Data Analysis with Python and Pandas - Handling Missing Data - p.10 Data Analysis with Python and Pandas Tutorial

Welcome to Part 10 of our Data Analysis with Python and Pandas tutorial. In this part, we're going to be talking about missing or not available data. We have a few options when considering the existence of missing data. Ignore it - Just leave it there Delete it - Remove all cases. Remove from data entirely. This means forfeiting the entire row of data. Fill forward or backwards - This means taking the prior or following value and just filling it in. Replace it with something static - For example, replacing all NaN data with -9999. Each of these options has their own merits for a variety of reasons. Ignoring it requires no more work on our end. You may choose to ignore missing data for legal reasons, or maybe to retain the utmost integrity of the data. Missing data might also be very important data. For example, maybe part of your analysis is investigating signal drops from a server. In this case, maybe the missing data is super important to keep in the set. Next, we have delete it. You have another two choices at this point. You can either delete rows if they contain any amount of NaN data, or you can delete the row if it is completely NaN data. Usually a row that is full of NaN data comes from a calculation you performed on the dataset, and no data is really missing, it's just simply not available given your formula. In most cases, you would at least want to drop all rows that are completely NaN, and in many cases you would like to just drop rows that have any NaN data. Tutorial sample code and text: http://pythonprogramming.net/nan-na-missing-data-analysis-python-pandas-tutorial/ http://pythonprogramming.net https://twitter.com/sentdex