Python Rental information analysis! Find the best housing information for you!

Rental Information Analysis

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns file_data = CSV ") file_data file_data.shape file_data.head() file_data.info() file_data.describe() # Duplicated () FILe_data = FILe_data. Drop_duplicates () file_data. Shape # Duplicated () File_data = file_data.dropna() file_data.shapeCopy the code

File_data = file_data.dropna()

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, Array (I [:-2])) # Convert data_new = data_new.astype(Np.float64) file_data.loc[:, Data_new house_data = file_data[" house_size "] temp_list = [] for I in house_data: # print(I) new_info = i.place (" room ", "room ") temp_list.appEnd (new_info) file_data.loc[:," house "] = temp_listCopy the code

Analysis of the distribution of housing quantity and location

File_data [r]. "area" () new_df = to chassis pd. DataFrame ({" area ": file_data [r]." regional "unique (), Area_count = file_data.groupby(by=" region ").count() new_df[" quantity "] = area_count.values New_df. sort_values(by=" amount ", Ascending =False)Copy the code

House type quantitative analysis

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 
house_info = all_house(house_data)
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Remove values with fewer statistics

House_data = dict((key, value) for key, Value in house_info.items() if value > 50) show_houses = pd.dataframe ({" house_type ": [x for x in house_data.keys()], "数 ": [x for x in house_data.values()]})Copy the code

A graphical representation

House_type = show_houses[" housetype "] house_type_num = show_houses[" quantity "] plt.barh(range(11), house_type_num) plt.yticks(range(11), house_type) plt.xlim(0, Xlabel (" quantity ") plt.ylabel(" housing type ") # add a specific number to each bar for x, Y in enumerate(house_type_num): # print(x, y) plt.text(y+0.5, x-0.2, "%s" %y) plt.show()Copy the code

For x, y in enumerate(house_type_num): print(x, y

Average rent analysis

Df_all = pd. DataFrame ({" area ": file_data [r]." regional "unique ()," the rent amount ": [0] * 13," total ": [0] * 13}) sum_price = file_data [" price (RMB/month) "]. Groupby (file_data [" regional "]), the sum () sum_area = Sum () df_all[" total rent "] = sum_price. Values df_all[" total rent "] = sum_area.valuesCopy the code

Calculate the rent per square meter for each area

Df_all [" total rent "] = round(df_all[" total rent "] / df_all[" total area "], 2)Copy the code

merge

df_merge = pd.merge(new_df, df_all)
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Graphic visualization

Num = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge price = df_merge[" area "] l = [I for I in range(13)] FIG = Figure (figsize=(10, 8), dpi=100) ax1 = FIG. Add_subplot (111) ax1.plot(l, price, "or-", label=" price ") (_x, _y) in enumerate(zip(l, price)): PLT. Text (_x + 0.2 _y, price [I]) ax1. Set_ylim ([0, Ax2 = ax1.twinx() plt.bar(l, num, label=" number ", Alpha =0.2, color="green") ax2.set_ylabel("数 ") plt.legend(loc="upper left") plt.xticks(l, lx) plt.show()Copy the code

Create an X and y share

Add scale, string one – to – one correspondence

Basic area analysis

# to check the maximum and minimum area of the building print (' housing is the most widespread % d square meters' % (file_data [' area (㎡)] Max ())) print (' minimum area of the building is % d square meters' % (file_data [' area (㎡)]. Min ())) # See the rent to the highest level and minimum print (' rent highest price is RMB monthly % d % (file_data [' price (RMB/month)]. The Max ())) print (' housing lowest price RMB monthly % d '% (file_data [' price (RMB/month)]. The min ()))Copy the code

Area is divided

Area_divide = [1, 30, 50, 70, 90, 120, 140, 160, 1200] area_cut = pd. Cut (list(file_data[" square meters "]), area_divide) area_cut_num = area_cut.describe()Copy the code

The pie chart shows

Area_per = (area_cut_num["freqs"]. Values)*100 labels = [' less than 30 sq.m ', '30-50 sq.m ', '50-70 sq.m ', '70-90 sq.m ', Figure (figsize=(20, 8), figsize=(20, 8) dpi=100) # plt.axes(aspect=1) plt.pie(x=area_per, labels=labels, autopct="%.2f %%") plt.legend() plt.show()Copy the code

Plt.axes (aspect=1) set ellipse

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