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.
This is a part of the course
“Data Manipulation with pandas”
Exercise instructions
- Use Boolean conditions, not
.isin()or.loc[], and the full date"yyyy-mm-dd", to subsettemperaturesfor rows where thedatecolumn is in 2010 and 2011 and print the results. - Set the index of
temperaturesto thedatecolumn and sort it. - Use
.loc[]to subsettemperatures_indfor rows in 2010 and 2011. - Use
.loc[]to subsettemperatures_indfor rows from August 2010 to February 2011.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Use Boolean conditions to subset temperatures for rows in 2010 and 2011
temperatures_bool = ____[(____ >= ____) & (____ <= ____)]
print(temperatures_bool)
# Set date as the index and sort the index
temperatures_ind = temperatures.____.____
# Use .loc[] to subset temperatures_ind for rows in 2010 and 2011
print(____)
# Use .loc[] to subset temperatures_ind for rows from Aug 2010 to Feb 2011
print(____)