Timeseries plot from CSV data (Timestamp and events): x-label constant

Question

(This question can be read alone, but is a sequel to: Timeseries from CSV data (Timestamp and events))

I would like to visualize CSV data (from 2 files) as shown below, by a timeseries representation, using python's pandas module (see links below).

Sample data of df1:

             TIMESTAMP  eventid
0  2017-03-20 02:38:24        1
1  2017-03-21 05:59:41        1
2  2017-03-23 12:59:58        1
3  2017-03-24 01:00:07        1
4  2017-03-27 03:00:13        1

The 'eventid' column always contains the value of 1, and I am trying to show the sum of events for each day in the dataset. The 2nd dataset, df0, has similar structure but contains only zeros:

Sample data of df0:

             TIMESTAMP  eventid
0  2017-03-21 01:38:24        0
1  2017-03-21 03:59:41        0
2  2017-03-22 11:59:58        0
3  2017-03-24 01:03:07        0
4  2017-03-26 03:50:13        0

The x-axis label only shows the same date, and my question is: How can the different dates be shown? (What causes the same date to be shown multiple times on x labels?)

script so far:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

df1 = pd.read_csv('timestamp01.csv',  parse_dates=True, index_col='TIMESTAMP')
df0 = pd.read_csv('timestamp00.csv',  parse_dates=True, index_col='TIMESTAMP')

f, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(df0.resample('D').size())

ax1.set_xlim([pd.to_datetime('2017-01-27'), pd.to_datetime('2017-04-30')])  

ax1.xaxis.set_major_formatter(ticker.FixedFormatter
(df0.index.strftime('%Y-%m-%d')))

plt.setp(ax1.xaxis.get_majorticklabels(), rotation=15)

ax2.plot(df1.resample('D').size())
ax2.set_xlim([pd.to_datetime('2017-03-22'), pd.to_datetime('2017-04-29')])
ax2.xaxis.set_major_formatter(ticker.FixedFormatter(df1.index.strftime
('%Y-%m-%d')))
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=15)
plt.show()

Output: (https://www.dropbox.com/s/z21koflkzglm6c3/figure_1.png?dl=0)

Links I have tried to follow:

• http://pandas.pydata.org/pandas-docs/stable/visualization.html

• Multiple timeseries plots from Pandas Dataframe

• Pandas timeseries plot setting x-axis major and minor ticks and labels

Any help is much appreciated.

Answer 1

Making the example reproducible, we can create the following text file (data/timestamp01.csv):

TIMESTAMP;eventid
2017-03-20 02:38:24;1
2017-03-21 05:59:41;1
2017-03-23 12:59:58;1
2017-03-24 01:00:07;1
2017-03-27 03:00:13;1

(same for data/timestamp00.csv). We can then read them in

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

df1 = pd.read_csv('data/timestamp01.csv', parse_dates=True, index_col='TIMESTAMP', sep=";")
df0 = pd.read_csv('data/timestamp00.csv', parse_dates=True, index_col='TIMESTAMP', sep=";")

Plotting them

f, (ax1, ax2) = plt.subplots(1, 2)

ax1.plot(df0.resample('D').size())
ax2.plot(df1.resample('D').size())

plt.setp(ax1.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.show()

results in

which is the desired plot.