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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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Business Vs Demographic Vs Product Analytics

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List of top analytics areas and their differences



1. Analytics in Business

  • Advertising Analytics
  • Brand Analytics
  • Promotion Analytics
  • Business-to-business marketing Analytics
  • Social Media Analytics
  • Tracking Studies

2. Demographic Analytics

  • Consumer Analytics
  • Concept Testing Data Mining
  • Customer Satisfaction Study Analytics
  • Demographic Analytics
  • Employee Satisfaction Analysis
  • Text Mining
  • Ethnographic Analytics
  • Media Testing
  • Opinion Polling and Predictive Analytics
  • Usage & Attitude Studies
  • Segmentation Analytics
  • Semiotic and Cultural Analysis

3. Product Analytics

  • Packaging and Design Effectiveness Analytics
  • New Product Development
  • Pricing Studies
  • Product Testing
  • Scenario Planning 

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