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Showing posts with the label differences between nosql and sql database

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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

RDBMS Vs NOSQL awesome differences to read now

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NoSQL and RDBMS or SQL are different from each other. You may ask what is the difference. Below explained in a way that you can understand quickly. 💡Traditional Database A schema is required. All traditional data warehouses using RDBMS to store datamarts. Databases understand SQL language. It has a specific format and rules to interact with traditional databases. Less scalable. It has certain limitations.  Expensive to make the databases as scalable Data should be in a certain format. Data stored in row format. NoSQL database The growing internet usage and involving a number of devices caused to invent databases that have the capability to store any kind of data. More: MongoDB 3.2 fundamentals for Developers-Learn with Exercises NoSQL Special Features The schema is not required. Ability to handle multiple data types. This is the power of NoSQL. NoSQL is much suitable for analytical databases. Since those should be flexible, scalable, and able to store any f