I get an errror when running this chunk of code:

```
trading_environment = gym.make('trading-v0',
ticker='AAPL',
trading_days=trading_days,
trading_cost_bps=trading_cost_bps,
time_cost_bps=time_cost_bps)
trading_environment.seed(42)
```

In particular, the error I am getting is:

```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[86], line 1
----> 1 trading_environment = gym.make('trading-v0',
2 ticker='AAPL',
3 trading_days=trading_days,
4 trading_cost_bps=trading_cost_bps,
5 time_cost_bps=time_cost_bps)
6 trading_environment.seed(42)
File ~/anaconda3/lib/python3.11/site-packages/gym/envs/registration.py:640, in make(id, max_episode_steps, autoreset, apply_api_compatibility, disable_env_checker, **kwargs)
637 render_mode = None
639 try:
--> 640 env = env_creator(**_kwargs)
641 except TypeError as e:
642 if (
643 str(e).find("got an unexpected keyword argument 'render_mode'") >= 0
644 and apply_human_rendering
645 ):
File ~/Library/CloudStorage/OneDrive-UniversidaddeLaRioja/CEMFI/Second_year/material_master_thesis/CODE/Machine-Learning-for-Algorithmic-Trading-Second-Edition-master/22_deep_reinforcement_learning/trading_env.py:242, in TradingEnvironment.__init__(self, trading_days, trading_cost_bps, time_cost_bps, ticker)
238 self.simulator = TradingSimulator(steps=self.trading_days,
239 trading_cost_bps=self.trading_cost_bps,
240 time_cost_bps=self.time_cost_bps)
241 self.action_space = spaces.Discrete(3)
...
101 )
103 # Capture the boundedness information before replacing np.inf with get_inf
104 _low = np.full(shape, low, dtype=float) if is_float_integer(low) else low
ValueError: Box shape is inferred from low and high, expect their types to be np.ndarray, an integer or a float, actual type low: <class 'pandas.core.series.Series'>, high: <class 'pandas.core.series.Series'>
```

To solve it, I have tried modifying the metadata class in trading_env.py. In particular, I modified this line of code:

```
self.observation_space = spaces.Box(self.data_source.min_values,self.data_source.max_values)
```

by this one:

```
self.observation_space = spaces.Box(np.array(self.data_source.min_values),np.array(self.data_source.max_values),dtype=np.float32)
```

But still the error persists.