21  API to download Thingspeak data with Python

An API (Application Programming Interface) is a set of rules and protocols that allow different software applications to communicate with each other.

In the code below, we will use Thingspeak’s API to read data, save it to file, and finally load it to python.

21.1 Import relevant packages

import urllib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.dates as mdates

21.2 Download data from Thingspeak and save them to file

# define what to download
channels = "2076217"
fields = "1,2,3"

# see here all the "Query String Parameters"
# https://www.mathworks.com/help/thingspeak/readdata.html

results = 100
# Download the last N results
# url = f"https://api.thingspeak.com/channels/{channels}/fields/{fields}.csv?results={results}"

minutes = "30"
# Number of minutes before NOW to include in response.
# url = f"https://api.thingspeak.com/channels/{channels}/fields/{fields}.csv?minutes={minutes}"

start = "2023-04-16%2000:00:00"  # format YYYY-MM-DD%20HH:NN:SS
end = "2023-04-19%2000:00:00"    # format YYYY-MM-DD%20HH:NN:SS
url = f"https://api.thingspeak.com/channels/{channels}/fields/{fields}.csv?start={start}&end={end}"

# Open the URL and get the response
data = urllib.request.urlopen(url)
# Read the response data
d = data.read()
# save data to csv
filename1 = "downloaded_data.csv"
file = open(filename1, "w")
file.write(d.decode('UTF-8'))
file.close()

21.3 Load data as a pandas dataframe

# load data
df = pd.read_csv(filename1)
# rename columns
df = df.rename(columns={"created_at": "timestamp",
                        "field1": "Temp",
                        "field2": "RH",
                        "field3": "VPD",
                        # "field4": "whatever",
                        })
# set timestamp as index
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df.set_index('timestamp')
df

	entry_id	Temp	RH	VPD
timestamp
2023-04-16 00:01:20+00:00	3892	21.59	72.31	0.71442
2023-04-16 00:06:22+00:00	3893	21.60	72.14	0.71925
2023-04-16 00:11:24+00:00	3894	21.59	72.38	0.71262
2023-04-16 00:16:25+00:00	3895	21.59	71.86	0.72603
2023-04-16 00:21:27+00:00	3896	21.54	71.49	0.73333
...	...	...	...	...
2023-04-18 18:58:48+00:00	4691	23.93	55.29	1.32931
2023-04-18 19:03:50+00:00	4692	23.98	55.25	1.33450
2023-04-18 19:08:52+00:00	4693	23.92	55.07	1.33505
2023-04-18 19:13:53+00:00	4694	23.92	59.84	1.19332
2023-04-18 19:18:17+00:00	4695	23.92	57.23	1.27087

804 rows × 4 columns

21.4 Plot data

Now you can manipulate your data however you want! Here we will simply plot one of the fields.

fig, ax = plt.subplots(1)
ax.plot(df['Temp'])
ax.set(ylabel="temperature (°C)")
plt.gcf().autofmt_xdate()  # makes slanted dates