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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

Print Dictionary Values Simplified Logic

Here's for loop logic that says how to use Dictionary to get its data. I will present here how to use it as input In for loop. You can also call Dictionary as Map, Hash, or Associative array.

Print dictionary values

Here is way to print dictionary values using for loop

Below is my example. I have created a simple Dictionary called 'store.' Then, I will get its data using for-loop. Here's my previous post Python -  How to Lookup Dictionary By Key.

Dictionary example

Store = { "rao" : 1, "srini' : 2}

For-loop logic

for key in store:
     print(key, store[key])

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Real-time result

The execution of for loop showed here. It's much simple. Just use the code as I did. You'll get the desired result quickly.

How to use for loop on Dictionary Useful Example



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