text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv', parse_dates=['date'])
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
data_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')

sales_train = sales_train[sales_train.item_price < 40000]
sales_train = sales_train[sales_train.item_cnt_day < 200]
sales_train = sales_train[sales_train.item_cnt_day > -1]
columns = ['date', 'date_block_num', 'shop_id', 'item_id', 'item_price', 'item_cnt_day']
sales_train.drop_duplicates(columns, keep='first', inplace=True)

plt.figure(figsize=(10, 10))
plt.scatter(sales_train.item_cnt_day, sales_train.item_price)
plt.show()