Dataframe is like that
and I want to extract that row values into same dataframe.
df["row"][0] values like
[{'acceleration': 0, 'distance': 0.917, 'energy_cost': 4.644, 'estimated_energy': 76.323, 'half': 1, 'metabolic_power': 17.043, 'pos_x': -27.14, 'pos_y': 50.03, 'speed': 3.67, 'video_second': 5.0}, {'acceleration': 0, 'distance': 0.848, 'energy_cost': 4.647, 'estimated_energy': 70.546, 'half': 1, 'metabolic_power': 15.753, 'pos_x': -27.988, 'pos_y': 50.022, 'speed': 3.39, 'video_second': 5.25}, {'acceleration': 0, 'distance': 0.848, 'energy_cost': 3.978, 'estimated_energy': 60.39, 'half': 1, 'metabolic_power': 13.485, 'pos_x': -28.835, 'pos_y': 50.015, 'speed': 3.39, 'video_second': 5.5}, {'acceleration': 0, 'distance': 0.848, 'energy_cost': 4.647, 'estimated_energy': 70.546, 'half': 1, 'metabolic_power': 15.753, 'pos_x': -29.682, 'pos_y': 50.008, 'speed': 3.39, 'video_second': 5.75}, {'acceleration': 0, 'distance': 0.848, 'energy_cost': 4.647, 'estimated_energy': 70.546, 'half': 1, 'metabolic_power': 15.753, 'pos_x': -30.53, 'pos_y': 50.0, 'speed': 3.39, 'video_second': 6.0}, {'acceleration': 0.445, 'distance': 0.959, 'energy_cost': 4.647, 'estimated_energy': 79.805, 'half': 1, 'metabolic_power': 17.82, 'pos_x': -31.345, 'pos_y': 50.505, 'speed': 3.835, 'video_second': 6.25}, {'acceleration': 0, 'distance': 0.959, 'energy_cost': 5.91, 'estimated_energy': 101.505, 'half': 1, 'metabolic_power': 22.666, 'pos_x': -32.16, 'pos_y': 51.01, 'speed': 3.835, 'video_second': 6.5}, {'acceleration': 0.0, 'distance': 0.959, 'energy_cost': 4.647, 'estimated_energy': 79.805, 'half': 1, 'metabolic_power': 17.82, 'pos_x': -32.975, 'pos_y': 51.515, 'speed': 3.835, 'video_second': 6.75}, {'acceleration': 0, 'distance': 0.959, 'energy_cost': 4.644, 'estimated_energy': 79.761, 'half': 1, 'metabolic_power': 17.81, 'pos_x': -33.79, 'pos_y': 52.02, 'speed': 3.835, 'video_second': 7}]
Desired dataframe is like id, number, acc, distance, ... and sorted with video second.
How can I do that?
CodePudding user response：
Try:
from ast import literal_eval
# if not converted already, apply ast.literal_eval
df["row"] = df["row"].apply(literal_eval)
df = df.explode("row")
df = pd.concat([df, df.pop("row").apply(pd.Series)], axis=1).sort_values(
"video_second"
)
print(df)
Prints:
id number acceleration distance energy_cost estimated_energy half metabolic_power pos_x pos_y speed video_second
0 340 6 0.000 0.917 4.644 76.323 1.0 17.043 -27.140 50.030 3.670 5.00
0 340 6 0.000 0.848 4.647 70.546 1.0 15.753 -27.988 50.022 3.390 5.25
0 340 6 0.000 0.848 3.978 60.390 1.0 13.485 -28.835 50.015 3.390 5.50
0 340 6 0.000 0.848 4.647 70.546 1.0 15.753 -29.682 50.008 3.390 5.75
0 340 6 0.000 0.848 4.647 70.546 1.0 15.753 -30.530 50.000 3.390 6.00
0 340 6 0.445 0.959 4.647 79.805 1.0 17.820 -31.345 50.505 3.835 6.25
0 340 6 0.000 0.959 5.910 101.505 1.0 22.666 -32.160 51.010 3.835 6.50
0 340 6 0.000 0.959 4.647 79.805 1.0 17.820 -32.975 51.515 3.835 6.75
0 340 6 0.000 0.959 4.644 79.761 1.0 17.810 -33.790 52.020 3.835 7.00