Join Michele Vallisneri for an in-depth discussion in this video Smoothing data, part of Introduction to Data Analysis with Python.
- Although we have now filled in…the missing data, our temperature series…are still very noisy.…To see trends on longer time scales…we need to average out short-term fluctuations.…This is know, technically, as smoothing.…So we will learn how to smooth data…using a simple running mean nampy.…And we will also see how to place…several slots side by side…using matplotlib.…Let's go to the IPython notebook,…and let's load the 05_05_smoothing_begin…IPython notebook.…
Where I have copied all the code…that we have developed so far.…I did comment out one line…that resulted in an error before.…So let's evaluate all cells,…and let's look at one of these plots again.…Indeed, it's very noisy.…It's basically a big block of pixels.…To get a better result,…we will smooth out short-term oscillations.…There are many ways to do it…but generally, it involves averaging other nearby values.…For simplicity, we will just take a running mean,…that is a mean over a limited window…centered at the data point.…
We can dial up or down the size of the window…
- Writing and running Python in iPython
- Using Python lists and dictionaries
- Creating NumPy arrays
- Indexing and slicing in NumPy
- Downloading and parsing data files into NumPy and Pandas
- Using multilevel series in Pandas
- Aggregating data in Pandas
Skill Level Intermediate
1. Installation and Setup
2. Refresher: Data Containers in Python
3. Word Anagrams in Python
4. Introduction to NumPy
5. Weather Data with NumPy
6. Introduction to Pandas
7. Baby Names with Pandas
Next steps1m 36s
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.