March 2020
Beginner to intermediate
352 pages
8h 40m
English
Content preview from Hands-On Exploratory Data Analysis with Python
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Handling missing data
Whenever there are missing values, a NaN value is used, which indicates that there is no value specified for that particular index. There could be several reasons why a value could be NaN:
- It can happen when data is retrieved from an external source and there are some incomplete values in the dataset.
- It can also happen when we join two different datasets and some values are not matched.
- Missing values due to data collection errors.
- When the shape of data changes, there are new additional rows or columns that are not determined.
- Reindexing of data can result in incomplete data.
Let's see how we can work with the missing data:
- Let's assume we have a dataframe as shown here:
data = np.arange(15, 30).reshape(5, ...
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