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How to Check Column Nulls and Replace: Pandas

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

Numpy Array Vs. List: What's the Difference

Here are the differences between List and NumPy Array. Both store data, but technically these are not the same. You'll find here where they differ from each other.

Python Lists

Here is all about Python lists:

  • Lists can have data of different data types. For instance, data = [3, 3.2, 4.6, 6, 6.8, 9, “hello”, ‘a’]
  • Operations such as subtraction, multiplying, and division allow doing through loops
  • Storage space required is more, as each element is considered an object in Python
  • Execution time is high for large datasets
  • Lists are inbuilt data types

Array vs list

NumPy Arrays

Here is all about NumPy Arrays:
  • Numpy arrays are containers for storing only homogeneous data types. For example: data= [3.2, 4.6, 6.8]; data=[3, 6, 9]; data=[‘hello’, ‘a’]
  • Numpy is designed to do all mathematical operations in parallel and is also simpler than Python
  • Numpy storage space is very much less compared to the list due to the practice of homogeneous data type
  • Execution time is very less for large datasets
  • Numpy is a third-party library that needs to be installed by Conda or PIP



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