title: Python 3 - ValueError: Found array with 0 sample(s) (shape=(0, 11)) while a minimum of 1 is required by MinMaxScaler
body: <p>I'm really having trouble trying to get this project of mine up and running, but I'm remaining resilient and I think I'm close! </p>
  
  <p>I'm trying to customize this project to work with my own dataset:</p>
  
  <p><a href="https://github.com/notadamking/Bitcoin-Trader-RL" rel="noreferrer">https://github.com/notadamking/Bitcoin-Trader-RL</a></p>
  
  <p>Basically, this project uses an OHLCV dataset of a stock's prices to train and trade on, however I have a one-day tick dataset that I want to use instead with a few more columns than the OHLCV .csv file provided in the project (mine consists of Time, Open,    Close, Last, LastSize, Bid, BidSize, Ask, AskSize, Volume). It puts the .csv file content into its own array and then uses the MinMaxScaler to convert all points into values between 0-1 relative to the values of the column that it's in.</p>
  
  <p>I'm receiving the following full error, and I'm hoping someone who knows what's going on here will be able to spot what I'm missing:</p>
  
  <pre><code>Traceback (most recent call last):
    File "main.py", line 23, in &lt;module&gt;
      model.learn(total_timesteps=200000)
    File "c:\users\windowshopr\desktop\python scripts\all tools\stable-baselines-master\stable_baselines\a2c\a2c.py", line 231, in learn
      runner = A2CRunner(self.env, self, n_steps=self.n_steps, gamma=self.gamma)
    File "c:\users\windowshopr\desktop\python scripts\all tools\stable-baselines-master\stable_baselines\a2c\a2c.py", line 310, in __init__
      super(A2CRunner, self).__init__(env=env, model=model, n_steps=n_steps)
    File "c:\users\windowshopr\desktop\python scripts\all tools\stable-baselines-master\stable_baselines\common\runners.py", line 19, in __init__
      self.obs[:] = env.reset()
    File "c:\users\windowshopr\desktop\python scripts\all tools\stable-baselines-master\stable_baselines\common\vec_env\dummy_vec_env.py", line 45, in reset
      obs = self.envs[env_idx].reset()
    File "C:\Users\windowshopr\Desktop\Current Python Project\1 - Matt's Bitcoin Trader (level 1)\env\BitcoinTradingEnv.py", line 90, in reset
      return self._next_observation()
    File "C:\Users\windowshopr\Desktop\Current Python Project\1 - Matt's Bitcoin Trader (level 1)\env\BitcoinTradingEnv.py", line 38, in _next_observation
      scaled_df = self.scaler.fit_transform(scaled_df)
    File "C:\Users\windowshopr\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\base.py", line 518, in fit_transform
      return self.fit(X, **fit_params).transform(X)
    File "C:\Users\windowshopr\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\preprocessing\data.py", line 308, in fit
      return self.partial_fit(X, y)
    File "C:\Users\windowshopr\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\preprocessing\data.py", line 334, in partial_fit
      estimator=self, dtype=FLOAT_DTYPES)
    File "C:\Users\windowshopr\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\utils\validation.py", line 431, in check_array
      context))
  ValueError:Found array with 0 sample(s) (shape=(0, 11)) while a minimum of 1 is required by MinMaxScaler.
  </code></pre>
  
  <p>Notice the lines:</p>
  
  <blockquote>
    <p>**File "C:\Users\windowshopr\Desktop\Current Python Project\1 - Matt's Bitcoin Trader (level 1)\env\BitcoinTradingEnv.py", line 90, in reset
            return self._next_observation()</p>
    
    <p>File "C:\Users\windowshopr\Desktop\Current Python Project\1 - Matt's Bitcoin Trader (level 1)\env\BitcoinTradingEnv.py", line 38, in _next_observation
            scaled_df = self.scaler.fit_transform(scaled_df)**</p>
  </blockquote>
  
  <p>I've seen two other questions on SO, but one of them was not answered and the other one didn't solve the question, so I'm at a loss. I know I'm not supposed to post the entire code as per SO's rules, however, in this case, I think it's needed from the traceback errors so I apologize for that but I hope it helps. I think the issue is in the "<strong>def _next_observation(self):</strong>" function:</p>
  
