How to measure correlation between several categorical features and a numerical label in Python?
If you want to do an ANOVA test, you can do it with scipy and stats package. Link to documentation

You can do it like that

def anova(data):
    if len(data.groupby(level=1)) &lt;= 2:
        raise Exception('ANOVA requires a secondary index with three or more values')

    return pd.DataFrame(
        [f_oneway(*[v for k, v in data[col].groupby(level=1)]) for col in data.columns],
        columns=['f', 'p'],
        index=data.columns) 


Source

or just choose the two columns you want to test

statistic, pvalue = stats.f_oneway(data['col1'], data['col2'])


However, I would advise you to take a different path. You need to test how important a feature is in your dataset to predict the lead_time. You can train a simple Decision Tree with the whole dataset and get the feature importance for each of the features. Then you remove those that are below a threshold.

from sklearn.tree import DecisionTreeRegressor

tree = DecisionTreeRegressor().fit(X, y)
tree.feature_importances_