File import

File import#

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
from urllib.request import urlretrieve
plt.rcParams.update({'font.size': 14})

Part 0: Download and Import and Interpret the Data Set#

\(\text{Task 7.1:}\)

Complete the code cell below to import the data. Use the commented lines of code to interpret the contents.

def findfile(fname):
    if not os.path.isfile(fname):
        print(f"Downloading {fname}...")
        urlretrieve('http://files.mude.citg.tudelft.nl/'+fname, fname)

findfile('chla.csv')

Downloading chla.csv...

h = pd.read_csv('chla.csv', delimiter=',', header=1)

h.columns=['Date', 'Chlfa']
h.head()
Date Chlfa
0 3/1/14 1:00 7.055628
1 3/1/14 2:00 7.018217
2 3/1/14 3:00 7.060986
3 3/1/14 4:00 7.340142
4 3/1/14 5:00 7.717564

Unfortunately we can’t create a plot to visualize the data with the Date information because matplotlib doesn’t know how to interpret the value as it’s stored as text.

Part 1: Use Pandas to Plot the Time Series with datetime#

The Pandas method is datetime, which converts the object type from a generic (un-usable-for-plotting) data type ito a datetime type. A full explanation of this is outside the scope of MUDE, so we simply illustrate it below. For our purposes, note that:

  • the datetime object created by to_datetime is a significant improvement on the standard Python functionality

  • Pandas provides a lot of methods that can use it (we only scratch the surface here)

  • to learn more, if you are interested, there is plenty available online, like the tutorial here

\(\text{Task 7.2:}\)

Study the code cell below to see how to create a datetime object. Note in particular the dtype printed in the Pandas output, indicating the method was successful.

h['Date'] = pd.to_datetime(h['Date'], format='%m/%d/%y %H:%M')
h['Date']

0      2014-03-01 01:00:00
1      2014-03-01 02:00:00
2      2014-03-01 03:00:00
3      2014-03-01 04:00:00
4      2014-03-01 05:00:00
               ...        
5874   2014-10-31 19:00:00
5875   2014-10-31 20:00:00
5876   2014-10-31 21:00:00
5877   2014-10-31 22:00:00
5878   2014-10-31 23:00:00
Name: Date, Length: 5879, dtype: datetime64[ns]

\(\text{Task 7.3:}\)

Now you can run the cell below to visualize the data!

plt.figure(figsize=(10, 6))
plt.plot(h['Date'], h['Chlfa'],'k.')
plt.xlabel('Date')
plt.ylabel('Concentrations [mg/m3]')
plt.grid()
plt.title('Concentrations of chlorophyll');
../../_images/f5e810d28bb1dd7f16a382ab473b71fcfc99b8737e8679c647de177a448eb41e.png

By Tom van Woudenberg and Robert Lanzafame, Delft University of Technology. CC BY 4.0, more info on the Credits page of Workbook.

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