Handling Duplicate Rows with Different Column Types in Pandas DataFrame
I'm attempting to set up I'm working with an scenario while trying to drop duplicate rows from a DataFrame where the columns involved have different data types. I'm using Pandas version 1.3.3 and have a DataFrame that looks like this: ```python import pandas as pd # Sample DataFrame data = { 'id': [1, 2, 2, 3], 'value': [10, 20, 20.0, 30], # Mixed types: int and float 'category': ['A', 'B', 'B', 'C'] } df = pd.DataFrame(data) ``` When I use the `drop_duplicates()` method to remove duplicates based on the 'id' column, I expect to keep the first occurrence. However, I'm running into some confusion because of the mixed data types in the 'value' column. Here’s what I tried: ```python # Attempt to drop duplicates result = df.drop_duplicates(subset=['id']) print(result) ``` The output I get is: ``` id value category 0 1 10.0 A 1 2 20.0 B 3 3 30.0 C ``` As you can see, the value for 'id' 2 appears as `20.0`, but in the original DataFrame, it was `20`. I expected it to keep the integer type rather than converting it to a float. I want to understand how I can ensure that when I drop duplicates, the data type remains consistent. Is there a way to specify data types in the `drop_duplicates()` or should I convert the column types before this operation? What’s the best practice for handling such cases?