How to reset x tick labels in matplotlib - python-3.x

Question

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.
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()
And I got a graph like this
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?
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'.
print(df_event_num[0]['Load_Avg'])
>>>
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
print(df_event_num[1]['Load_Avg'])
>>>
TIMESTAMP
2018-04-25 06:30:00 1133.0
2018-04-25 07:00:00 1159.0
Freq: 30T, Name: Load_Avg, dtype: float64
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
print(df_event_num[3]['Load_Avg'])
>>>
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
print(df_event_num[4]['Load_Avg'])
>>>
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

Answer 1

I modified your code a little bit:
You do not need to use range() to loop over, you can iterate directly over the list of DataFrames
Use the created ax subplot to set the data and the title on it.
Create 5 linear separated ticks on the x-axis based on the first and last index of the individual dataframe: pd.to_datetime(np.linspace(df.index[0].value, df.index[-1].value, 5))
Use just the last value as label, and replace all other with empty stings: ts_names = ['','','','',ts_loc[-1]]
import numpy as np
colors_2018 = ['red', 'blue', 'green', 'yellow', 'orange', 'brown']
fig = plt.figure(figsize=(15, 4))
for i, df in enumerate(df_event_num): # because I have 5 DataFrames in 'df_event_num'
ax = plt.subplot(1,5,i+1)
ax.plot(df['Load_Avg'], color=colors_2018[i])
ax.set_title('event_num{}'.format(i))
# If the index is not a Timestamp-type already:
df.index = pd.to_datetime(df.index)
# x-Axis locations of 5 timestamps
ts_loc = pd.to_datetime(np.linspace(df.index[0].value, df.index[-1].value, 5))
ax.set_xticks(ts_loc, minor=False)
# Names of the timestamps (only last shown)
ts_names = ['','','','',ts_loc[-1]]
ax.set_xticklabels(ts_names, rotation="vertical")
fig.tight_layout()