Beijing rental data analysis

1. Basic data processing

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pylab import mpl #  Set display Chinese font  
mpl.rcParams["font.sans-serif"] = ["SimHei"]


file_data=pd.read_csv("./data/ Lianjia Beijing rental data .csv")
file_data.head(10)

1.1 Processing of duplicate values and null values

#  duplicate value 
#file_data.duplicated() 
#  Delete rows with duplicate values 
file_data=file_data.drop_duplicates()   

#  Null processing 
file_data=file_data.dropna()

1.2 Data type conversion

1.2.1 Area data type conversion

#  Single value implementation 
file_data[" area (㎡)"].values[0][:-2]

#  Create an empty array 
data_new=np.array([])
data_area=file_data[" area (㎡)"].values
for i in data_area:
    data_new=np.append(data_new,np.array(i[:-2]))

data_new
# transformation data_new Data type of 
data_new=data_new.astype(np.float64)
data_new
file_data.loc[:," area (㎡)"]=data_new
file_data

1.2.2 House type expression replacement

file_data.head()

house_data=file_data[" House type "]
house_data.head()
temp_list=[]
for i in house_data:
    new_info=i.replace(" room "," room ")
    temp_list.append(new_info)
temp_list 

file_data.loc[:," House type "]=temp_list
file_data

1.3 Chart analysis

1.3.1 Number of houses available , Location distribution analysis

file_data[" Area "].unique()   # Find out all the locations of the listing information 

new_df=pd.DataFrame({
    " Area ":file_data[" Area "].unique()," Number ":0*13})

#  Get the number of listings in each area 
area_count=file_data.groupby(by=" Area ").count()
area_count

new_df[" Number "]=area_count.values
new_df.sort_values(by=" Number ",ascending=False)

1.3.2 Analysis of house type quantity

house_data=file_data[" House type "]
house_data.head()

def all_house(arr):
    key=np.unique(arr)
    result={
    }
    for k in key:
        mask=(arr==k)
        arr_new=arr[mask]
        v=arr_new.size
        result[k]=v
    return result



housr_info=all_house(house_data)
np.unique(house_data)

#  Remove the value with smaller statistical quantity 
housr_data=dict((key,value) for key,value in housr_info.items() if value>50)
house_data.head()

show_houses=pd.DataFrame({
    " House type ": [x for x in house_data.values],
              " Number ": [x for x in house_data.keys()]})
show_houses=show_houses.head(11)
show_houses

#  Graphic display of house type 
house_type=show_houses[" House type "]
house_type_num=show_houses[" Number "]
plt.barh(range(11),house_type_num)
plt.yticks(range(11),house_type)
plt.xlim(0,20)
plt.title(" Statistics on the number of rental houses in various regions of Beijing ")
plt.xlabel(" Number ")
plt.ylabel(" type ")

# Add specific numbers to each bar 
for x,y in enumerate(house_type_num):
    plt.text(y+0.5,x-0.2,"%s" %y)


plt.show()

1.3.3 Average rent analysis

df_all=pd.DataFrame({
    " Area ":file_data[" Area "].unique(),
                    " Total rent ":[0]*13,
                    " Total area ":[0]*13})
df_all

sum_price=file_data[" Price ( element / month )"].groupby(file_data[" Area "]).sum()
sum_area=file_data[" area (㎡)"].groupby(file_data[" Area "]).sum()

sum_price
sum_area

df_all[" Total rent "]=sum_price.values
df_all[" Total area "]=sum_area.values

#  Calculate the rent per square meter of each area 
df_all[" Rent per square meter （ element ）"]=round(df_all[" Total rent "]/df_all[" Total area "],2)  # 2  Express   Keep two decimal places 

df_merge=pd.merge(new_df,df_all)  # Merge two tables 

#  Graphic visualization 
num=df_merge[" Number "]
price=df_all[" Rent per square meter （ element ）"]
lx=df_merge[" Area "]
l=[i for i in range(13)]

# Create a canvas 
fig=plt.figure(figsize=(10,8),dpi=100)
# Show line chart 
ax1=fig.add_subplot(111)
ax1.plot(l,price,"or-",label=" Price ")
for i,(_x,_y) in enumerate(zip(l,price)):
    plt.text(_x+0.2,_y,price[i])
ax1.set_ylim([0,160])
ax1.set_ylabel(" Price ")
plt.legend(loc="upper right")

# Show bar chart 
ax2=ax1.twinx()
plt.bar(l,num,label=" Number ",alpha=0.2,color="green")
ax2.set_ylabel(" Number ")
plt.legend(loc="upper left")
plt.xticks(l,lx)
plt.show()

1.3.4 Area interval analysis

#  Check the maximum and minimum area of the house 
print(' The largest area of the house is %d Square meters '%(file_data[' area (㎡)'].max()))
print(' The minimum area of the house is %d Square meters '%(file_data[' area (㎡)'].min())) 
#  Check the maximum and minimum rent  
print(' The maximum rent is per month %d element '%(file_data[' Price ( element / month )'].max()))
print(' The minimum price of the house is per month %d element '%(file_data[' Price ( element / month )'].min()))

#  Area division 
area_divide=[1,30,50,70,90,120,140,160,1200]
area_cut=pd.cut(list(file_data[" area (㎡)"]),area_divide)

area_cut

area_cut_num=area_cut.describe()
area_cut_num

#  Image visualization 
area_per=(area_cut_num["freqs"].values)*100
labels = ['30 Below square meters ', '30-50 Square meters ', '50-70 Square meters ', '70-90 Square meters ', '90-120 Square meters ','120-140 Square meters ','140-160 Square meters ','160 Square meters or more ']
plt.figure(figsize=(20,8),dpi=100)
plt.axes(aspect=1)
plt.pie(x=area_per,labels=labels,autopct="%.2f %%")

plt.legend()
plt.show()

area_per