#### Overview of this book

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
Preface
Pandas Foundations
Free Chapter
Essential DataFrame Operations
Creating and Persisting DataFrames
Exploratory Data Analysis
Selecting Subsets of Data
Combining Pandas Objects
Visualization with Matplotlib, Pandas, and Seaborn
Debugging and Testing Pandas
Other Books You May Enjoy
Index

# Continuous data

My broad definition of continuous data is data that is stored as a number, either an integer or a float. There is some gray area between categorical and continuous data. For example, the grade level could be represented as a number (ignoring Kindergarten, or using 0 to represent it). A grade column, in this case, could be both categorical and continuous, so the techniques in this section and the previous section could both apply to it.

We will examine a continuous column from the fuel economy dataset in this section. The `city08` column lists the miles per gallon that are expected when driving a car at the lower speeds found in a city.

## How to do it…

1. Pick out the columns that are numeric (typically `int64` or `float64`):
``````>>> fueleco.select_dtypes("number")
barrels08  barrelsA08  ...  phevHwy  phevComb
0      15.695714         0.0  ...        0         0
1      29.964545         0.0  ...        0         0
2     ...``````