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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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Hyperion: How to Learn as Alternative for Mainframe

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Oracle Hyperion is a reporting tool. Its applications are Capital management, Asset planning, Workforce planning and more.

#Hyperion Career for Mainframe programmers:
Photo Credit: Srini

Books to Read on Hyperion

The Oracle Hyperion Financial Reporting 11 covers all basics to learn financial reporting using Hyperion tool.

The popular contents are

  • Explore Grids and the Point of View
  • Create Functions and Formulas
  • Master Conditional Formatting and Conditional Suppression
  • Create Dynamic Books and Batches
  • Import Reporting Content into MS Office with Oracle Smart View

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