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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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4 Essential Points on Modern Data Warehouse 2.0

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The new data warehouse often called “Data Warehouse 2.0,” is the fast-growing trend of doing away with the old idea of huge, off-site, mega-warehouses stuffed with hardware and connected to the world through huge trunk lines and big satellite dishes.
modern data warehouse

How Modern Data warehouse You Can Implement

The replacement is very different from that highly controlled, centralized, and inefficient ideal towards a more cloud-based, decentralized preference of varied hardware and widespread connectivity.

In today’s world of instant, varied access by many different users and consumers, data is no longer nicely tucked away in big warehouses.

How Data Will Be Stored in Modern Data Warehouse

Instead, it is often stored in multiple locations (often with redundancy) and overlapping small storage spaces that are often nothing more than large closets in an office building. 

The trend is towards always-on, always-accessible, and very open storage that is fast and friendly for consumers yet complex and deep enough to appease the most intense data junkie.

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