From cff2467f0f82770aa19178bdba0e03403197c621 Mon Sep 17 00:00:00 2001 From: Allen Lee Date: Mon, 4 Mar 2024 17:38:25 -0700 Subject: [PATCH] fix: clarify pandas usage with non-numeric columns pandas now raises errors when computing the mean on a dataframe with non-numeric columns add a note to the challenge describing the issue and provide a few additional ways of solving it Thanks to @davidwilby for finding this bug and outlining possible solutions to it in #670 Co-authored-by: David Wilby Co-authored-by: Olav Vahtras --- episodes/14-looping-data-sets.md | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/episodes/14-looping-data-sets.md b/episodes/14-looping-data-sets.md index ec19be367..6e95b24c3 100644 --- a/episodes/14-looping-data-sets.md +++ b/episodes/14-looping-data-sets.md @@ -180,7 +180,10 @@ What other special strings does the [`float` function][float-function] recognize Write a program that reads in the regional data sets and plots the average GDP per capita for each region over time -in a single chart. +in a single chart. Pandas will raise an error if it encounters +non-numeric columns in a dataframe computation so you may need +to either filter out those columns or tell pandas to ignore them. + ::::::::::::::: solution @@ -200,8 +203,17 @@ for filename in glob.glob('data/gapminder_gdp*.csv'): # we will split the string using the split method and `_` as our separator, # retrieve the last string in the list that split returns (`.csv`), # and then remove the `.csv` extension from that string. + # NOTE: the pathlib module covered in the next callout also offers + # convenient abstractions for working with filesystem paths and could solve this as well: + # from pathlib import Path + # region = Path(filename).stem.split('_')[-1] region = filename.split('_')[-1][:-4] - dataframe.mean().plot(ax=ax, label=region) + # pandas raises errors when it encounters non-numeric columns in a dataframe computation + # but we can tell pandas to ignore them with the `numeric_only` parameter + dataframe.mean(numeric_only=True).plot(ax=ax, label=region) + # NOTE: another way of doing this selects just the columns with gdp in their name using the filter method + # dataframe.filter(like="gdp").mean().plot(ax=ax, label=region) + plt.legend() plt.show() ``` @@ -231,8 +243,8 @@ gapminder_gdp_africa .csv ``` -**Hint:** It is possible to check all available attributes and methods on the `Path` object with the `dir()` -function! +**Hint:** Check all available attributes and methods on the `Path` object with the `dir()` +function. ::::::::::::::::::::::::::::::::::::::::::::::::::