Showing posts with the label soa

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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

How Micro-services differ from SOA

Here you will know the differences between microservices and SOA. Both are different architectures. 1. Micro-services  Microservices are interconnected using simple API You can develop highly scalable and modular applications Service-based architecture It is distributed architecture Here, security is a big challenge. Since there is no middleware Functional services, basically this kind No coordination between services. 2. SOA Service-based architecture It is distributed architecture Security is good It is an infrastructure kind of service Links and References Popular differences between Micro-Services and SOA models Architecture for microservices