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Load weather data and station metadata

import pandas as pd
import geopandas as gpd

Load weather data and station metadata#

weather_data = pd.read_csv('./weather_data.csv', index_col=0)
weather_data.index = pd.DatetimeIndex(weather_data.index)
weather_data
BROCKVILLE PCC;MEAN_TEMPERATURE BROCKVILLE PCC;MIN_TEMPERATURE BROCKVILLE PCC;MAX_TEMPERATURE BROCKVILLE PCC;TOTAL_PRECIPITATION POWELL RIVER A;MEAN_TEMPERATURE POWELL RIVER A;MIN_TEMPERATURE POWELL RIVER A;MAX_TEMPERATURE POWELL RIVER A;TOTAL_PRECIPITATION STONY MOUNTAIN;MEAN_TEMPERATURE STONY MOUNTAIN;MIN_TEMPERATURE ... CHATHAM POINT;MAX_TEMPERATURE CHATHAM POINT;TOTAL_PRECIPITATION GREENWOOD A;MEAN_TEMPERATURE GREENWOOD A;MIN_TEMPERATURE GREENWOOD A;MAX_TEMPERATURE GREENWOOD A;TOTAL_PRECIPITATION BROOKS;MEAN_TEMPERATURE BROOKS;MIN_TEMPERATURE BROOKS;MAX_TEMPERATURE BROOKS;TOTAL_PRECIPITATION
LOCAL_DATE
1990-01-01 -5.0 -10.0 0.0 0.0 3.6 2.0 5.2 0.0 -13.0 -25.0 ... 3.5 1.2 2.2 -5.0 9.3 8.4 0.5 -2.2 3.1 0.0
1990-01-02 -4.0 -9.0 1.0 0.0 0.9 -2.0 3.8 1.8 -10.5 -13.0 ... 4.2 13.2 -3.3 -8.0 1.4 0.0 -8.6 -15.2 -1.9 0.0
1990-01-03 0.5 -4.0 5.0 0.0 3.7 1.9 5.5 3.2 -15.0 -18.0 ... 5.5 20.2 -1.7 -6.4 3.1 0.0 -7.1 -15.4 1.2 0.0
1990-01-04 4.0 2.0 6.0 2.4 5.9 3.8 8.0 1.0 -21.0 -25.0 ... 6.9 11.8 -0.3 -7.1 6.6 3.0 -12.3 -15.1 -9.4 0.0
1990-01-05 -3.0 -4.0 -2.0 0.0 5.7 2.2 9.2 14.0 -18.5 -25.0 ... 8.5 34.2 -0.7 -8.0 6.6 0.0 -10.3 -15.1 -5.4 0.7
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-12-28 -3.0 -9.0 3.0 0.0 -5.0 -7.0 -3.0 0.6 -22.0 -26.5 ... -3.0 0.0 -2.4 -7.2 2.5 4.2 -27.3 -37.3 -17.4 0.2
2021-12-29 -2.5 -4.0 -1.0 0.0 -7.5 -12.0 -3.0 8.0 -30.0 -35.0 ... -1.5 5.0 0.2 -1.5 1.8 0.4 -24.0 -27.7 -20.2 0.0
2021-12-30 -1.0 -3.0 1.0 0.0 -3.3 -5.0 -1.5 0.0 -29.5 -36.0 ... 0.0 0.0 0.4 -1.5 2.2 0.0 -24.9 -31.3 -18.6 0.2
2021-12-31 NaN NaN NaN NaN -6.0 -10.5 -1.5 0.0 -31.5 -35.0 ... -0.5 0.0 0.9 0.0 1.8 3.0 -27.3 -33.3 -21.3 0.0
2022-01-01 -0.5 -2.5 1.5 8.8 -3.8 -10.5 3.0 11.4 -32.3 -38.0 ... 4.0 29.2 3.9 0.6 7.1 5.3 -19.4 -33.9 -4.9 0.0

