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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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RDBMS Vs Key-value Four Top Differences

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This post tells you differences between rdbms and distributed key-value storage.

Rdbms is quite  different from key-value storage.

RDBMS Vs Key-value Four Top Differences

RDBMS (Relational Database)

  1. You have already used a relational database management system — a storage product that's commonly referred to as RDBMS. 
  2. It is basically a structured data.
  3. RDBMS systems are fantastically useful to handle moderate data.
  4. The BIG challenge is in scaling beyond a single server. 
  5. You can't maintain redundant data in rdbms.
  6. All the data available on single server.
  7. The entire database runs on single server. So when server is down then database may not be available to normal business operations.
  8. Outages and server downs are common in this rdbms model of database.

Key-Value Database

  1. Key-value storage systems often make use of redundancy within hardware resources to prevent outages. This concept is important when you're running thousands of servers because they're bound to suffer hardware breakdowns. 
  2. Multiple copies same data available on multiple servers.
  3. The use of redundancy makes the key-value system always available — and, more importantly, your data is always available because it's protected from hardware outages.
  4. Literally, dozens of key-value storage products are available. Many of them were first developed by so-called webscale companies, such as Facebook and LinkedIn, to ensure that they can handle massive amounts of traffic. 
  5. Currently key-value storages under open source licenses are available. Now you (or anyone else) can use them in other environments too.

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