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13.3.5. Getting a subset of a time series and creating a Scatter plot

Here you learn how to:

1. plot a time series using dots (scatter plot)

2. Get subset of time series data and plot them

First, some packages:

# These packages are necessary later on. Load all the packages in one place for consistency
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime

Load the data:

#The path of the directory where all AMF data are
path_dir = Path.cwd()/'data'/'1'
name_of_site = 'CA-Obs_clean.csv.gz'
path_data = path_dir/name_of_site
path_data.resolve()
df_data = pd.read_csv(path_data, index_col='time',parse_dates=['time'])
df_data.head()

	WS	RH	TA	PA	WD	P	SWIN	LWIN	SWOUT	LWOUT	NETRAD	H	LE
time
1997-01-01 00:30:00	2.988	73.036	-24.570	93.942	102.84	NaN	-0.14	213.39	0.01	214.83	-1.59	NaN	NaN
1997-01-01 01:00:00	2.671	73.146	-24.562	93.887	96.09	NaN	-0.03	216.73	0.03	215.03	1.64	NaN	NaN
1997-01-01 01:30:00	2.303	73.151	-24.431	93.934	112.34	NaN	0.24	223.46	0.02	215.91	7.77	NaN	NaN
1997-01-01 02:00:00	2.789	73.093	-24.379	93.917	109.16	NaN	-0.04	218.32	0.00	215.72	2.56	NaN	NaN
1997-01-01 02:30:00	2.274	73.140	-24.284	93.977	115.74	NaN	0.14	217.89	0.01	215.99	2.02	NaN	NaN

Plot the net all-wave radiation:

df_data['NETRAD'].plot(figsize=(12,5))

<matplotlib.axes._subplots.AxesSubplot at 0x26ffae40f28>

If you want to focus on a subset of the data, use this feature of pandas package (DataFrame.loc[index1:index2]):

df_sub=df_data.loc['2002 07 01':'2002 07 30']
df_sub.head()

	WS	RH	TA	PA	WD	P	SWIN	LWIN	SWOUT	LWOUT	NETRAD	H	LE
time
2002-07-01 00:00:00	3.508	51.637	14.268	93.494	266.48	0.0	0.13	286.54	-0.20	361.24	-74.37	-3.451	0.175
2002-07-01 00:30:00	3.183	53.221	13.799	93.509	265.86	0.0	0.07	282.77	-0.23	358.10	-75.02	NaN	NaN
2002-07-01 01:00:00	3.759	55.664	13.182	93.506	266.07	0.0	-0.11	281.14	-0.14	356.13	-74.96	-14.770	1.628
2002-07-01 01:30:00	4.518	52.553	13.217	93.500	276.19	0.0	0.02	282.81	0.15	362.15	-79.47	-69.300	12.360
2002-07-01 02:00:00	3.940	52.343	13.150	93.484	275.58	0.0	-0.35	282.55	0.15	362.48	-80.43	-35.270	3.713

df_sub.index

DatetimeIndex(['2002-07-01 00:00:00', '2002-07-01 00:30:00',
               '2002-07-01 01:00:00', '2002-07-01 01:30:00',
               '2002-07-01 02:00:00', '2002-07-01 02:30:00',
               '2002-07-01 03:00:00', '2002-07-01 03:30:00',
               '2002-07-01 04:00:00', '2002-07-01 04:30:00',
               ...
               '2002-07-30 19:00:00', '2002-07-30 19:30:00',
               '2002-07-30 20:00:00', '2002-07-30 20:30:00',
               '2002-07-30 21:00:00', '2002-07-30 21:30:00',
               '2002-07-30 22:00:00', '2002-07-30 22:30:00',
               '2002-07-30 23:00:00', '2002-07-30 23:30:00'],
              dtype='datetime64[ns]', name='time', length=1440, freq=None)

We have a subset of df_data which contains the data from July 2002. Now let’s plot it:

df_sub['NETRAD'].plot(figsize=(15,5))
plt.ylabel('Temp ($\degree$C)')

Text(0, 0.5, 'Temp ($\\degree$C)')

You can specify the date and time of interest in the subset:

df_sub.loc['2002 07 12 6:00:00':'2002 07 12 22:00:00']['TA'].plot(figsize=(15,5))
plt.ylabel('Temp ($\degree$C)')

Text(0, 0.5, 'Temp ($\\degree$C)')

Sometimes we want to plot points instead of lines (e.g. if there are missing data, large variations in the data etc). To do this use plt.scatter:

Y=df_sub['TA']
X=df_sub.index

fig,ax=plt.subplots(1,1,figsize=(15,5))
plt.scatter(X,Y)
plt.xlim([df_sub.index.date.min(),df_sub.index.date.max()])
plt.ylabel('Temp ($\degree$C)')

Text(0, 0.5, 'Temp ($\\degree$C)')

The marker color and style can be changed (different types of markers are shown here):

Y=df_sub['TA']
X=df_sub.index

fig,ax=plt.subplots(1,1,figsize=(15,5))
plt.scatter(X,Y,color='r',marker='+')
plt.xlim([df_sub.index.date.min(),df_sub.index.date.max()])
plt.ylabel('Temp ($\degree$C)')

Text(0, 0.5, 'Temp ($\\degree$C)')