Project 2: Holiday weather
by Rob Griffiths and Suzanne Lees, 11 September 2015, updated 11 April 2017, 18 October 2017, 20 December 2017, and 6 August 2017
This is the project notebook for the second part of The Open University's Learn to code for Data Analysis course.
There is nothing I like better than taking a holiday. In the winter I like to have a two week break in a country where I can be guaranteed sunny dry days. In the summer I like to have two weeks off relaxing in my garden in London. However I'm often disappointed because I pick a fortnight when the weather is dull and it rains. So in this project I am going to use the historic weather data from the Weather Underground for London to try to predict two good weather weeks to take off as holiday next summer. Of course the weather in the summer of 2016 may be very different to 2014 but it should give me some indication of when would be a good time to take a summer break.
Getting the data
Weather Underground keeps historical weather data collected in many airports around the world. Right-click on the following URL and choose 'Open Link in New Window' (or similar, depending on your browser):
http://www.wunderground.com/history
When the new page opens start typing 'LHR' in the 'Location' input box and when the pop up menu comes up with the option 'LHR, United Kingdom' select it and then click on 'Submit'.
When the next page opens with London Heathrow data, click on the 'Custom' tab and select the time period From: 1 January 2014 to: 31 December 2014 and then click on 'Get History'. The data for that year should then be displayed further down the page.
You can copy each month's data directly from the browser to a text editor like Notepad or TextEdit, to obtain a single file with as many months as you wish.
Weather Underground has changed in the past the way it provides data and may do so again in the future. I have therefore collated the whole 2014 data in the provided 'London_2014.csv' file.
Now load the CSV file into a dataframe making sure that any extra spaces are skipped:
Cleaning the data
First we need to clean up the data. I'm not going to make use of 'WindDirDegrees'
in my analysis, but you might in yours so we'll rename 'WindDirDegrees< br />'
to 'WindDirDegrees'
.
remove the < br />
html line breaks from the values in the 'WindDirDegrees'
column.
and change the values in the 'WindDirDegrees'
column to float64
:
We definitely need to change the values in the 'GMT'
column into values of the datetime64
date type.
We also need to change the index from the default to the datetime64
values in the 'GMT'
column so that it is easier to pull out rows between particular dates and display more meaningful graphs:
Finding a summer break
According to meteorologists, summer extends for the whole months of June, July, and August in the northern hemisphere and the whole months of December, January, and February in the southern hemisphere. So as I'm in the northern hemisphere I'm going to create a dataframe that holds just those months using the datetime
index, like this:
I now look for the days with warm temperatures.
GMT | Max TemperatureC | Mean TemperatureC | Min TemperatureC | Dew PointC | MeanDew PointC | Min DewpointC | Max Humidity | Mean Humidity | Min Humidity | ... | Max VisibilityKm | Mean VisibilityKm | Min VisibilitykM | Max Wind SpeedKm/h | Mean Wind SpeedKm/h | Max Gust SpeedKm/h | Precipitationmm | CloudCover | Events | WindDirDegrees | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GMT |
0 rows × 23 columns
Summer 2014 was rather cool in London: there are no days with temperatures of 25 Celsius or higher. Best to see a graph of the temperature and look for the warmest period.
So next we tell Jupyter to display any graph created inside this notebook:
Now let's plot the 'Mean TemperatureC'
for the summer:
Well looking at the graph the second half of July looks good for mean temperatures over 20 degrees C so let's also put precipitation on the graph too:
The second half of July is still looking good, with just a couple of peaks showing heavy rain. Let's have a closer look by just plotting mean temperature and precipitation for July.
Yes, second half of July looks pretty good, just two days that have significant rain, the 25th and the 28th and just one day when the mean temperature drops below 20 degrees, also the 28th.
Conclusions
The graphs have shown the volatility of a British summer, but a couple of weeks were found when the weather wasn't too bad in 2014. Of course this is no guarantee that the weather pattern will repeat itself in future years. To make a sensible prediction we would need to analyse the summers for many more years. By the time you have finished this course you should be able to do that.
