text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future-sales/test.csv')
items = pd.read_csv('../input/competitive-data-science-predict-future-sales/items.csv')
item_category = pd.read_csv('../input/competitive-data-science-predict-future-sales/item_categories.csv')
shops = pd.read_csv('../input/competitive-data-science-predict-future-sales/shops.csv')

Monthly_sales = sales.groupby(['date_block_num', 'shop_id'])['item_cnt_day'].sum().reset_index(name='item_cnt_month')
fig, axs = plt.subplots(10, 6)
for i in range(60):
    shop_sale_per_month = Monthly_sales.loc[Monthly_sales['shop_id'] == i]
    axs[i // 6, i % 6].tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False, left=False, labelleft=False)
    axs[i // 6, i % 6].plot(shop_sale_per_month['date_block_num'], shop_sale_per_month['item_cnt_month'])
del Monthly_sales, shop_sale_per_month