How to Handle DataFrame with Mixed Data Types when Aggregating in Pandas?
I'm writing unit tests and I need help solving I keep running into I've been banging my head against this for hours... I have a DataFrame where some columns contain mixed data types, specifically integers and strings, which is causing issues when I try to perform aggregation operations. For example, I have the following DataFrame: ```python import pandas as pd data = { 'category': ['A', 'B', 'A', 'B'], 'value': [10, 20, '30', 40], # '30' is a string 'count': [1, 2, 3, 'four'] # 'four' is also a string } df = pd.DataFrame(data) ``` When I attempt to compute the sum of the 'value' and 'count' columns grouped by 'category', I encounter a TypeError, like this: ```python TypeError: unsupported operand type(s) for +: 'int' and 'str' ``` I tried using `pd.to_numeric()` on the 'value' column to convert everything to a numeric type, but I still run into issues with the 'count' column because of the non-numeric string values. Here's what I did: ```python df['value'] = pd.to_numeric(df['value'], errors='coerce') df['count'] = pd.to_numeric(df['count'], errors='coerce') ``` After coercing, I still found that some entries become NaN, which leads to unexpected results in the aggregation. When I run: ```python result = df.groupby('category').sum() ``` I get: ```python ValueError: want to perform 'add' with a dtyped [object] array and scalar of type [int] ``` How can I effectively handle this situation to ensure that I can aggregate the numeric values while avoiding errors from the mixed data types? What best practices can I follow for cleaning up the DataFrame before performing operations like these? Any insights would be greatly appreciated! What's the best practice here? Hoping someone can shed some light on this. For context: I'm using Python on macOS. I appreciate any insights!