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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

Real Opportunities to Get a Job in Data Analytics

In my recent analysis, I have found that a lot of jobs will be created in big data analysis area. I have listed the real opportunities here. I have collected a few of the things, and I am sharing with you.

Opportunities ahead to get a job 

  • The huge volume of data created by users from multiple devices in a variety of formats. 
  • Need specialized skills to analyze the data, and to get predictive results.
  • The tools developed by SAP, IBM, and Oracle provide multiple opportunities to start a career in data analytics. 

 Video on job opportunities


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