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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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How to Create UDF in Python Example

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In Python,user-defined function usage is to avoid repeated work. The UDFs in Python are not like C/C++/JAVA. I am sharing ideas on how to create UDF in Python.



udf in python

Python Syntax for User defined function(UDF)

Below is the good example on Python UDF.
def function_name(list of parameters): 
"docstring" 
statement(s) 
return(parameter)

Explanation of each keyword 

  1. The keyword def symbolizes the start of the function header.
  2. A function name to uniquely identify it. Function naming follows the similar rules that are used for writing identifiers
  3. List of parameters also called as a list of arguments through which value is passed to the function. The list of parameters is optional.
  4. A colon (:) to mark the end of function header.
  5. Optional documentation string (docstring) is used to describe the purpose of the function, which is slightly similar to python documentation using comment.
  6. Python statements that perform the intended task for which the user-defined function is made. It is mandatory to maintain the indentation level while writing python statements in the function definition.
  7. In the end, an optional return statement is used to return a value (result) from the function. This statement can contain an optional parameter to return the computed result back to the function call. If there is no parameter in the statement or the return statement is not mentioned at the end of function definition then the function returns the None object.

Python Vs Other Languages

Python user defined functions
Python is one of the most popular languages in data analytics. There are many other languages that have an option to create UDFs. Even in SQL of any database, you can easily create user-defined functions.

Advantages of Python User defined Function

  • User-defined functions help to decompose a large program into small segments which make the program easy to understand, maintain and debug.
  • If repeated code occurs in a program. The function can be used to include those codes and execute when needed by calling that function.
  • Programmers working on the large project can divide the workload by making different functions.
References

One practical advice

It is always a good idea to name user-defined functions according to the task they perform.

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