  <pre><code>import gym
  import pandas as pd
  import numpy as np
  from gym import spaces
  from sklearn import preprocessing
  
  from render.BitcoinTradingGraph import BitcoinTradingGraph
  
  MAX_TRADING_SESSION = 100000
  
  
  class BitcoinTradingEnv(gym.Env):
      """A Bitcoin trading environment for OpenAI gym"""
      metadata = {'render.modes': ['human', 'system', 'none']}
      scaler = preprocessing.MinMaxScaler()
      viewer = None
  
      def __init__(self, df, lookback_window_size=40, initial_balance=10000, commission=0.005, serial=False):
          super(BitcoinTradingEnv, self).__init__()
  
          self.df = df.dropna().reset_index()
          self.lookback_window_size = lookback_window_size
          self.initial_balance = initial_balance
          self.commission = commission
          self.serial = serial
  
          # Actions of the format Buy 1/10, Sell 3/10, Hold (amount ignored), etc.
          self.action_space = spaces.MultiDiscrete([3, 10])
  
          # Observes the OHCLV values, net worth, and trade history
          self.observation_space = spaces.Box(
              low=0, high=1, shape=(10, lookback_window_size + 1), dtype=np.float16)
  
      def _next_observation(self):
          end = self.current_step + self.lookback_window_size + 1
  
          scaled_df = self.active_df.values[:end].astype('float64')
          scaled_df = self.scaler.fit_transform(scaled_df)
          scaled_df = pd.DataFrame(scaled_df, columns=self.df.columns)
  
          obs = np.array([
              scaled_df['Open'].values[self.current_step:end],
              scaled_df['Close'].values[self.current_step:end],
              scaled_df['Last'].values[self.current_step:end],
              scaled_df['LastSize'].values[self.current_step:end],
              scaled_df['Bid'].values[self.current_step:end],
              scaled_df['BiSize'].values[self.current_step:end],
              scaled_df['Ask'].values[self.current_step:end],
              scaled_df['AskSize'].values[self.current_step:end],
              scaled_df['Volume'].values[self.current_step:end],
          ])
  
          scaled_history = self.scaler.fit_transform(self.account_history)
  
          obs = np.append(
              obs, scaled_history[:, -(self.lookback_window_size + 1):], axis=0)
  
          return obs
  
      def _reset_session(self):
          self.current_step = 0
  
          if self.serial:
              self.steps_left = len(self.df) - self.lookback_window_size - 1
              self.frame_start = self.lookback_window_size
          else:
              self.steps_left = np.random.randint(1, MAX_TRADING_SESSION)
              self.frame_start = np.random.randint(
                  self.lookback_window_size, len(self.df) - self.steps_left)
  
          self.active_df = self.df[self.frame_start - self.lookback_window_size:
                                   self.frame_start + self.steps_left]
  
      def reset(self):
          self.balance = self.initial_balance
          self.net_worth = self.initial_balance
          self.btc_held = 0
  
          self._reset_session()
  
          self.account_history = np.repeat([
              [self.balance],
              [0],
              [0],
              [0],
              [0]
          ], self.lookback_window_size + 1, axis=1)
          self.trades = []
  
          return self._next_observation()
  
      def _get_current_price(self):
          return self.df['Last'].values[self.frame_start + self.current_step]
  
      def _get_ask_price(self):
          return self.df['Ask'].values[self.frame_start + self.current_step]
  
      def _get_bid_price(self):
          return self.df['Bid'].values[self.frame_start + self.current_step]
  
      def _take_action(self, action, current_price, buy_price, sell_price):
          action_type = action[0]
          amount = action[1] / 10
  
          btc_bought = 0
          btc_sold = 0
          cost = 0
          sales = 0
  
          if action_type &lt; 1:
              btc_bought = self.balance / buy_price * amount
              cost = btc_bought * buy_price * (1 + self.commission)
  
              self.btc_held += btc_bought
              self.balance -= cost
  
          elif action_type &lt; 2:
              btc_sold = self.btc_held * amount
              sales = btc_sold * sell_price * (1 - self.commission)
  
              self.btc_held -= btc_sold
              self.balance += sales
  
          if btc_sold &gt; 0 or btc_bought &gt; 0:
              self.trades.append({'step': self.frame_start + self.current_step,
                                  'amount': btc_sold if btc_sold &gt; 0 else btc_bought, 'total': sales if btc_sold &gt; 0 else cost,
                                  'type': "sell" if btc_sold &gt; 0 else "buy"})
  
