Unexpected Behavior When Using `pd.cut` with NaN Values in Pandas
I've been banging my head against this for hours. I'm trying to use `pd.cut` to bin a column of continuous values into discrete intervals, but I'm running into an issue with how it handles NaN values. I have a DataFrame that looks like this: ```python import pandas as pd import numpy as np data = {'value': [1.2, 2.5, np.nan, 4.8, 5.1, np.nan, 3.3]} df = pd.DataFrame(data) ``` When I apply `pd.cut`, I want to categorize these values into bins. However, I'm noticing that the result includes NaN in the bin assignment, which I didn't expect. My code is as follows: ```python bins = [0, 2, 4, 6] labels = ['Low', 'Medium', 'High'] df['category'] = pd.cut(df['value'], bins=bins, labels=labels) ``` After running this, the output for `df` shows that the `category` column has NaN entries where the `value` column is NaN, which is fine, but when I try to use this DataFrame for further analysis, I'm seeing inconsistent results: ```python print(df) ``` This prints: ``` value category 0 1.2 Low 1 2.5 Medium 2 NaN NaN 3 4.8 High 4 5.1 High 5 NaN NaN 6 3.3 Medium ``` I would expect that when I filter out NaN values from the `value` column, the corresponding `category` column would also be filtered out or at least handled differently. However, when I attempt to group by `category`, I'm encountering empty groups for entries that contained NaN values. I tried using `dropna()` before cutting: ```python df_filtered = df.dropna(subset=['value']) df_filtered['category'] = pd.cut(df_filtered['value'], bins=bins, labels=labels) ``` But this doesn't solve the issue as I'm still left with those NaN categories in the original DataFrame. Is there any way to prevent `pd.cut` from assigning NaN to the `category` column or to automatically handle these NaN values in a more predictable way? Any best practices here would be appreciated. My development environment is Windows. Thanks in advance!