title: Why do I get unexpected nonzero values when doing a quantiles calculation from timeseries data?
body: <p>I have a csv file with timeseries data of hourly PV production for a whole year. I want to get the quantiles (from 0.1 to 0.9) for each hour in order to represent their behavior every 24 h for all days of the year.</p>
  <p>It seemed to work ok at first, but then I realized that in my some of the quantiles I have values different than zero (0) at 00:00 which is impossible as my dataset does not have such values in that time of the day (I triple checked it).</p>
  <p>Where is the mistake?</p>
  <p>My code:</p>
  <pre><code>colnames = ['Date', 'Energy']
  df_1 = pd.read_csv('PV_Autumn_2020.csv', names = colnames  , encoding=&quot;utf8&quot;, delimiter=&quot;;&quot;)
  
  start_date = datetime(2022, 9, 27, 1, 0)
  end_date = datetime(2022, 9, 28, 1, 0)
  
  def daterange(start_date, end_date):
      delta = timedelta(hours=1)
      while start_date &lt; end_date:
          yield start_date
          start_date += delta
          
  df = pd.DataFrame(columns = ['Time','pct0.1','pct0.2','pct0.3','pct0.4','pct0.5','pct0.6','pct0.7','pct0.8','pct0.9'])
  
  for single_date in daterange(start_date, end_date):
              df.loc[single_date, ['Time']] = single_date.strftime(&quot;%H:%M&quot;)
  
  
  x = []
  
  for index in df.index:
      y = df_1.loc[df_1['Date'].str.contains(df['Time'][index])]
      for i in np.arange(1, 10, 1)/10:
          x.append(y.quantile(i))
  
  pct = pd.DataFrame(x, columns = ['Energy'])
  
  df['pct0.1'] = pct.loc[0.1].values
  df['pct0.2'] = pct.loc[0.2].values
  df['pct0.3'] = pct.loc[0.3].values
  df['pct0.4'] = pct.loc[0.4].values
  df['pct0.5'] = pct.loc[0.5].values
  df['pct0.6'] = pct.loc[0.6].values
  df['pct0.7'] = pct.loc[0.7].values
  df['pct0.8'] = pct.loc[0.8].values
  df['pct0.9'] = pct.loc[0.9].values
  </code></pre>
  <p>A part of the csv file:</p>
  <pre class="lang-none prettyprint-override"><code>2021/03/01 00:00:00;0
  2021/03/01 01:00:00;0
  2021/03/01 02:00:00;0
  2021/03/01 03:00:00;0
  2021/03/01 04:00:00;0
  2021/03/01 05:00:00;0
  2021/03/01 06:00:00;0
  2021/03/01 07:00:00;0
  2021/03/01 08:00:00;111
  2021/03/01 09:00:00;609
  2021/03/01 10:00:00;1152
  2021/03/01 11:00:00;1596
  2021/03/01 12:00:00;1919
  2021/03/01 13:00:00;2062
  2021/03/01 14:00:00;2003
  2021/03/01 15:00:00;1755
  2021/03/01 16:00:00;1351
  2021/03/01 17:00:00;801
  2021/03/01 18:00:00;233
  2021/03/01 19:00:00;0
  2021/03/01 20:00:00;0
  2021/03/01 21:00:00;0
  2021/03/01 22:00:00;0
  2021/03/01 23:00:00;0
  2021/03/02 00:00:00;0
  2021/03/02 01:00:00;0
  2021/03/02 02:00:00;0
  2021/03/02 03:00:00;0
  2021/03/02 04:00:00;0
  2021/03/02 05:00:00;0
  2021/03/02 06:00:00;0
  2021/03/02 07:00:00;0
  2021/03/02 08:00:00;92
  2021/03/02 09:00:00;449
  2021/03/02 10:00:00;905
  2021/03/02 11:00:00;1387
  2021/03/02 12:00:00;1516
  2021/03/02 13:00:00;1617
  2021/03/02 14:00:00;1671
  2021/03/02 15:00:00;1525
  2021/03/02 16:00:00;1290
  2021/03/02 17:00:00;899
  2021/03/02 18:00:00;312
  2021/03/02 19:00:00;0
  2021/03/02 20:00:00;0
  2021/03/02 21:00:00;0
  2021/03/02 22:00:00;0
  2021/03/02 23:00:00;0
  </code></pre>
  <p>The dataframe created</p>
  <pre class="lang-none prettyprint-override"><code>    Time    pct0.1  pct0.2  pct0.3  pct0.4  pct0.5  pct0.6  pct0.7  pct0.8  pct0.9
  2022-09-27 01:00:00 01:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 02:00:00 02:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 03:00:00 03:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 04:00:00 04:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 05:00:00 05:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 06:00:00 06:00   0.0 0.0 0.0 1.0 4.0 13.0    24.0    39.0    56.0
  2022-09-27 07:00:00 07:00   25.0    51.0    84.0    148.0   180.0   249.0   297.0   378.0   426.0
  2022-09-27 08:00:00 08:00   271.0   374.0   444.0   540.0   632.0   763.0   843.0   885.0   969.0
  2022-09-27 09:00:00 09:00   545.0   743.0   880.0   1012.0  1079.0  1182.0  1285.0  1359.0  1401.0
  2022-09-27 10:00:00 10:00   754.0   1037.0  1184.0  1315.0  1392.0  1478.0  1574.0  1654.0  1700.0
  2022-09-27 11:00:00 11:00   858.0   1155.0  1291.0  1454.0  1540.0  1612.0  1695.0  1800.0  1853.0
  2022-09-27 12:00:00 12:00   866.0   1185.0  1333.0  1452.0  1545.0  1630.0  1721.0  1820.0  1893.0
  2022-09-27 13:00:00 13:00   871.0   1069.0  1239.0  1350.0  1435.0  1521.0  1612.0  1715.0  1817.0
  2022-09-27 14:00:00 14:00   673.0   943.0   1036.0  1112.0  1207.0  1318.0  1383.0  1484.0  1622.0
  2022-09-27 15:00:00 15:00   420.0   590.0   689.0   743.0   805.0   989.0   1045.0  1163.0  1297.0
  2022-09-27 16:00:00 16:00   130.0   184.0   222.0   304.0   385.0   487.0   603.0   682.0   855.0
  2022-09-27 17:00:00 17:00   1.0 3.0 7.0 30.0    55.0    96.0    152.0   207.0   338.0
  2022-09-27 18:00:00 18:00   0.0 0.0 0.0 0.0 0.0 2.0 4.0 11.0    31.0
  2022-09-27 19:00:00 19:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 20:00:00 20:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 21:00:00 21:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 22:00:00 22:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-27 23:00:00 23:00   0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
  2022-09-28 00:00:00 00:00   0.0 0.0 0.0 0.0 5.0 163.0   626.1   1059.0  1439.7
  </code></pre>