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")

sales_train.groupby('shop_id').sum()

sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)

sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sub_sales_df['index_shop'], y=sub_sales_df['item_cnt_day'], order=sub_sales_df['index_shop'],color="salmon")

sns.set(rc={'figure.figsize': (10, 10)})
ax = sns.kdeplot(sales_train['item_price'], color='black', shade=True)