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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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The 5 Skills You need to Start Data Analytics Career

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Data analytics is the job role hot in demand in each organization. Digital skills such as Mobile development, Full-stack development, Data Science, and Cloud computing are successful because they are very user-friendly to the end-users.


skills in-demand

Digital devices enabled digital technologies to cause to generate more data. You need different kinds of tools to analyze data in different formats.

You need the right tools. Else you cannot predict the user's mind. User search data is the source of big retail markets. Based on these search words, they start selling the products.

The motto behind data analytics is to get the benefit all stakeholders. Let us take cloud computing the main advantage is cost-effectiveness and scalability.

5 Skills you need for data analyst job

  1. R Programming
  2. SAS
  3. Excel
  4. Tableau
  5. QlikView

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