Pandas - Tag - 300.Wattshttps://300watts.me/tags/pandas/Pandas - Tag - 300.WattsHugo -- gohugo.ioenmorristai01@gmail.com (Morris)morristai01@gmail.com (Morris)This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.Sat, 26 Aug 2017 11:16:25 +0800Pandas ufuncs Tips and Trickshttps://300watts.me/posts/pandas-ufuncs-tips-and-tricks/Sat, 26 Aug 2017 11:16:25 +0800Morrishttps://300watts.me/posts/pandas-ufuncs-tips-and-tricks/

Why are pandas ufuncs recommended over apply?

Pandas has an apply function that lets you run arbitrary functions across every value in a column. The catch is that apply is only marginally faster than a plain Python loop. That’s why pandas’ built-in ufuncs are the preferred choice for column preprocessing.
ufuncs are special functions built on top of numpy and implemented in C, which is why they’re so fast. Below we introduce several examples of ufuncs: .diff, .shift, .cumsum, .cumcount, .str commands (for strings), and .dt commands (for dates).

]]>Pandas cut and qcut Functionshttps://300watts.me/posts/pandas-cut-and-qcut-functions/Sat, 05 Aug 2017 11:46:56 +0800Morrishttps://300watts.me/posts/pandas-cut-and-qcut-functions/When we have continuous numerical values, we can discretize them using cut and qcut. The cut function bins values by numeric intervals, while qcut bins them by quantiles. In other words, cut produces bins of equal length, while qcut produces bins of equal size.

Suppose we have the ages of a group of people:
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32, 101]
If we want to discretize this list into “18 to 25”, “25 to 35”, “35 to 60”, and “60 and above”, we can use the cut function:

]]>