|
Reading and Working with Tabular Data
|
Use pandas.read_* and pandas.DataFrame.to_* to import / export data.
The method info describes the data frame object.
The method describe summarizes value distributions in columns.
You can use labels or index locations to select both subsets and elements from your data frame.
Selection using conditions is very powerful.
Selections create views on your original data.
Use mean, max, min, and others to calculate simple statistics.
Use split-apply-combine to calculate statistics within groups in a data frame.
|
|
Visualize Your Data
|
Pandas provides quick ways to create simple visualizations.
A layered grammar of graphics implementation provides a structured approach to plotting.
A good implementation can make expressing complex visualizations straightforward.
|
|
Machine Learning
|
Preparing data in the right format is often the hardest task.
Machine learning provides methods for tasks such as dimensionality reduction, clustering, classification, and anomaly detection.
Having good visualizations is crucial for interpretation.
|