          self.net_worth = self.balance + self.btc_held * sell_price
  
          self.account_history = np.append(self.account_history, [
              [self.balance],
              [btc_bought],
              [cost],
              [btc_sold],
              [sales]
          ], axis=1)
  
      def step(self, action):
          current_price = self._get_current_price()
          buy_price = self._get_ask_price()
          sell_price = self._get_bid_price()
  
          prev_net_worth = self.net_worth
  
          self._take_action(action, current_price, buy_price, sell_price)
  
          self.steps_left -= 1
          self.current_step += 1
  
          if self.steps_left == 0:
              self.balance += self.btc_held * sell_price
              self.btc_held = 0
  
              self._reset_session()
  
          obs = self._next_observation()
          reward = self.net_worth - prev_net_worth
          done = self.net_worth &lt;= 0
  
          return obs, reward, done, {}
  
      def render(self, mode='human', **kwargs):
          if mode == 'system':
              print('Price: ' + str(self._get_current_price()))
              print(
                  'Bought: ' + str(self.account_history[2][self.current_step + self.frame_start]))
              print(
                  'Sold: ' + str(self.account_history[4][self.current_step + self.frame_start]))
              print('Net worth: ' + str(self.net_worth))
  
          elif mode == 'human':
              if self.viewer is None:
                  self.viewer = BitcoinTradingGraph(
                      self.df, kwargs.get('title', None))
  
              self.viewer.render(self.frame_start + self.current_step,
                                 self.net_worth,
                                 self.trades,
                                 window_size=self.lookback_window_size)
  
       def close(self):
          if self.viewer is not None:
              self.viewer.close()
              self.viewer = None
  </code></pre>
  
  <p>Thanks a lot, let me know if I need to provide anything else.</p>
  
  <p><strong>UPDATE</strong></p>
  
  <p>Anyone?</p>
  
  <p>If it helps, download the full project at the above Github link, update the BitcoinTradingEnv.py file to match the one I've posted here, and update the .csv file in the data folder to this partial one (where each is in its own column):</p>
  
