text_html_output_1.png
import pandas as pd

import seaborn as sns

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

sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
              data = items,
              order = items['item_category_id'].value_counts(ascending=True).index)

sales_train.groupby('shop_id').mean()

sns.set(rc={'figure.figsize': (13, 13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color='salmon')