text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])

missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])

cnt = state_ts['RegionName'].value_counts().to_frame()

state_vise = state_ts.groupby(['RegionName']).median()
state_vise.shape

fig, ax = plt.subplots(1, 2, figsize=(16, 10), sharey='all')
ax1, ax2 = ax.flatten()
sns.barplot(state_vise['InventorySeasonallyAdjusted_AllHomes'], state_vise.index, palette='magma', ax=ax1)
sns.barplot(state_vise['InventoryRaw_AllHomes'], state_vise.index, palette='magma', ax=ax2)