Moscow
I will now analyse the data for Moscow!
Getting the Data:
Cleaning the Data:
Finding a summer break:
Date | Max TemperatureC | Mean TemperatureC | Min TemperatureC | Dew PointC | MeanDew PointC | Min DewpointC | Max Humidity | Mean Humidity | Min Humidity | ... | Max VisibilityKm | Mean VisibilityKm | Min VisibilitykM | Max Wind SpeedKm/h | Mean Wind SpeedKm/h | Max Gust SpeedKm/h | Precipitationmm | CloudCover | Events | WindDirDegrees | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | |||||||||||||||||||||
2014-06-02 | 2014-06-02 | 27 | 22 | 17 | 12 | 8 | 4 | 68 | 42 | 23 | ... | 10.0 | 10.0 | 10.0 | 29 | 16 | 43.0 | 0.0 | 7.0 | Rain | 115.0 |
2014-06-03 | 2014-06-03 | 29 | 20 | 12 | 11 | 6 | 3 | 77 | 40 | 19 | ... | NaN | NaN | NaN | 21 | 13 | 35.0 | 0.0 | NaN | NaN | 137.0 |
2014-06-04 | 2014-06-04 | 29 | 20 | 11 | 12 | 8 | 2 | 82 | 45 | 18 | ... | 8.0 | 8.0 | 8.0 | 26 | 11 | 43.0 | 0.0 | 1.0 | NaN | 108.0 |
2014-06-05 | 2014-06-05 | 31 | 22 | 14 | 14 | 8 | 2 | 77 | 44 | 16 | ... | NaN | NaN | NaN | 18 | 10 | 35.0 | 0.0 | NaN | NaN | 116.0 |
2014-06-06 | 2014-06-06 | 31 | 22 | 14 | 13 | 9 | 6 | 82 | 45 | 21 | ... | 10.0 | 10.0 | 10.0 | 32 | 5 | NaN | 0.0 | 6.0 | Thunderstorm | 154.0 |
2014-06-07 | 2014-06-07 | 30 | 22 | 14 | 17 | 13 | 10 | 94 | 64 | 31 | ... | 10.0 | 9.0 | 2.0 | 29 | 5 | 29.0 | 0.0 | 6.0 | Rain-Thunderstorm | 144.0 |
2014-06-08 | 2014-06-08 | 27 | 21 | 15 | 15 | 13 | 12 | 94 | 63 | 39 | ... | 10.0 | 9.0 | 8.0 | 21 | 10 | NaN | 0.0 | 4.0 | NaN | 256.0 |
2014-07-01 | 2014-07-01 | 28 | 22 | 17 | 14 | 12 | 11 | 72 | 52 | 37 | ... | NaN | NaN | NaN | 26 | 19 | 43.0 | 0.0 | NaN | NaN | 186.0 |
2014-07-02 | 2014-07-02 | 29 | 22 | 14 | 16 | 13 | 9 | 100 | 61 | 32 | ... | 10.0 | 10.0 | 7.0 | 32 | 13 | 47.0 | 0.0 | 6.0 | Rain-Thunderstorm | 155.0 |
2014-07-14 | 2014-07-14 | 30 | 21 | 13 | 13 | 10 | 8 | 94 | 49 | 25 | ... | 9.0 | 9.0 | 9.0 | 14 | 10 | 29.0 | 0.0 | 1.0 | NaN | 138.0 |
2014-07-15 | 2014-07-15 | 30 | 22 | 14 | 15 | 11 | 8 | 88 | 51 | 27 | ... | NaN | NaN | NaN | 18 | 5 | 32.0 | 0.0 | NaN | NaN | 208.0 |
2014-07-16 | 2014-07-16 | 31 | 23 | 16 | 16 | 13 | 11 | 82 | 52 | 31 | ... | 10.0 | 10.0 | 10.0 | 18 | 5 | 21.0 | 0.0 | 5.0 | Thunderstorm | 178.0 |
2014-07-17 | 2014-07-17 | 27 | 21 | 16 | 19 | 17 | 15 | 94 | 75 | 48 | ... | 10.0 | 9.0 | 5.0 | 21 | 6 | 40.0 | 0.0 | 5.0 | Rain-Thunderstorm | 12.0 |
