Counting Missing Data
Counting Missing Data
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If you have a large dataframe, and it contains a few missing values (None or a numpy.NaN), then you can find the count of such missing value across the given label. For this purpose, you can use either of the following two analogous functions :
If you have a large dataframe, and it contains a few missing values (None or a numpy.NaN), then you can find the count of such missing value across the given label. For this purpose, you can use either of the following two analogous functions :
The functions above are alias of each other and detect missing values by returning the same sized object as that of the calling dataframe, made up of boolean True/False.
The functions above are alias of each other and detect missing values by returning the same sized object as that of the calling dataframe, made up of boolean True/False.
Answers
Note - The seaborn.barplot() is a useful function to keep in mind if your data is summarized and you still want to build a bar chart. If your data is not yet summarized, however, just use the countplot function so that you don't need to do extra summarization work. In addition, you'll see what barplot's main purpose is in the next lesson when we discuss adaptations of univariate plots for plotting bivariate data.
Note - The seaborn.barplot() is a useful function to keep in mind if your data is summarized and you still want to build a bar chart. If your data is not yet summarized, however, just use the countplot function so that you don't need to do extra summarization work. In addition, you'll see what barplot's main purpose is in the next lesson when we discuss adaptations of univariate plots for plotting bivariate data.
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Supporting Materials
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