Richie CottonSenior Data Evangelist, DataCamp · Ask me about data and AI upskilling

Python Tutorial: pandas Foundations (part 4)

Name: Python Tutorial: pandas Foundations (part 4)
Uploaded: 2017-03-06T22:38:37+00:00
Duration: 1 min 14 s
Channel: Richie Cotton
Description: Load and clean two messy weather datasets for Austin: 30 years of climate normals from NOAA and 2011 hourly observations. You'll use pandas.read_csv…

6 Mar 2017 · 1 min

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1:14

Load and clean two messy weather datasets for Austin: 30 years of climate normals from NOAA and 2011 hourly observations. You'll use pandas.read_csv options to handle missing values and parse dates, then prep both files so you can compare them.

Chapters

It's time to pull together the skills you've learned so far. You're going to work now with some messy weather and climate data. For this case study, you'll be comparing observed weather data from two sources. The first is the climate normals of Austin, Texas from 1981 to 2010 from the national oceanic and Atmospheric Administration. This dataset consists of climate measurements for each hour of the day averaged over 30 years. The second is from Austin weather data from 2011. This also has hourly readings of many climate related measurements like temperature, dew point and so on. It has many unrelated measurements, missing values and so forth. It does need to be cleaned up somewhat. You've already seen how to read data from CSV files using the PANDAS READCSV function. Remember, this includes many options for labeling columns, specifying an index, parsing dates and interpreting missing values. Your first job in the exercises is to get those data frames in usable form for analysis. Go ahead and get started with that now.