2014-07-20 | 2014-07-20 | 27 | 20 | 13 | 13 | 11 | 9 | 88 | 57 | 34 | ... | 10.0 | 10.0 | 9.0 | 14 | 8 | 32.0 | 0.0 | 3.0 | NaN | 2.0 |
2014-07-26 | 2014-07-26 | 26 | 21 | 17 | 16 | 12 | 8 | 73 | 54 | 36 | ... | 10.0 | 10.0 | 10.0 | 26 | 16 | 47.0 | 0.0 | 3.0 | NaN | 327.0 |
2014-07-27 | 2014-07-27 | 29 | 20 | 11 | 13 | 10 | 8 | 88 | 54 | 30 | ... | NaN | NaN | NaN | 18 | 8 | 29.0 | 0.0 | NaN | NaN | 319.0 |
2014-07-28 | 2014-07-28 | 32 | 23 | 15 | 13 | 12 | 10 | 82 | 49 | 26 | ... | 9.0 | 9.0 | 9.0 | 18 | 8 | NaN | 0.0 | 1.0 | NaN | 278.0 |
2014-07-29 | 2014-07-29 | 32 | 23 | 15 | 14 | 12 | 8 | 82 | 47 | 22 | ... | 7.0 | 5.0 | 2.0 | 26 | 8 | 50.0 | 0.0 | 6.0 | NaN | 233.0 |
2014-07-31 | 2014-07-31 | 32 | 24 | 18 | 17 | 12 | 8 | 88 | 48 | 22 | ... | 9.0 | 7.0 | 5.0 | 21 | 10 | 40.0 | 0.0 | 1.0 | NaN | 237.0 |
2014-08-01 | 2014-08-01 | 33 | 24 | 16 | 14 | 12 | 10 | 77 | 47 | 24 | ... | 9.0 | 9.0 | 9.0 | 14 | 5 | 26.0 | 0.0 | 6.0 | NaN | 172.0 |
2014-08-03 | 2014-08-03 | 29 | 24 | 19 | 19 | 16 | 11 | 94 | 65 | 35 | ... | 10.0 | 9.0 | 7.0 | 26 | 10 | NaN | 0.0 | 6.0 | Rain | 14.0 |
2014-08-04 | 2014-08-04 | 29 | 22 | 16 | 14 | 13 | 10 | 82 | 56 | 34 | ... | NaN | NaN | NaN | 29 | 13 | 35.0 | 0.0 | NaN | NaN | 14.0 |
2014-08-05 | 2014-08-05 | 29 | 23 | 17 | 16 | 13 | 12 | 82 | 56 | 35 | ... | 10.0 | 10.0 | 10.0 | 29 | 14 | 47.0 | 0.0 | 3.0 | NaN | 29.0 |
2014-08-06 | 2014-08-06 | 30 | 23 | 17 | 16 | 14 | 12 | 88 | 58 | 37 | ... | 10.0 | 10.0 | 9.0 | 29 | 14 | 43.0 | 0.0 | 4.0 | NaN | 43.0 |
2014-08-07 | 2014-08-07 | 29 | 23 | 16 | 17 | 15 | 13 | 100 | 72 | 37 | ... | 10.0 | 10.0 | 9.0 | 32 | 11 | 35.0 | 0.0 | 6.0 | Rain-Thunderstorm | 43.0 |
2014-08-09 | 2014-08-09 | 29 | 22 | 15 | 17 | 14 | 11 | 100 | 66 | 33 | ... | 10.0 | 10.0 | 10.0 | 21 | 10 | 32.0 | 0.0 | 3.0 | NaN | 347.0 |
2014-08-10 | 2014-08-10 | 28 | 23 | 18 | 17 | 16 | 15 | 83 | 65 | 45 | ... | 10.0 | 10.0 | 10.0 | 32 | 10 | 35.0 | 0.0 | 6.0 | Rain | 298.0 |
2014-08-11 | 2014-08-11 | 29 | 22 | 16 | 16 | 14 | 12 | 94 | 63 | 37 | ... | 10.0 | 10.0 | 10.0 | 14 | 8 | NaN | 0.0 | 4.0 | Fog | 7.0 |
2014-08-12 | 2014-08-12 | 31 | 23 | 17 | 17 | 15 | 13 | 88 | 59 | 33 | ... | 10.0 | 10.0 | 10.0 | 21 | 8 | 32.0 | 0.0 | 6.0 | NaN | 162.0 |
