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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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Understand Data power why quality everyone wants

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Information and data quality is new service work for data intense companies. I have seen not only in Analytics projects but in Mainframe projects, there is the Data Quality team.

How incorrect data impact on us

Information quality problems and their impact are all around us:
  • A customer does not receive an order because of incorrect shipping information.
  • Products are sold below cost because of wrong discount rates.
  • A manufacturing line is stopped because parts were not ordered—the result of inaccurate inventory information.
  • A well-known U.S. senator is stopped at an airport (twice) because his name is on a government "Do not fly" list.
  • Many communities cannot run an election with results that people trust.
  • Financial reform has created new legislation such as Sarbanes—Oxley. 
Incorrect data leads to many problems. The role of Data Science is to use quality data for effective decisions.

What is information

  1. Information is not simply data, strings of numbers, lists of addresses, or test results stored in a computer. Information is the product of business processes and is continuously used and reused by them. 
  2. It takes human beings to bring information to its real-world context and give it meaning. 
  3. Every day human beings use the information to make decisions, complete transactions and carry out all the other activities that make a business run. Applications come and applications go, but the information in those applications lives on.
  4. Effective business decisions and actions can only be made when based on high-quality information—the key here being effective. Yes, business decisions are based all the time on poor-quality data, but effective business decisions cannot be made with flawed, incomplete, or misleading data. 
  5. People need information they can trust to be correct and current if they are to do the work that furthers business goals and objectives.

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