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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

Here is an Audio Post Explained About Blockchain

According to Investopedia - Originally developed as the accounting method for the virtual currency Bitcoin , blockchains – which use what's known as distributed ledger technology (DLT) – are appearing in a variety of commercial applications today. Distributed Ledger 1 - What is the Current Trend All the transactions currently can be edited by the server owners. They have full control to change the transaction details. The current trend is either centralized or decentralized. 2- Distributed Trend No one can edit the transaction details. It is transparent to all stakeholders. Video on Distributed Systems References What is Centralized Server Processing Role of Distributed Server Processing