text_plain_output_1.png
import matplotlib.pyplot as plt

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

import seaborn as sns

data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')

data.dtypes

data_city_price = data[['city_name', 'price', 'property_type']]

import matplotlib.pyplot as plt
per = data_city_price.isnull().sum() / data.shape[0] * 100
per.plot.barh()

data_cp = data_city_price.dropna()
data_cp.shape

import seaborn as sns
fig_dims = (14, 10)
fig, ax = plt.subplots(figsize=fig_dims)
sns.barplot(x='city_name', y='price', hue='property_type', ax=ax, data=data_cp)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
ax.set_xlabel('City')
ax.set_ylabel('Prices')
plt.show()