#%%
# 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
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
#%%
train=pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
shops=pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
items=pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
cat=pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
train=train.sample(frac=0.1,random_state=42)
这看起来就是本地导入啊...