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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

PostgreSQL is beyond NoSQL database

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The PostegreSQL is a popular database for web applications. You can manage user data in this database conveniently. #postgreSQL What is PostgreSQL Web applications started using NoSQL databases. PostgreSQL is updating their database to meet the requirements of web applications. So PostgreSQL is almost equal to NoSQL database. Java Script PostgreSQL supports  JSON (JavaScript Simple Object Notation) . JSON is portable data format to share data. The MongoDB follows JSON. PostgreSQL's structured format for saving JSON, called JSONB, eliminates the need for restructuring a document before it is committed to the database. Benefits  PostgreSQL is similar to MongoDB to ingest documents  PostgreSQL follows ACID compatibility  PostgreSQL have all the features and options to edit JSON data.