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import numpy as np # linear algebra

import os

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

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import os
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../input/') else '../input'

train = pd.read_csv(f'{data_dir}/train.csv')
test = pd.read_csv(f'{data_dir}/test.csv')
sub = pd.read_csv(f'{data_dir}/sample_submission.csv')
structures = pd.read_csv(f'{data_dir}/structures.csv')

print('train shape', train.shape)
print('test shape', train.shape)
print('structures shape', structures.shape)
print('sub', sub.shape)
print('train cols', list(train.columns))
print('test cols', list(test.columns))
print('structures cols', list(structures.columns))
print('structures atoms', list(np.unique(structures['atom'])))
print('')
print(f"There are {train['molecule_name'].nunique()} distinct molecules in train data.")
print(f"There are {test['molecule_name'].nunique()} distinct molecules in test data.")
print(f"There are {structures['atom'].nunique()} unique atoms in structures")
print(f"There are {train['type'].nunique()} unique types in train")
print(f"There are {test['type'].nunique()} unique types in test")
are_the_same_types = np.all(sorted(train['type'].unique()) == sorted(test['type'].unique()))
print(f'Are all types in train and test the same? {are_the_same_types}')