Weather modelisation

Machine learning project that aims to model weather conditions in several French cities.

Context

The goal of this project is to study the temporal evolution of temperature and wind in France, across one year.

Required packages

• pandas : to manipulate dataframes.
• numpy : to manipulate arrays.
• matplotlib : to plot graphs.
• cartopy : to plot maps.
• IPython : to display dataframes in Jupyter Notebook.
• scikit-learn : to perform machine learning tasks.
• Other packages that are available by default in any Python distribution (datetime...).

Data

Data are provided through 3 files, localted in the data folder:

• dataGPS.csv : contains the GPS coordinates of the cities.
• dataTemp.csv : contains the temperature data.
• dataWind.csv : contains the wind data.

All of them share a common ID, that can be used to merge them into a single dataframe.

Below are the first rows of each dataframe, after removing the ID prefix in each column to have better readability.

GPS data

ID	Lattitude	Longitude
3426	2.5	51
3510	2	50.5
3511	2.5	50.5
3512	3	50.5
3513	3.5	50.5

Temperature data

ID	01-01 00:00:00	01-01 01:00:00	01-01 02:00:00	01-01 03:00:00	01-01 04:00:00
3426	1.5	2.2	2.8	3.5	4.1
3510	0.8	1.8	2.9	3.9	4.3
3511	1	1.6	2.4	3.1	3.5
3512	1.4	1.9	2.4	2.9	3.4
3513	1.4	1.8	2.3	2.7	3.1

Wind data

ID	01-01 00:00:00	01-01 01:00:00	01-01 02:00:00	01-01 03:00:00	01-01 04:00:00
3426	17.1607	17.2627	17.3666	17.5034	17.0188
3510	18.2784	18.1728	18.2395	18.3	17.818
3511	17.0658	17.0426	17.1657	17.2142	16.8003
3512	16.1409	16.1276	16.2364	16.2693	15.9154
3513	16.4551	16.5387	16.646	16.7765	16.4247