application_vnd.jupyter.stderr_output_1.png
import matplotlib.pyplot as plt

import numpy as np # linear algebra

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

import seaborn as sn

data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')

from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)

data_train = data_train_db.values

labels = 'Runs', 'Does not run'
sizes = [np.sum(data_train[:,0]), np.sum(1-data_train[:,0])]

fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=90)
ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

plt.show()

corrMatrix = data_train_db.corr().abs()
plt.figure(figsize=(12, 12))
sn.heatmap(corrMatrix, annot=False)
plt.show()
plt.figure(figsize=(12, 6))
plt.plot(np.arange(1, 100), corrMatrix['Running'][1:100])
plt.title('Correlation with Running')
plt.show()