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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
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Data Vault Top benefits Useful to Your Project

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Data Vault 2.0 (DV2) is a system of business intelligence that includes: modeling, methodology, architecture, and implementation best practices.
The benefits of Data Vault
The components, also known as the pillars of DV2 are identified as follows:
data vault
  • DV2 Modeling (changes to the model for performance and scalability)
  • DV2 Methodology (following Scrum and agile best practices)
  • DV2 Architecture (including NoSQL systems and Big Data systems)
  • DV2 Implementation (pattern-based, automation, generation Capability Maturity Model Integration [CMMI] level 5)
There are many special aspects of Data Vault, including the modeling style for the enterprise data warehouse. The methodology takes commonsense lessons from software development best practices such as CMMI, Six Sigma, total quality management (TQM), Lean initiatives, and cycle-time reduction and applies these notions for repeatability, consistency, automation, and error reduction.

Each of these components plays a key role in the overall success of an enterprise data warehousing project. These components are combined with industry-known and time-tested best practices ranging from CMMI to Six Sigma, TQM (total quality management) to Project Management Professional (PMP).

Data Vault 1.0

Data Vault 1.0 is highly focused on just the data modeling section, while DV2 encompasses the effort of business intelligence. The evolution of Data Vault extends beyond the data model and enables teams to execute in parallel while leveraging Scrum agile best practices.

Data Vault 2.0

DV2 architecture is designed to include NoSQL (think: Big Data, unstructured, multistructured, and structured data sets). Seamless integration points in the model and well-defined standards for implementation offer guidance to the project teams.

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