11689 rows × 428 columns

station_metadata = pd.read_csv("./station_metadata.csv", index_col=0)
station_metadata
x y STATION_NAME FILENAME PROVINCE_CODE min_date max_date mean_temp_coverage max_temp_coverage total_precipitation_coverage direction_max_gust_coverage speed_max_gust_coverage min_rel_humiudity_coverage max_rel_humiudity_coverage
0 -75.666667 44.600000 BROCKVILLE PCC station_4236_data.csv ON 1990-01-01 2022-01-01 0.998624 0.999570 0.999398 0.000000 0.000000 0.000000 0.000000
1 -124.500278 49.834169 POWELL RIVER A station_327_data.csv BC 1990-01-01 2022-01-01 0.982635 0.982981 0.999914 0.000000 0.000000 0.000000 0.000000
2 -97.166667 50.116667 STONY MOUNTAIN station_3678_data.csv MB 1990-01-01 2022-01-01 0.999825 1.000000 0.999912 0.000000 0.000000 0.000000 0.000000
3 -82.933333 42.333333 WINDSOR RIVERSIDE station_4715_data.csv ON 1993-12-01 2022-01-01 1.000000 1.000000 0.999902 0.000000 0.000000 0.000000 0.000000
5 -77.533333 44.116667 TRENTON A station_5126_data.csv ON 1990-01-01 2022-01-01 0.953286 0.953286 0.953114 0.889711 0.899948 0.926445 0.926531
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
106 -93.966667 48.633333 BARWICK station_3932_data.csv ON 1990-01-01 2022-01-01 0.998912 0.999275 0.999637 0.000000 0.000000 0.000000 0.000000
107 -110.283333 54.416667 COLD LAKE A station_2832_data.csv AB 1990-01-01 2022-01-01 0.999914 0.999914 0.999914 0.908731 0.927346 0.999052 0.999224
108 -125.445556 50.333194 CHATHAM POINT station_153_data.csv BC 1990-01-01 2022-01-01 0.999359 0.999680 0.999786 0.000000 0.000000 0.000000 0.000000
109 -64.916667 44.983333 GREENWOOD A station_6354_data.csv NS 1990-01-01 2022-01-01 0.999743 0.999743 0.999743 0.965780 0.966293 0.998460 0.998802
110 -111.848897 50.555297 BROOKS station_2180_data.csv AB 1990-01-01 2022-01-01 0.993552 0.993937 0.974112 0.494274 0.494274 0.344240 0.344048

109 rows × 14 columns

Visualize locations of included weather stations#

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
ax = world.loc[world.continent == 'North America'].plot(color='white', edgecolor='black', figsize=(12,12))
ax.scatter(station_metadata.x, station_metadata.y, s=5, color='red')
ax

<matplotlib.axes._subplots.AxesSubplot at 0x7f9d32a2c590>
../../_images/a1bdd2288a71628e0e4ab3162699ed25457b035e654cc95b4f4c9f982f4953ca.png

Data visualization#

Examine one series of each feature type.

weather_data[('BROCKVILLE PCC;MEAN_TEMPERATURE')].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f9d332e7f50>
../../_images/96231d727b06dcde5c558be3dfb0d8c66fce1b25d998fe6d5b7a9eeb17706070.png 
weather_data[('BROCKVILLE PCC;TOTAL_PRECIPITATION')].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f9d3360da50>
../../_images/41a11d1b8e1c457be47b4eb1496e9d11a47e6289d4145e7ac1adaaa144b627c3.png

