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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

How to Work with 'Pointers' in Python

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Pointers denote an address (memory location). It has three identities - Name, Value, and Location (Address). Python doesn't support pointers as-is. You need to import 'ctypes' package to work with C Language. Note : Pointer is popular in C, C++. The called module just uses the value of Pointer (not address).  Below is my detailed post on pointers.   How to work with Pointers To pass a reference(address) to the C interface. You can use C Language in Python by importing 'ctypes.'  Pointer Notation 1. Value 2. Address 3. Name Python Pointers Python doesn't support pointers. C and C++ extensively support pointers. Pointer is nothing but an ADDRESS. It is immutable. That means you can't change the value. Python supports pointers for the purpose to interact with C Language. How to Import 'ctypes' Import 'ctypes' library for the purpose of working with C language.  Here's how to import 'ctypes' for windows and Linux. How to denote Pointe