How to use Resample in Pandas to enhance your time series data analysis
When it comes to time series analysis, resampling is a critical technique that allows you to flexibly define the resolution of the data you want. You can either increase the frequency like converting 5-minute data into 1-minute data (upsample, increase in data points), or you can do the other way around (downsample, decrease in data points).
Quoting the words from documentation, resample is a “Convenient method for frequency conversion and resampling of time series.”
In practice, there are 2 main reasons why using resample.
Without further ado, let’s get our hands dirty and learn it from hands-on practice!
Many a time, I have seen beginners in data science skip exploratory data analysis (EDA) and jump straight into building a hypothesis function or model. In my opinion, this should not be the case.
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Pandas is a popular Python package as it offers powerful, expressive, and flexible data structures that make data manipulation and analysis easy, among many other things. We will go over the essential bits of information about pandas, including how to install it, its uses, and how it works, and much more.
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Learn to group the data and summarize in several different ways, to use aggregate functions, data transformation, filter, map.