Showing posts with the label hadoop-2x-vs-3x

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How to Check Column Nulls and Replace: Pandas

Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

Hadoop 2x vs 3x top differences

In many interviews, the first question for Hadoop developers is what are the differences between Hadoop 2 and 3. You already know that Hadoop upgraded from version 1. The below list is useful to know the differences. I have given Hadoop details in the form of questions and answers so that beginners can understand. Hadoop 2.x Vs 3.x The major change in hadoop 3 is no storage overhead. So, you may be curious about how Hadoop 3 is managing storage. My plan is for you is first to go through the list of differences and check the references section, to learn more about Hadoop storage management. References Real story of storage management in Hadoop Follow me on twitter Applyanalytics