text_html_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

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

import seaborn as sns

sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')

train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')

stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)

fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets

for i in range(2):
    axes[i].tick_params(axis = 'x',labelsize =20)
    axes[i].tick_params(axis = 'y', labelsize = 20)

axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)  
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)   
plt.show()

targ_score.shape