# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

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

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.
import numpy as np

train_accuracy = pd.read_csv("../input/train.csv", usecols = ['x', 'accuracy'])


amax = train_accuracy["accuracy"].max()
print (amax)
amin = train_accuracy["accuracy"].min()
print(amin)
mediana = train_accuracy["accuracy"].median()
print(mediana)

newt = train_accuracy.loc[train_accuracy["accuracy"] >= 62]

print (newt)