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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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Here's Python Program for List Duplicates

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Here is a program to find the item that occurs most frequently in a data structure. So why to find frequent item? Maybe it is the most purchased item on your shopping site. Perhaps it is the web page that gets hit the most often.

If you are a tester, it could easily be the test that has had the most failures over the last year. Whatever it is, you want an easy way to find the data you need, and Python is here to help you.


Python List duplicates

List frequent item


Here are the two simple lists:

list_1 = [1,2,3,2,3,2] 
list_2 = ['a', 'b', 'a', 'b', 'c']

  • We can't do simple math on the individual items since the second list contains characters. For example, it could contain the words of a book, and you want to find the most commonly used word in the work. 
  • Also, it maybe list of UPC values for commonly purchased items. Whatever it is, all we can guarantee is that the data is probably comparable, in that we can compare one of the items to another. Yet we need to find frequent items.


Python program

Below, you will find a program to find repeated values.

list_1 = [1,2,3,2,3,2]
list_2 = ['a', 'b', 'a', 'b', 'c']

def most_common_brute_force(l):

  # Find the counts of all elements
    dict_of_counts = {}
    for i in l:
        if i in dict_of_counts.keys():
            dict_of_counts[i] = dict_of_counts[i] + 1
        else:
            dict_of_counts[i] = 1

            max_count = -1
            max_value = -1
 
        for k, v in dict_of_counts.items():
            if v > max_count:
                max_count = v
                max_value = k
    return max_value
print(most_common_brute_force(list_1))
print(most_common_brute_force(list_2))

Output

2
a    

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