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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

Big data real role to help Real estate business

How big data helps real estate is trending today. When people buy real estate and its dependencies you can get from analytics

Advantages of Big-data in Real estate

  1. Study the data from real estate consume
  2. Understand the buyers
  3. Loan dependencies and role of consumers
  4. Sale activities by agents
  5. Sales boost

Role of Big Data

Real estate agents need to check lot of data sources to identify sales pitch and formula to boost sales. The first point is agents should understand the requirements of consumers or buyers of real estate.



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