How to plot count of category columns of a dataframe based on timestamp column using any pandas visualization library

I have a Pandas Dataframe which looks like below

test_case,test_type,source_path,table_path,test_status,record_count,table_partition,load_ts,id
check1,T1,/documents/Status,pass,nan,nan,2021-02-13 19:06:02.527,1
check2,T1,/documents/Status,fail,nan,nan,2021-02-11 19:06:02.527,2
check3,T2,/documents/Status,pass,nan,nan,2021-02-12 19:06:02.527,3
check4,T2,/documents/Status,fail,nan,nan,2021-02-12 19:06:02.527,4

I want my Output or Visualization Plots which tell: For each date(load_ts column),show test case names(test_case column values) which have passed and test case names which have failed

The problem with the below code is that it only shows test status count of pass and fail per date but i want to further drill down and see that which test cases are under pass and which are under fail. Kindly see the below plot which i have created as of now.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
plt.figure(figsize=(15,16))
sns.countplot(x='load_ts', hue="test_status", data=result_df)

plt.show()