image_output_1.png
import matplotlib.pyplot as plt

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

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

plt.figure(figsize=(13, 10))
plt.subplot(221)
plt.hist(sales_train.shop_id, bins=25)
plt.xlabel('shop_id')
plt.ylabel('counts')
plt.title('train_data')
plt.subplot(222)
plt.hist(test.shop_id, bins=25)
plt.ylabel('counts')
plt.xlabel('shop_id')
plt.title('test_data')
plt.subplot(223)
plt.hist(sales_train.item_id, bins=25)
plt.xlabel('item_id')
plt.ylabel('counts')
plt.title('train_data')
plt.subplot(224)
plt.hist(test.item_id, bins=25)
plt.ylabel('counts')
plt.xlabel('item_id')
plt.title('test_data')
plt.show()