2014-08-13 | 2014-08-13 | 24 | 20 | 16 | 19 | 16 | 12 | 100 | 75 | 60 | ... | 10.0 | 9.0 | 5.0 | 26 | 11 | NaN | 0.0 | 7.0 | Rain | 234.0 |
2014-08-15 | 2014-08-15 | 26 | 21 | 16 | 20 | 18 | 15 | 100 | 85 | 61 | ... | 10.0 | 10.0 | 6.0 | 29 | 14 | 32.0 | 0.0 | 6.0 | Rain-Thunderstorm | 229.0 |
2014-08-21 | 2014-08-21 | 27 | 22 | 16 | 13 | 12 | 9 | 77 | 54 | 34 | ... | 10.0 | 10.0 | 10.0 | 14 | 8 | 32.0 | 0.0 | 7.0 | Rain | 245.0 |
32 rows × 23 columns
For my ideal holiday, I would have little rain, low humidity, high visibility, and temperatures between 20 and 25 degrees C. The temperature condition was satisfied for the 32 dates above. I will now disregard any of these 32 dates for which there was rain.
Date | Max TemperatureC | Mean TemperatureC | Min TemperatureC | Dew PointC | MeanDew PointC | Min DewpointC | Max Humidity | Mean Humidity | Min Humidity | ... | Max VisibilityKm | Mean VisibilityKm | Min VisibilitykM | Max Wind SpeedKm/h | Mean Wind SpeedKm/h | Max Gust SpeedKm/h | Precipitationmm | CloudCover | Events | WindDirDegrees | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | |||||||||||||||||||||
2014-06-02 | 2014-06-02 | 27 | 22 | 17 | 12 | 8 | 4 | 68 | 42 | 23 | ... | 10.0 | 10.0 | 10.0 | 29 | 16 | 43.0 | 0.0 | 7.0 | Rain | 115.0 |
2014-06-03 | 2014-06-03 | 29 | 20 | 12 | 11 | 6 | 3 | 77 | 40 | 19 | ... | NaN | NaN | NaN | 21 | 13 | 35.0 | 0.0 | NaN | NaN | 137.0 |
2014-06-04 | 2014-06-04 | 29 | 20 | 11 | 12 | 8 | 2 | 82 | 45 | 18 | ... | 8.0 | 8.0 | 8.0 | 26 | 11 | 43.0 | 0.0 | 1.0 | NaN | 108.0 |
2014-06-05 | 2014-06-05 | 31 | 22 | 14 | 14 | 8 | 2 | 77 | 44 | 16 | ... | NaN | NaN | NaN | 18 | 10 | 35.0 | 0.0 | NaN | NaN | 116.0 |
2014-06-06 | 2014-06-06 | 31 | 22 | 14 | 13 | 9 | 6 | 82 | 45 | 21 | ... | 10.0 | 10.0 | 10.0 | 32 | 5 | NaN | 0.0 | 6.0 | Thunderstorm | 154.0 |
2014-06-07 | 2014-06-07 | 30 | 22 | 14 | 17 | 13 | 10 | 94 | 64 | 31 | ... | 10.0 | 9.0 | 2.0 | 29 | 5 | 29.0 | 0.0 | 6.0 | Rain-Thunderstorm | 144.0 |
2014-06-08 | 2014-06-08 | 27 | 21 | 15 | 15 | 13 | 12 | 94 | 63 | 39 | ... | 10.0 | 9.0 | 8.0 | 21 | 10 | NaN | 0.0 | 4.0 | NaN | 256.0 |
2014-07-01 | 2014-07-01 | 28 | 22 | 17 | 14 | 12 | 11 | 72 | 52 | 37 | ... | NaN | NaN | NaN | 26 | 19 | 43.0 | 0.0 | NaN | NaN | 186.0 |
