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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

Data Security - Safetly Storing Data In Cloud

What exactly is the cloud? It is basically the collection of computers on the internet that companies are using to offer their services.  One cloud service that is being offered is a revolutionary storage method for your data. From music files to pictures to sensitive documents, the cloud invisibly backs up your files and folders  and alleviates the potentially endless and costly search for extra storage space. An alternative to buying an external hard drive or deleting old files to make room for new ones, cloud storage is convenient and cost-effective.

It works by storing your files on a server out in the internet somewhere rather than on your local hard drive.  (For a more technical discussion of cloud computing basics, read more here.) 

This allows  you to back up, sync, and access your data across multiple devices as long as they have internet capability.

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