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How to Check Column Nulls and Replace: Pandas

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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

The In-and-Out of Nodes in Blockchain

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Blockchain is a decentralized technology or distributed ledger on which transactions are anonymously recorded. Which means the transaction ledger is maintained simultaneously across a network of unrelated computers or servers called “nodes”, like a spreadsheet that is duplicated thousands of times across a network of computers. The ledger contains a continuous and complete record (the “chain”) of all transactions performed which are grouped into blocks A block is only added to the chain if the nodes, which are members in the blockchain network with high levels of computing power, reach consensus on the next ‘valid’ block to be added to the chain. A transaction can only be verified and form part of a candidate block if all the nodes on the network confirm that the transaction is valid. Related 11 Useful Blockchain Advantages to Read now Blockchain Smart Contract The Perfect Example