Slicing time series

Slicing is particularly useful for time series since it's a common thing to want to filter for data within a date range. Add the date column to the index, then use .loc[] to perform the subsetting. The important thing to remember is to keep your dates in ISO 8601 format, that is, "yyyy-mm-dd" for year-month-day, "yyyy-mm" for year-month, and "yyyy" for year.

Recall from Chapter 1 that you can combine multiple Boolean conditions using logical operators, such as &. To do so in one line of code, you'll need to add parentheses () around each condition.

pandas is loaded as pd and temperatures, with no index, is available.

Use Boolean conditions, not .isin() or .loc[], and the full date "yyyy-mm-dd", to subset temperatures for rows in 2010 and 2011 and print the results.
Set the index of temperatures to the date column and sort it.
Use .loc[] to subset temperatures_ind for rows in 2010 and 2011.
Use .loc[] to subset temperatures_ind for rows from Aug 2010 to Feb 2011.

Exercise

Slicing time series

Instructions