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This link might be helpful - they discuss the similar topic. https://stackoverflow.com/questions/61641632/pandas-json-normalize-to-flatten-a-dictionary-with-values-as-columns


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Iterating through Dataframes is (generally speaking) an anti-pattern. Always try to avoid it if you can! You can easily vectorize this operation by subtracting the scalar value from target rather than treating target like another array: # Vectorized squared errors combopd["SSE"] = sum( (target[f'x{n}'].values[0] - combopd[f'x{n}'])**2 # ...


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Let's simplify the requirements A wanted row from the right dataframe is a row that has the least amount of unmatched keys has the minimum euclidean distance def merge_left_and_right(left, right): # save original input from modification left = left.copy() right = right.copy() # numerate rows in the right dataframe right['row_num'] =...


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This is more of a programming question than a data science question and would therefore be better suited for stackoverflow stackexchange, but the following code should do what you're looking for: df[["A", "C"]] = ( df # create groups .groupby(["B", "D"]) # transform the groups by filling na values with ...


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These string data, called categorical data can be converted to numerical data using many Categorical Encoding Techniques. Encoding categorical data is a process of converting categorical data into integer format so that the data with converted categorical values can be provided to the different models. Types of Categorical Techniques: Backward Difference ...


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You can multiple things here : Converting them to numerical introduces some sense of ordering For example if you say slovenia is 1 and USA is 2 ans ordering is introduced instead you can use one hot encoding. Pandas getdummies function will do it for you If one of your string has a lot of values say 1000 one hot encoding does not makes sense. In those ...


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