  <pre><code>Time    Open    Close   Last    LastSize    Bid BidSize Ask AskSize Volume
  1   0.01    3.38    3.38    285 3.95    11  3.99    1   460600
  2   0.01    3.38    3.38    285 3.95    11  4.00    5   460600
  3   0.01    3.38    3.38    285 3.93    2   4.00    5   463936
  4   0.01    3.38    3.38    285 3.93    8   3.95    1   463936
  5   0.01    3.38    3.38    285 3.95    4   4.00    5   464136
  6   0.01    3.38    3.38    285 3.95    4   4.00    6   464136
  7   0.01    3.38    3.38    285 3.95    4   4.00    6   464146
  8   0.01    3.38    3.38    285 3.93    8   4.00    6   464586
  9   0.01    3.38    3.38    285 3.93    8   3.99    1   464586
  10  0.01    3.38    3.38    285 3.93    8   3.99    4   464586
  11  0.01    3.38    3.38    285 3.94    13  3.99    4   464586
  12  0.01    3.38    3.38    285 3.94    13  3.99    4   464590
  13  0.01    3.38    3.38    285 3.94    13  3.99    4   464661
  14  0.01    3.38    3.38    285 3.95    5   3.99    4   464661
  15  0.01    3.38    3.38    285 3.95    5   3.99    3   465261
  16  0.01    3.38    3.38    285 3.94    13  3.98    1   465265
  17  0.01    3.38    3.38    285 3.94    13  3.98    1   467544
  18  0.01    3.38    3.38    285 3.96    5   3.98    1   468544
  19  0.01    3.38    3.38    285 3.96    5   3.98    1   468602
  20  0.01    3.38    3.38    285 3.96    5   3.99    3   468802
  21  0.01    3.38    3.38    285 3.96    4   3.99    2   470247
  22  0.01    3.38    3.38    285 3.96    4   3.99    2   470747
  23  0.01    3.38    3.38    285 3.96    4   3.97    5   470747
  24  0.01    3.38    3.38    285 3.95    1   3.97    5   471747
  25  0.01    3.38    3.38    285 3.95    1   3.97    5   471948
  26  0.01    3.38    3.38    285 3.95    1   3.97    5   471958
  27  0.01    3.38    3.38    285 3.95    1   3.99    2   472958
  28  0.01    3.38    3.38    285 3.95    2   3.97    3   472958
  29  0.01    3.38    3.38    285 3.95    1   3.97    3   473033
  30  0.01    3.38    3.38    285 3.95    1   3.97    2   473117
  31  0.01    3.38    3.38    285 3.96    2   3.97    2   473117
  32  0.01    3.38    3.38    285 3.96    2   3.97    2   473127
  33  0.01    3.38    3.38    285 3.96    2   3.99    2   473327
  34  0.01    3.38    3.38    285 3.96    2   3.99    2   473343
  35  0.01    3.38    3.38    285 3.97    1   3.99    2   473343
  36  0.01    3.38    3.38    285 3.99    1   4.00    13  473818
  37  0.01    3.38    3.38    285 3.99    1   4.00    1   476618
  38  0.01    3.38    3.38    285 3.99    1   4.00    3   477918
  39  0.01    3.38    3.38    285 3.99    5   4.00    1   478425
  40  0.01    3.38    3.38    285 3.99    5   4.00    1   479686
  41  0.01    3.38    3.38    285 3.99    4   4.00    1   479686
  42  0.01    3.38    3.38    285 3.99    4   4.00    1   480366
  43  0.01    3.38    3.38    285 3.95    6   3.97    1   481115
  44  0.01    3.38    3.38    285 3.95    6   4.00    1   481162
  45  0.01    3.38    3.38    285 3.95    6   4.00    8   481162
  46  0.01    3.38    3.38    285 3.95    6   3.99    5   481226
  47  0.01    3.38    3.38    285 3.98    1   3.99    5   481279
  48  0.01    3.38    3.38    285 3.95    6   3.99    5   481929
  49  0.01    3.38    3.38    285 3.95    6   3.98    1   481929
  50  0.01    3.38    3.38    285 3.95    6   3.98    1   482041
  51  0.01    3.38    3.38    285 3.96    6   3.98    1   482041
  52  0.01    3.38    3.38    285 3.96    6   3.98    1   482066
  53  0.01    3.38    3.38    285 3.96    6   3.98    1   482093
  54  0.01    3.38    3.38    285 3.96    6   3.98    1   482137
  55  0.01    3.38    3.38    285 3.96    6   3.98    1   484512
  56  0.01    3.38    3.38    285 3.96    6   3.98    1   484541
  57  0.01    3.38    3.38    285 3.96    6   3.98    4   484541
  58  0.01    3.38    3.38    285 3.96    6   3.98    4   488541
  59  0.01    3.38    3.38    285 3.96    6   3.98    1   488541
  60  0.01    3.38    3.38    285 3.96    6   3.98    1   489541
  61  0.01    3.38    3.38    285 3.95    12  3.96    3   490171
  62  0.01    3.38    3.38    285 3.95    12  3.96    5   490171
  63  0.01    3.38    3.38    285 3.92    10  3.95    56  492046
  64  0.01    3.38    3.38    285 3.92    10  3.95    60  492046
  65  0.01    3.38    3.38    285 3.92    10  3.95    60  502046
  66  0.01    3.38    3.38    285 3.92    10  3.95    56  502046
  67  0.01    3.38    3.38    285 3.92    8   3.95    56  503418
  68  0.01    3.38    3.38    285 3.92    8   3.95    56  503427
  69  0.01    3.38    3.38    285 3.92    8   3.95    54  503617
