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

"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')

train.columns

"""
now for the structures
"""
structures.columns

"""
looking for outliers.
soo..
we are dealing with:
molecule_name', 'atom_index', 'atom', 'x', 'y', 'z'  - Structres.
'id', 'molecule_name', 'atom_index_0', 'atom_index_1', 'type','scalar_coupling_constant'
"""
sample_data = train.sample(500)
all_data = pd.merge(sample_data, structures, how='left', on='molecule_name')

"""
there is a huge peack around the 0 of this constant.
but I do not think these are na values or outliers.
lets now check if the train and test appeare to be from the same distrebution or not.
"""
print(test.columns)
structures_train = structures.loc[structures['molecule_name'].isin(train['molecule_name'])]
structures_test = structures.loc[structures['molecule_name'].isin(test['molecule_name'])]
print(structures_train.shape)
print(structures_test.shape)