title: How to reset x tick labels in matplotlib
body: <p>I have 5 time series data in DataFrames and each of them have a different time scale.
  For example, data1 is from 4/15 0:00 to 4/16 0:00, data2 is from 9/16 06:30 to 7:00.
  All these data are in different DataFrames and I wanna draw graphs of them by using matplotlib. I want to set the numbers of x tick labels 5 and put a date of the data just on the leftmost x tick label. I tried the code below but I couldn't get graphs I wanted.</p>
  
  <pre><code>fig = plt.figure(figsize=(15, 3))
  for i in range(1,6):    # because I have 5 DataFrames in 'df_event_num'
      ax = plt.subplot(150+i)
      plt.title('event_num{}'.format(i))
      df_event_num[i-1]['Load_Avg'].plot(color=colors_2018[i-1])
      ax.tick_params(rotation=270)
  fig.tight_layout()
  </code></pre>
  
  <p>And I got a graph like this
  <a href="https://i.stack.imgur.com/bCLLC.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/bCLLC.png" alt="enter image description here"></a></p>
  
  <p>Again, I want to set the numbers of x tick labels to 5 and put a date just on the leftmost x tick label on every graph. And hopefully, I want to rotate the characters of x tick labels.
  Could anyone teach me how to get graphs I want?</p>
  
  <p>df_event_num has 5 DataFrames and I want to make time series graphs of the column data named 'Load_Avg'.
  Here is the sample data of 'df_event_num'.</p>
  
  <pre><code>print(df_event_num[0]['Load_Avg'])
  &gt;&gt;&gt;
  TIMESTAMP
  2018-04-15 00:00:00    406.2
  2018-04-15 00:30:00    407.4
  2018-04-15 01:00:00    409.6
  2018-04-15 01:30:00    403.3
  2018-04-15 02:00:00    405.0
  2018-04-15 02:30:00    401.8
  2018-04-15 03:00:00    401.1
  2018-04-15 03:30:00    401.0
  2018-04-15 04:00:00    402.3
  2018-04-15 04:30:00    402.5
  2018-04-15 05:00:00    404.3
  2018-04-15 05:30:00    404.7
  2018-04-15 06:00:00    417.0
  2018-04-15 06:30:00    438.9
  2018-04-15 07:00:00    466.4
  2018-04-15 07:30:00    476.6
  2018-04-15 08:00:00    499.3
  2018-04-15 08:30:00    523.1
  2018-04-15 09:00:00    550.2
  2018-04-15 09:30:00    590.2
  2018-04-15 10:00:00    604.4
  2018-04-15 10:30:00    622.4
  2018-04-15 11:00:00    657.7
  2018-04-15 11:30:00    737.2
  2018-04-15 12:00:00    775.0
  2018-04-15 12:30:00    819.0
  2018-04-15 13:00:00    835.0
  2018-04-15 13:30:00    848.0
  2018-04-15 14:00:00    858.0
  2018-04-15 14:30:00    866.0
  2018-04-15 15:00:00    874.0
  2018-04-15 15:30:00    879.0
  2018-04-15 16:00:00    883.0
  2018-04-15 16:30:00    889.0
  2018-04-15 17:00:00    893.0
  2018-04-15 17:30:00    894.0
  2018-04-15 18:00:00    895.0
  2018-04-15 18:30:00    897.0
  2018-04-15 19:00:00    895.0
  2018-04-15 19:30:00    898.0
  2018-04-15 20:00:00    899.0
  2018-04-15 20:30:00    900.0
  2018-04-15 21:00:00    903.0
  2018-04-15 21:30:00    904.0
  2018-04-15 22:00:00    905.0
  2018-04-15 22:30:00    906.0
  2018-04-15 23:00:00    906.0
  2018-04-15 23:30:00    907.0
  2018-04-16 00:00:00    909.0
  Freq: 30T, Name: Load_Avg, dtype: float64
  
  </code></pre>
  
  <pre><code>print(df_event_num[1]['Load_Avg'])
  &gt;&gt;&gt;
  TIMESTAMP
  2018-04-25 06:30:00    1133.0
  2018-04-25 07:00:00    1159.0
  Freq: 30T, Name: Load_Avg, dtype: float64
  </code></pre>
  
  <pre><code>print(df_event_num[2]['Load_Avg'])
  TIMESTAMP
  2018-06-28 09:30:00     925.0
  2018-06-28 10:00:00    1008.0
  Freq: 30T, Name: Load_Avg, dtype: float64
  </code></pre>
  
  <pre><code>print(df_event_num[3]['Load_Avg'])
  &gt;&gt;&gt;
  TIMESTAMP
  2018-09-08 00:00:00    769.3
  2018-09-08 00:30:00    772.4
  2018-09-08 01:00:00    778.3
  2018-09-08 01:30:00    787.5
  2018-09-08 02:00:00    812.0
  2018-09-08 02:30:00    825.0
  2018-09-08 03:00:00    836.0
  2018-09-08 03:30:00    862.0
  2018-09-08 04:00:00    884.0
  2018-09-08 04:30:00    905.0
  2018-09-08 05:00:00    920.0
  2018-09-08 05:30:00    926.0
  2018-09-08 06:00:00    931.0
  2018-09-08 06:30:00    942.0
  2018-09-08 07:00:00    948.0
  2018-09-08 07:30:00    956.0
  2018-09-08 08:00:00    981.0
  Freq: 30T, Name: Load_Avg, dtype: float64
  </code></pre>
  
  <pre><code>print(df_event_num[4]['Load_Avg'])
  &gt;&gt;&gt;
  TIMESTAMP
  2018-09-30 21:00:00    252.2
  2018-09-30 21:30:00    256.5
  2018-09-30 22:00:00    264.1
  2018-09-30 22:30:00    271.1
  2018-09-30 23:00:00    277.7
  2018-09-30 23:30:00    310.0
  2018-10-01 00:00:00    331.6
  2018-10-01 00:30:00    356.3
  2018-10-01 01:00:00    397.2
  2018-10-01 01:30:00    422.4
  2018-10-01 02:00:00    444.2
  2018-10-01 02:30:00    464.7
  2018-10-01 03:00:00    477.2
  2018-10-01 03:30:00    487.2
  2018-10-01 04:00:00    494.7
  2018-10-01 04:30:00    515.2
  2018-10-01 05:00:00    527.6
  2018-10-01 05:30:00    537.5
  2018-10-01 06:00:00    541.7
  Freq: 30T, Name: Load_Avg, dtype: float64
  </code></pre>