  70  0.01    3.38    3.38    285 3.92    8   3.95    54  503817
  71  0.01    3.38    3.38    285 3.92    6   3.95    54  505167
  72  0.01    3.38    3.38    285 3.91    62  3.92    7   505727
  73  0.01    3.38    3.38    285 3.92    7   3.95    54  506517
  74  0.01    3.38    3.38    285 3.92    7   3.95    54  506867
  75  0.01    3.38    3.38    285 3.92    7   3.95    54  506877
  76  0.01    3.38    3.38    285 3.94    4   3.95    54  506877
  77  0.01    3.38    3.38    285 3.94    2   3.95    54  507572
  78  0.01    3.38    3.38    285 3.94    3   3.95    54  507572
  79  0.01    3.38    3.38    285 3.94    3   3.95    54  507972
  80  0.01    3.38    3.38    285 3.92    8   3.94    9   508592
  81  0.01    3.38    3.38    285 3.91    62  3.92    50  509192
  82  0.01    3.38    3.38    285 3.91    16  3.91    1   514523
  83  0.01    3.38    3.38    285 3.91    1   3.91    1   516023
  84  0.01    3.38    3.38    285 3.89    16  3.90    17  518826
  85  0.01    3.38    3.38    285 3.89    12  3.90    17  520025
  86  0.01    3.38    3.38    285 3.86    21  3.87    7   522335
  87  0.01    3.38    3.38    285 3.86    21  3.87    7   522355
  88  0.01    3.38    3.38    285 3.86    21  3.87    4   522655
  89  0.01    3.38    3.38    285 3.86    3   3.87    1   524452
  90  0.01    3.38    3.38    285 3.85    5   3.87    1   525652
  91  0.01    3.38    3.38    285 3.83    6   3.86    1   526575
  92  0.01    3.38    3.38    285 3.84    1   3.86    1   526675
  93  0.01    3.38    3.38    285 3.84    6   3.85    4   526675
  94  0.01    3.38    3.38    285 3.85    1   3.86    1   527575
  95  0.01    3.38    3.38    285 3.85    15  3.87    1   528175
  96  0.01    3.38    3.38    285 3.87    4   3.90    2   529300
  97  0.01    3.38    3.38    285 3.87    2   3.88    1   531300
  98  0.01    3.38    3.38    285 3.87    2   3.90    2   531325
  99  0.01    3.38    3.38    285 3.87    8   3.90    2   531325
  100 0.01    3.38    3.38    285 3.87    8   3.90    2   531625
  101 0.01    3.38    3.38    285 3.90    2   3.91    6   532475
  102 0.01    3.38    3.38    285 3.87    8   3.91    6   532675
  103 0.01    3.38    3.38    285 3.88    2   3.91    6   532675
  104 0.01    3.38    3.38    285 3.88    2   3.92    1   534590
  105 0.01    3.38    3.38    285 3.88    2   3.92    1   534670
  106 0.01    3.38    3.38    285 3.90    1   3.92    1   534720
  107 0.01    3.38    3.38    285 3.90    1   3.92    1   535223
  108 0.01    3.38    3.38    285 3.90    1   3.92    3   535723
  109 0.01    3.38    3.38    285 3.90    1   3.91    4   535778
  110 0.01    3.38    3.38    285 3.87    9   3.90    4   535939
  111 0.01    3.38    3.38    285 3.88    2   3.90    2   535939
  112 0.01    3.38    3.38    285 3.88    2   3.90    2   536440
  113 0.01    3.38    3.38    285 3.88    1   3.90    2   536470
  114 0.01    3.38    3.38    285 3.87    9   3.91    3   536710
  115 0.01    3.38    3.38    285 3.87    8   3.91    3   536780
  116 0.01    3.38    3.38    285 3.87    8   3.91    2   536780
  117 0.01    3.38    3.38    285 3.88    1   3.91    2   536780
  118 0.01    3.38    3.38    285 3.88    5   3.91    2   536780
  119 0.01    3.38    3.38    285 3.88    5   3.91    2   536795
  120 0.01    3.38    3.38    285 3.89    1   3.91    2   536795
  121 0.01    3.38    3.38    285 3.89    1   3.89    2   536945
  122 0.01    3.38    3.38    285 3.88    3   3.89    2   537495
  123 0.01    3.38    3.38    285 3.88    3   3.89    2   538495
  124 0.01    3.38    3.38    285 3.88    3   3.91    2   538495
  125 0.01    3.38    3.38    285 3.88    3   3.90    2   538495
  126 0.01    3.38    3.38    285 3.88    3   3.91    2   538645
  127 0.01    3.38    3.38    285 3.88    4   3.91    2   538645
  128 0.01    3.38    3.38    285 3.88    5   3.91    2   538645
  129 0.01    3.38    3.38    285 3.89    1   3.91    2   538645
  130 0.01    3.38    3.38    285 3.89    1   3.91    2   538645
  131 0.01    3.38    3.38    285 3.87    7   3.88    48  539326
  132 0.01    3.38    3.38    285 3.87    4   3.88    48  539868
  133 0.01    3.38    3.38    285 3.87    4   3.88    48  540368
  134 0.01    3.38    3.38    285 3.86    6   3.87    1   541081
  135 0.01    3.38    3.38    285 3.86    2   3.87    1   541581
  136 0.01    3.38    3.38    285 3.86    2   3.87    6   541581
  137 0.01    3.38    3.38    285 3.86    2   3.87    6   541608
  138 0.01    3.38    3.38    285 3.86    2   3.87    11  541608
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  </code></pre>