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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

Storage Node Vs Compute Node

Here are the differences between compute node vs storage node Nodes are two types. Those are compute and storage. The compute node process business logic whereas the storage node stores the data.

Compute node Vs. Storage node

compute node vs storage node

Compute Node

  • A computer (machine) where you can execute actual business logic.
  • The two parameters it might have are RAM and CPU.

Compute node

Storage Node

  • Stores the processing-data where your file system resides
  • Compute and storage nodes you can find at one location.
  • It designates block storage.


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