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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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Limitations of Mobile Computing

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What is Mobile Computing?
Mobile computing ─ ability to use the technology to wirelessly connect to and use centrally located information and/or application software through the application of small, portable, and wireless computing and communication devices voice, data and multimedia communication standards

 Limitations
  • Resource constraints: Battery
  • Interference: the quality of service (QoS)
  • Bandwidth: connection latency
  • Dynamic changes in communication environment: variations in signal power within a region, thus link delays and connection losses
  • Network Issues: discovery of the connection-service to destination and connection stability
  • Interoperability issues: the varying protocol standards
  • Security constraints: Protocols conserving privacy of communication 

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