Note
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Data Load (Pandas DataFrame)¶
Experiment initialization and data preparation
import pandas as pd
from piml import Experiment
exp = Experiment()
data = pd.read_csv('https://github.com/SelfExplainML/PiML-Toolbox/blob/main/datasets/BikeSharing.csv?raw=true')
exp.data_loader(data=data)
season yr mnth hr holiday weekday workingday weathersit temp \
0 1 0 1 0 0 6 0 1 0.24
1 1 0 1 1 0 6 0 1 0.22
2 1 0 1 2 0 6 0 1 0.22
3 1 0 1 3 0 6 0 1 0.24
4 1 0 1 4 0 6 0 1 0.24
... ... .. ... .. ... ... ... ... ...
17374 1 1 12 19 0 1 1 2 0.26
17375 1 1 12 20 0 1 1 2 0.26
17376 1 1 12 21 0 1 1 1 0.26
17377 1 1 12 22 0 1 1 1 0.26
17378 1 1 12 23 0 1 1 1 0.26
atemp hum windspeed cnt
0 0.2879 0.81 0.0000 16
1 0.2727 0.80 0.0000 40
2 0.2727 0.80 0.0000 32
3 0.2879 0.75 0.0000 13
4 0.2879 0.75 0.0000 1
... ... ... ... ...
17374 0.2576 0.60 0.1642 119
17375 0.2576 0.60 0.1642 89
17376 0.2576 0.60 0.1642 90
17377 0.2727 0.56 0.1343 61
17378 0.2727 0.65 0.1343 49
[17379 rows x 13 columns]
Total running time of the script: ( 0 minutes 8.890 seconds)
Estimated memory usage: 19 MB
