Another useful method to visualize and make some of the initial analysis of the data is resampling, smoothing, and other rolling estimates. When resampling, a frequency keyword needs to be passed to the function. This is a combination of integers and letters, where the letters signify the type of the integer. To give you an idea, some of the frequency specifiers are as follows:
B
, business, or D
, calendar day
W
, weekly
M
, calendar month end or MS
for start
Q
, calendar quarter end or QS
for start
A
, calendar year end, or AS
for start
H
, hourly, T
, minutely
Most of these can be modified by adding a B
at the start of the specifier to change it to Business (month, quarter, year, and so on), and there are a few other keywords/descriptors that can be found in the Pandas documentation. Now let's try some of these out in the following examples. As this chapter contains several real-world data examples, which we use to highlight different things, feel free...