天池_短租数据分析

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rc("font",family="SimHei",size="12")  #解决中文无法显示的问题

#导入数据
listings=pd.read_csv(‘F:\\python\\天池_短租数据分析\\listings.csv‘)

listings.info()
listings.head(5)
listings.shape
listings.describe()

#数据缺失计算
missing=listings.isnull().sum()
missing[missing>0].plot.bar()

#数据特征
numeric_features = listings.select_dtypes(include=[np.number])
numeric_features.columns
#类别特征
categorical_features = listings.select_dtypes(include=[np.object])
categorical_features.columns

for cat_fea in categorical_features:
    print(cat_fea + "的特征分布如下：")
    print("{}特征有个{}不同的值".format(cat_fea, listings[cat_fea].nunique()))
    print(listings[cat_fea].value_counts())

#使用str.split
listings[‘neighbourhood‘]=listings[‘neighbourhood‘].str.split(‘/‘).str[0]

listings=listings.drop(‘neighbourhood_group‘,axis=1)

#按照neighbourhood和room_type分组，计算每个值的个数和price的均值
neigh_roomtype=listings.groupby([‘neighbourhood‘,‘room_type‘]).agg({‘id‘:‘size‘,‘price‘:‘mean‘})
#将ID改为number
neigh_roomtype=neigh_roomtype.rename(columns={‘id‘:‘number‘})

#先对number计算统计可视化等
number_n_r=neigh_roomtype.unstack()[‘number‘]
number_n_r.plot(figsize=(12,5),title=‘图1：不同房屋类型在不同地区的数量‘)

#画饼图
number_n_r.sum(1).sort_values().plot.pie(figsize=(6,6),autopct=‘%.2f%%‘,title=‘图2：房屋地区占比‘)

#画饼图
number_n_r.sum(0).sort_values().plot.pie(figsize=(6,6),autopct=‘%.2f%%‘,title=‘图3：房屋类型占比‘)