In this post,
We will load the data from the csv file from given URL and then do some basic processing and then display the extracted parameters from the data. and plot the data graph.
Programming Language: Python 3
Software: Anaconda Jupyter Notebook
Here, csv file contains temperature data and the format of the the csv file is as follows:
Run the following code in Anaconda Jupyter Notebook.
code block and its expected output is shown below:
Code In[1]:
# coding: utf-8
# In[1]:
## EXAMPLE: Reading a file from a given URL
## Read data from URL and plot its graph
## URL here is from thinkspeak server.
## DATA FORMAT through URL is as follows:
#
# created_at entry_id field1
# 2018-01-04 17:00:54 UTC 1 21.52
# 2018-01-04 17:02:44 UTC 2 21.52
# 2018-01-04 17:03:05 UTC 3 21.82
## Run this code in Anaconda Jupyter (Python 3)
import pandas as pd
#model_data = pd.read_csv('https://thingspeak.com/channels/396517/field/1.csv?apikey=IWX3DY3PBB7E8WVX')
model_data = pd.read_csv('https://thingspeak.com/channels/396517/feed.csv?apikey=IWX3BV3PCQ7E8WUX')
print (type(model_data))
print (model_data)
Output In[1]:
created_at entry_id field1
0 2018-01-04 17:00:54 UTC 1 21.52
1 2018-01-04 17:02:44 UTC 2 21.52
2 2018-01-04 17:03:05 UTC 3 21.82
3 2018-01-04 17:03:24 UTC 4 21.82
4 2018-01-04 17:03:45 UTC 5 21.52
5 2018-01-04 17:04:06 UTC 6 21.52
6 2018-01-04 17:04:26 UTC 7 21.52
7 2018-01-04 17:04:46 UTC 8 21.82
8 2018-01-04 17:05:08 UTC 9 21.52
9 2018-01-04 17:05:27 UTC 10 21.52
10 2018-01-04 17:05:50 UTC 11 21.52
11 2018-01-04 17:06:06 UTC 12 21.82
12 2018-01-04 17:06:28 UTC 13 21.52
13 2018-01-04 17:06:49 UTC 14 21.52
14 2018-01-04 17:07:15 UTC 15 21.52
15 2018-01-04 17:07:48 UTC 16 21.52
16 2018-01-04 17:08:03 UTC 17 21.82
17 2018-01-04 17:08:21 UTC 18 21.52
18 2018-01-04 17:08:44 UTC 19 21.52
19 2018-01-04 17:09:04 UTC 20 21.52
20 2018-01-04 17:09:28 UTC 21 21.52
21 2018-01-04 17:09:46 UTC 22 21.52
22 2018-01-04 17:10:07 UTC 23 21.52
23 2018-01-04 17:10:22 UTC 24 21.82
24 2018-01-04 17:10:37 UTC 25 21.52
25 2018-01-04 17:11:01 UTC 26 21.52
26 2018-01-04 17:11:21 UTC 27 21.52
27 2018-01-04 17:11:39 UTC 28 21.52
28 2018-01-04 17:11:59 UTC 29 21.52
29 2018-01-04 17:12:21 UTC 30 21.52
30 2018-01-04 17:12:44 UTC 31 21.52
31 2018-01-04 17:13:05 UTC 32 21.52
32 2018-01-04 17:13:24 UTC 33 21.52
33 2018-01-04 17:13:40 UTC 34 21.52
34 2018-01-04 17:14:00 UTC 35 21.52
35 2018-01-04 17:14:22 UTC 36 21.52
36 2018-01-04 17:14:46 UTC 37 21.52
37 2018-01-04 17:15:04 UTC 38 21.52
38 2018-01-04 17:15:34 UTC 39 21.52
39 2018-01-04 17:15:49 UTC 40 21.52
40 2018-01-04 17:16:13 UTC 41 21.52
41 2018-01-04 17:16:33 UTC 42 21.52
42 2018-01-04 17:16:52 UTC 43 21.52
43 2018-01-04 17:17:11 UTC 44 21.52
44 2018-01-04 17:17:32 UTC 45 21.82
Code In[2]:
# In[2]:
model_data.tail()
Output In[2]:
created_at entry_id field1
40 2018-01-04 17:16:13 UTC 41 21.52
41 2018-01-04 17:16:33 UTC 42 21.52
42 2018-01-04 17:16:52 UTC 43 21.52
43 2018-01-04 17:17:11 UTC 44 21.52
44 2018-01-04 17:17:32 UTC 45 21.82
Code In[3]:
# In[3]:
model_data.shape
Output In[3]:
(45, 3)
Code In[4]:
# In[4]:
model_data.describe()
Output In[4]:
entry_id field1
count 45.000000 45.000000
mean 23.000000 21.566667
std 13.133926 0.109959
min 1.000000 21.520000
25% 12.000000 21.520000
50% 23.000000 21.520000
75% 34.000000 21.520000
max 45.000000 21.820000
Code In[5]:
# In[5]:
model_data['field1'].describe()
Output In[5]:
count 45.000000
mean 21.566667
std 0.109959
min 21.520000
25% 21.520000
50% 21.520000
75% 21.520000
max 21.820000
Name: field1, dtype: float64
Code In[6]:
# In[6]:
model_data.columns.values
Output In[6]:
array(['created_at', 'entry_id', 'field1'], dtype=object)
Code In[7]:
# In[7]:
model_data.dtypes
Output In[7]:
created_at object
entry_id int64
field1 float64
dtype: object
Code In[8]:
# In[8]:
import matplotlib.pyplot as plt
model_data["field1"].plot()
plt.show()
Output In[8]:
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Feel free to ask doubts in the comment section. I will try my best to solve it.
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This is the simplest way to encourage me to keep doing such work.
Thanks and Regards,
-Akshay P. Daga
