Showing posts with the label python-dictionary

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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

Python Subset: How to Get Subset of Dictionary

Here's a sample program to get the python subset. In this case, you'll find logic for dictionary subsets. Dictionary python To illustrate, I have taken a dictionary as below with keys and values. my_first_dict = { 'HP': 100 'IBM': 200 'NTT': 300 'ABC': 400 'GDF': 500 } I want to make a subset of values greater than 100 and less than 400. How can you achieve this? No worries, below, you will find the logic. Logic to get subset out of a dictionary I am using dictionary comprehension to achieve this. Syntax: sub_set = { key:value for key, value in my_first_dict.items() value >100 and value <400} Result References Python Programming: Using Problem-Solving Approach