Selecting feature subsets#

feature_name = "MAX_TEMPERATURE"
weather_data[[col for col in weather_data.columns if feature_name in col]]
BROCKVILLE PCC;MAX_TEMPERATURE POWELL RIVER A;MAX_TEMPERATURE STONY MOUNTAIN;MAX_TEMPERATURE WINDSOR RIVERSIDE;MAX_TEMPERATURE TRENTON A;MAX_TEMPERATURE PEACHLAND;MAX_TEMPERATURE CASTLEGAR BCHPA DAM;MAX_TEMPERATURE OUTLOOK PFRA;MAX_TEMPERATURE SCOTT CDA;MAX_TEMPERATURE COWAN;MAX_TEMPERATURE ... MACTAQUAC PROV PARK;MAX_TEMPERATURE LEROY;MAX_TEMPERATURE WHISTLER;MAX_TEMPERATURE GREEN ISLAND;MAX_TEMPERATURE QUINSAM RIVER HATCHERY;MAX_TEMPERATURE BARWICK;MAX_TEMPERATURE COLD LAKE A;MAX_TEMPERATURE CHATHAM POINT;MAX_TEMPERATURE GREENWOOD A;MAX_TEMPERATURE BROOKS;MAX_TEMPERATURE
LOCAL_DATE
1990-01-01 0.0 5.2 -1.0 NaN 1.2 NaN 3.0 1.5 -0.5 1.0 ... 3.0 0.5 2.0 4.0 5.5 -4.0 -1.6 3.5 9.3 3.1
1990-01-02 1.0 3.8 -8.0 NaN 0.8 NaN 1.5 -8.0 -10.0 -11.0 ... -0.5 -9.5 -2.8 7.0 5.0 -0.5 -10.3 4.2 1.4 -1.9
1990-01-03 5.0 5.5 -12.0 NaN 5.1 NaN 0.5 -7.5 -13.0 -11.5 ... 3.5 -13.5 -0.1 6.5 6.0 -3.0 -12.2 5.5 3.1 1.2
1990-01-04 6.0 8.0 -17.0 NaN 5.7 NaN 2.0 -13.5 -17.0 -17.0 ... 5.0 -20.0 2.2 8.0 9.0 -11.0 -18.8 6.9 6.6 -9.4
1990-01-05 -2.0 9.2 -12.0 NaN 0.8 NaN 2.5 4.5 3.5 -14.5 ... -1.0 -3.0 4.2 9.0 8.5 -15.0 -10.8 8.5 6.6 -5.4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-12-28 3.0 -3.0 -17.5 2.5 NaN NaN -10.0 -27.8 -29.5 -20.0 ... -3.0 -27.0 -15.5 -3.0 NaN -12.0 -28.8 -3.0 2.5 -17.4
2021-12-29 -1.0 -3.0 -25.0 4.5 NaN -11.5 -10.0 -26.4 -28.5 -21.0 ... 3.0 -29.0 NaN 0.5 NaN -19.0 -28.3 -1.5 1.8 -20.2
2021-12-30 1.0 -1.5 -23.0 5.0 NaN -9.5 -7.0 -28.9 -29.4 -24.0 ... 1.0 -27.0 NaN -1.5 0.5 -16.5 -26.0 0.0 2.2 -18.6
2021-12-31 NaN -1.5 -28.0 6.5 NaN -10.5 -9.0 -29.9 -30.3 -19.0 ... 2.5 -28.0 -10.5 4.5 -2.0 -21.0 -29.0 -0.5 1.8 -21.3
2022-01-01 1.5 3.0 -26.5 4.0 NaN -4.5 -9.0 -22.6 -20.1 -17.0 ... NaN -22.0 NaN 6.0 3.0 -20.5 -22.1 4.0 7.1 -4.9

11689 rows × 107 columns

Selecting location subsets#

station_name = "BROCKVILLE PCC"
weather_data[[col for col in weather_data.columns if station_name in col]]
BROCKVILLE PCC;MEAN_TEMPERATURE BROCKVILLE PCC;MIN_TEMPERATURE BROCKVILLE PCC;MAX_TEMPERATURE BROCKVILLE PCC;TOTAL_PRECIPITATION
LOCAL_DATE
1990-01-01 -5.0 -10.0 0.0 0.0
1990-01-02 -4.0 -9.0 1.0 0.0
1990-01-03 0.5 -4.0 5.0 0.0
1990-01-04 4.0 2.0 6.0 2.4
1990-01-05 -3.0 -4.0 -2.0 0.0
... ... ... ... ...
2021-12-28 -3.0 -9.0 3.0 0.0
2021-12-29 -2.5 -4.0 -1.0 0.0
2021-12-30 -1.0 -3.0 1.0 0.0
2021-12-31 NaN NaN NaN NaN
2022-01-01 -0.5 -2.5 1.5 8.8

11689 rows × 4 columns

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