# # 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

# 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 read-only "../input/" directory
# # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

# import os
# for dirname, _, filenames in os.walk('/kaggle/input'):
#     for filename in filenames:
#         print(os.path.join(dirname, filename))

# # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

import pandas as pd
import numpy as np

train_csv = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
train_csv.head()

train_csv.info()