2014-07-02 | 2014-07-02 | 29 | 22 | 14 | 16 | 13 | 9 | 100 | 61 | 32 | ... | 10.0 | 10.0 | 7.0 | 32 | 13 | 47.0 | 0.0 | 6.0 | Rain-Thunderstorm | 155.0 |
2014-07-14 | 2014-07-14 | 30 | 21 | 13 | 13 | 10 | 8 | 94 | 49 | 25 | ... | 9.0 | 9.0 | 9.0 | 14 | 10 | 29.0 | 0.0 | 1.0 | NaN | 138.0 |
2014-07-15 | 2014-07-15 | 30 | 22 | 14 | 15 | 11 | 8 | 88 | 51 | 27 | ... | NaN | NaN | NaN | 18 | 5 | 32.0 | 0.0 | NaN | NaN | 208.0 |
2014-07-16 | 2014-07-16 | 31 | 23 | 16 | 16 | 13 | 11 | 82 | 52 | 31 | ... | 10.0 | 10.0 | 10.0 | 18 | 5 | 21.0 | 0.0 | 5.0 | Thunderstorm | 178.0 |
2014-07-17 | 2014-07-17 | 27 | 21 | 16 | 19 | 17 | 15 | 94 | 75 | 48 | ... | 10.0 | 9.0 | 5.0 | 21 | 6 | 40.0 | 0.0 | 5.0 | Rain-Thunderstorm | 12.0 |
2014-07-20 | 2014-07-20 | 27 | 20 | 13 | 13 | 11 | 9 | 88 | 57 | 34 | ... | 10.0 | 10.0 | 9.0 | 14 | 8 | 32.0 | 0.0 | 3.0 | NaN | 2.0 |
2014-07-26 | 2014-07-26 | 26 | 21 | 17 | 16 | 12 | 8 | 73 | 54 | 36 | ... | 10.0 | 10.0 | 10.0 | 26 | 16 | 47.0 | 0.0 | 3.0 | NaN | 327.0 |
2014-07-27 | 2014-07-27 | 29 | 20 | 11 | 13 | 10 | 8 | 88 | 54 | 30 | ... | NaN | NaN | NaN | 18 | 8 | 29.0 | 0.0 | NaN | NaN | 319.0 |
2014-07-28 | 2014-07-28 | 32 | 23 | 15 | 13 | 12 | 10 | 82 | 49 | 26 | ... | 9.0 | 9.0 | 9.0 | 18 | 8 | NaN | 0.0 | 1.0 | NaN | 278.0 |
2014-07-29 | 2014-07-29 | 32 | 23 | 15 | 14 | 12 | 8 | 82 | 47 | 22 | ... | 7.0 | 5.0 | 2.0 | 26 | 8 | 50.0 | 0.0 | 6.0 | NaN | 233.0 |
2014-07-31 | 2014-07-31 | 32 | 24 | 18 | 17 | 12 | 8 | 88 | 48 | 22 | ... | 9.0 | 7.0 | 5.0 | 21 | 10 | 40.0 | 0.0 | 1.0 | NaN | 237.0 |
2014-08-01 | 2014-08-01 | 33 | 24 | 16 | 14 | 12 | 10 | 77 | 47 | 24 | ... | 9.0 | 9.0 | 9.0 | 14 | 5 | 26.0 | 0.0 | 6.0 | NaN | 172.0 |
2014-08-03 | 2014-08-03 | 29 | 24 | 19 | 19 | 16 | 11 | 94 | 65 | 35 | ... | 10.0 | 9.0 | 7.0 | 26 | 10 | NaN | 0.0 | 6.0 | Rain | 14.0 |
2014-08-04 | 2014-08-04 | 29 | 22 | 16 | 14 | 13 | 10 | 82 | 56 | 34 | ... | NaN | NaN | NaN | 29 | 13 | 35.0 | 0.0 | NaN | NaN | 14.0 |
2014-08-05 | 2014-08-05 | 29 | 23 | 17 | 16 | 13 | 12 | 82 | 56 | 35 | ... | 10.0 | 10.0 | 10.0 | 29 | 14 | 47.0 | 0.0 | 3.0 | NaN | 29.0 |
2014-08-06 | 2014-08-06 | 30 | 23 | 17 | 16 | 14 | 12 | 88 | 58 | 37 | ... | 10.0 | 10.0 | 9.0 | 29 | 14 | 43.0 | 0.0 | 4.0 | NaN | 43.0 |
2014-08-07 | 2014-08-07 | 29 | 23 | 16 | 17 | 15 | 13 | 100 | 72 | 37 | ... | 10.0 | 10.0 | 9.0 | 32 | 11 | 35.0 | 0.0 | 6.0 | Rain-Thunderstorm | 43.0 |
