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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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Data Science Real Advantages to Read Today

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Real business solution you can get from data science analysis. Your sales is the main requirement. No sales means no business. Data Analytics helps to boost your organization sales.

Real Advantages of Data Science

My presentation here gives you complete business picture of data analytics, and skills companies expecting from analytics team.
  1. Data analytics-The data can be from web, user devices, own databases and Social media
  2. AI-Delivering products based on artificial intelligence
  3. Big data-Data of any format and you need to make ready for analysis.
  4. You can understand patterns of people
  5. You can explore current market
  6. You can explore solutions to Agriculture
  7. You can show answers to weather
  8. Predicting the disaster like earth-quake 

Data Analytics what else you can read


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