40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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participant = "p031"
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all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]
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for sensor in all_sensors:
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if sensor == "eda":
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path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
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elif sensor == "bvp":
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path = f"/rapids/data/interim/{participant}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
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elif sensor == "ibi":
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path = f"/rapids/data/interim/{participant}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
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elif sensor == "acc":
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path = f"/rapids/data/interim/{participant}/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
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elif sensor == "temp":
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path = f"/rapids/data/interim/{participant}/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
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else:
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path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
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df = pd.read_csv(path)
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print(df)
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is_NaN = df.isnull()
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row_has_NaN = is_NaN.any(axis=1)
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rows_with_NaN = df[row_has_NaN]
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print("All rows:", len(df.index))
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print("\nCount NaN vals:", rows_with_NaN.size)
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print("\nDf mean:")
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print(df.mean())
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sns.heatmap(df.isna(), cbar=False)
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plt.savefig(f'{sensor}_{participant}_windows_NaN.png', bbox_inches='tight')
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