如何在时间h1：m1和h2：m2之间用x_lims绘制图形

Question

I'm working on a project with loads of temperature data and I'm currently processing and plotting all of my data. However, I keep falling foul when I try to set x_lims on my plots between a time1 (9:00) and time2 (21:00)

Data background:

The sensor has collected data every second for two weeks and I've split the main data file into smaller daily files (eg dayX). Each day contains a timestamp (column = 'timeStamp') and a mean temperature (column = 'meanT').

The data for each day has been presliced just slightly over the window I want to plot (ie dayX contains data from 8:55:00 - 21:05:00). The dataset contains NaN values at some points as the sensors were not worn and data needed to be discarded.

Goal:

What I want to do is to be able to plot the dayX data between a set time interval (x_lim = 9:00 - 21:00). As I have many days of data, I eventually want to plot each day using the same x axis (I want them as separate figures however, not subplots), but each day has different gaps in the main data set, so I want to set constant x lims. As I have many different days of data, I'd rather not have to specify the date as well as the time.

Example data:

dayX = 
    timeStamp              meanT
    2018-05-10 08:55:00    NaN
             .              .
             .              .
             .              .
    2018-05-10 18:20:00    32.4
             .              .
             .              . 
             .              . 
    2018-05-10 21:05:00    32.0

What I've tried:

    import pandas as pd
    import numpy as np
    import datetime as dt
    import matplotlib.pyplot as plt
    from matplotlib.dates import date2num, DateFormatter

    dayX = pd.read_csv('path/to/file/dayX.csv)
    dayX['timeStamp'] = pd.to_datetime(dayX['timeStamp'], format=%Y %m %d %H:%M:%S.%f')

    fig, ax1 = plt.subplots(1,1)
    ax1.plot(dayX['timeStamp'], dayX['meanT'])
    ax1.xaxis.set_major_formatter(DateFormatter('%H:%M'))
    ax1.set_xlim(pd.Timestamp('9:00'), pd.Timestamp('21:00'))
    fig.autofmt_xdate()

    plt.show()

Which gives:

If I remove the limit line however, the data plots okay, but the limits are automatically selected

    # Get rid of this line:
    ax1.set_xlim(pd.Timestamp('9:00'), pd.Timestamp('21:00'))
    # Get this:

I'm really not sure why this is going wrong or what else I should be trying.

Answer 1

Your timeStamp is a datetime object. All you got to do is pass the datetime objects as the limits.

import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.dates import date2num, DateFormatter

dayX = df
dayX['timeStamp'] = pd.to_datetime(dayX['timeStamp'], format='%Y-%m-%d %H:%M:%S')

fig, ax1 = plt.subplots(1,1)
ax1.plot(dayX['timeStamp'], dayX['meanT'])
ax1.xaxis.set_major_formatter(DateFormatter('%H:%M'))
ax1.set_xlim(df['timeStamp'].min().replace(hour=9), df['timeStamp'].min().replace(hour=21))
fig.autofmt_xdate()

plt.show()

Output:

Answer 2

You probably need to construct a full timestamp object since it'll default to today's date, which has no data in your case. the following snippet shoudl replace the ax1.set_xlim line in your code, and should also work for starting and ending multiday time ranges on specific hours of your choosing.

min_h = 9 # hours
max_h = 21 # hours

start = dayX['timeStamp'].min()
end = dayX['timeStamp'].max()

xmin = pd.Timestamp(year=start.year, month=start.month, day=start.day, hour=min_h)
xmax = pd.Timestamp(year=end.year, month=end.month, day=end.day, hour=max_h)

ax1.set_xlim(xmin, xmax)