I am working on a project of analyzing weather data. Below is a abbreviated version of my csv file (only focus on the last column "Conditions"):
Year,Month,Day,Hour,DOW,Maximum Temperature,Minimum Temperature,Temperature,Precipitation,Snow,SnowDepth,Wind Speed,Visibility,Cloud Cover,Relative Humidity,Conditions
2020,3,5,8,3,48.0,48.0,48.0,0.0,0.0,0.0,10.3,9.9,0.0,81.44,Clear
2020,3,5,10,3,56.9,56.9,56.9,0.0,0.0,0.0,6.3,9.9,25.1,55.29,Partially cloudy
2020,3,9,8,0,60.7,60.7,60.7,0.0,0.0,0.0,14.5,8.1,79.6,91.95,Overcast
2020,3,9,10,0,62.5,62.5,62.5,0.01,0.0,0.0,16.0,7.0,94.7,89.95,"Rain, Overcast"
2020,3,17,20,1,66.4,66.4,66.4,0.02,0.0,0.0,8.7,4.3,68.6,88.78,"Rain, Partially cloudy"
and I want to transfer it to something like this:
Clear,Partially cloudy,Rain,Overcast
1,0,0,0
0,1,0,0
0,0,0,1
0,0,1,1
0,1,1,0
I saw that I could use the code below but I don't know how to deal with the condition when I have 2 categories in one data.
dataset['Conditions'] = dataset['Conditions'].map({1: 'Clear', 2: 'Partially cloudy', 3: 'Rain', 4: 'Snow'})
dataset = pd.get_dummies(dataset, columns=['Conditions'], prefix='', prefix_sep='')
Thank you in advance : )
Try str.split + explode then sum level 0:
dummies = pd.get_dummies(
dataset['Conditions'].str.split(', ').explode()
).sum(level=0)
print(dummies)
dummies:
Clear Overcast Partially cloudy Rain
0 1 0 0 0
1 0 0 1 0
2 0 1 0 0
3 0 1 0 1
4 0 0 1 1
To join back to the original DataFrame:
dummies = pd.get_dummies(
dataset['Conditions'].str.split(', ').explode()
).sum(level=0)
# Join Back to dataset
dataset = dataset.drop(columns='Conditions').join(dummies)
print(dataset.to_string())
Year Month Day Hour ... Clear Overcast Partially cloudy Rain
0 2020 3 5 8 ... 1 0 0 0
1 2020 3 5 10 ... 0 0 1 0
2 2020 3 9 8 ... 0 1 0 0
3 2020 3 9 10 ... 0 1 0 1
4 2020 3 17 20 ... 0 0 1 1