2014-08-09 | 2014-08-09 | 29 | 22 | 15 | 17 | 14 | 11 | 100 | 66 | 33 | ... | 10.0 | 10.0 | 10.0 | 21 | 10 | 32.0 | 0.0 | 3.0 | NaN | 347.0 |
2014-08-10 | 2014-08-10 | 28 | 23 | 18 | 17 | 16 | 15 | 83 | 65 | 45 | ... | 10.0 | 10.0 | 10.0 | 32 | 10 | 35.0 | 0.0 | 6.0 | Rain | 298.0 |
2014-08-11 | 2014-08-11 | 29 | 22 | 16 | 16 | 14 | 12 | 94 | 63 | 37 | ... | 10.0 | 10.0 | 10.0 | 14 | 8 | NaN | 0.0 | 4.0 | Fog | 7.0 |
2014-08-12 | 2014-08-12 | 31 | 23 | 17 | 17 | 15 | 13 | 88 | 59 | 33 | ... | 10.0 | 10.0 | 10.0 | 21 | 8 | 32.0 | 0.0 | 6.0 | NaN | 162.0 |
2014-08-13 | 2014-08-13 | 24 | 20 | 16 | 19 | 16 | 12 | 100 | 75 | 60 | ... | 10.0 | 9.0 | 5.0 | 26 | 11 | NaN | 0.0 | 7.0 | Rain | 234.0 |
2014-08-15 | 2014-08-15 | 26 | 21 | 16 | 20 | 18 | 15 | 100 | 85 | 61 | ... | 10.0 | 10.0 | 6.0 | 29 | 14 | 32.0 | 0.0 | 6.0 | Rain-Thunderstorm | 229.0 |
2014-08-21 | 2014-08-21 | 27 | 22 | 16 | 13 | 12 | 9 | 77 | 54 | 34 | ... | 10.0 | 10.0 | 10.0 | 14 | 8 | 32.0 | 0.0 | 7.0 | Rain | 245.0 |
32 rows × 23 columns
In fact, it did not rain on any of these 32 dates! I will show graphically the precipitation and rainfall between the earliest and latest of the above dates.
I will now include the visibility and humidity in this graph. Since there was no rain during this time period, I will no longer include this data.
The graph looks misleading because the values for humidity are much higher than those for visibility and temperature. In an attempt to combat this, I will scale the humidity down.
Now it appears that the visibility does not vary by enough for it to impact my decision. I wish to choose the 2 week period with most temperatures in my chosen range and low humidity. It looks like the end of July/beginning of August is the best. I will restrict my consideration to the time period 15th July to 15th August. I will now show the maximum and minimum humidity and temperature.
With this amount of variation, it is difficult to choose the best 2 weeks.
Conclusion
Using the data about Moscow, and my personal weather preferences, I have concluded that the best time to visit Moscow is between late July and early August. However, during this time period, humidity and temperature did not vary with enough significance to choose a specific 2 week period.