas is a Python library that operates efficiently against large datasets. Datetime indexing with Pandas enables resampling of timeseries data using different time steps.
Reading Data Files with Python	PANDAS is a Python library designed to work with large datasets. Use `concat()` to concatenate tabular dataframes that have the same structure.
Subsetting Dataframes with PANDAS	Rows can be selected in Pandas using label or position based indexing. Boolean indexing and the `query()` method can be used to select subsets of a dataframe using conditions.
Summarizing Tabular Data with PANDAS	Use the PANDAS `describe()` method to generate descriptive statistics for a dataframe.
Creating a Datetime Index with Pandas	Use `resample()` on a datetime index to calculate aggregate statistics across time series.

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