text_plain_output_1.png
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')

dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
    pass
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
    dispDF(df, name)

dispDF(potential_energy_df, 'potential energy')