Datasets:
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209d7ae 9ceb405 209d7ae 7d4b475 9ceb405 7d4b475 23378b2 7d4b475 23378b2 7d4b475 23378b2 9ceb405 209d7ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | from datasets import load_dataset
import pandas as pd
### Measurements by cell ###
measurements_by_cell = load_dataset('CelfAI/COOPER','measurements_by_cell')
measurements_by_cell_data_train = measurements_by_cell['train'].to_pandas()
measurements_by_cell_data_test = measurements_by_cell['test'].to_pandas()
measurements_by_cell_data = pd.concat([measurements_by_cell_data_train, measurements_by_cell_data_test])
### Topology ###
topology = load_dataset('CelfAI/COOPER','topology')
topology_data = topology['main'].to_pandas()
### Performance indicators meanings ###
performance_indicators_meanings = load_dataset('CelfAI/COOPER','performance_indicators_meanings')
performance_indicators_meanings_data = performance_indicators_meanings['main'].to_pandas()
#### Optionally Join Measurements by cell and Topology ###
all_data = pd.merge(measurements_by_cell_data, topology_data, on='LocalCellName', how='left')
pm_columns=[x for x in measurements_by_cell_data.columns.tolist() if x not in ['LocalCellName', 'datetime']]
mean_by_cell= measurements_by_cell_data.groupby('LocalCellName')[pm_columns].mean().reset_index()
min_by_cell= measurements_by_cell_data.groupby('LocalCellName')[pm_columns].min().reset_index()
mean_by_band= all_data.groupby('Band')[pm_columns].mean().reset_index()
mean_by_site= all_data.groupby('SiteLabel')[pm_columns].mean().reset_index()
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