github地址 ： github.com/yangjinghit…
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data1 = pd.read_csv('lianjia1.csv', encoding='gbk')
data2 = pd.read_csv('lianjia2.csv', encoding='gbk')
data3 = pd.read_csv('lianjia3.csv', encoding='utf-8')
data4 = pd.read_csv('lianjia4.csv', encoding='utf-8')
data5 = pd.read_csv('lianjia5.csv', encoding='utf-8')
data6 = pd.read_csv('lianjia6.csv', encoding='utf-8')
data7 = pd.read_csv('lianjia7.csv', encoding='utf-8')
data = pd.concat([data1, data2, data3, data4, data5,data6,data7])
len(data)
162227
data = data.dropna()
len(data)
144531
data.cjdanjia = np.round(data.cjdanjia.str.replace('元/平','').astype(np.float32).map(lambda x: x/10000), 2)
data.cjshijian = pd.to_datetime(data.cjshijian.map(lambda x :x[5:]))
data = data[data.cjxiaoqu.str.contains('远洋山水')]
data = data.sort_values(by='cjshijian')
data.set_index('cjshijian', inplace=True)
data = data['2012':]
#data.set_index('cjshijian',inplace=True)['2012':]
data = data[data.cjdanjia > 1]
data.head(3)
| cjtaoshu | mendian | cjzongjia | zhiwei | haoping | cjdanjia | cjxiaoqu | xingming | cjzhouqi | biaoqian | cjlouceng | congyenianxian | bankuai | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cjshijian | |||||||||||||
| 2012-01-07 | 32 | 远洋山水中街店 | 180.0 | 店经理 | 99% 122 | 2.54 | 远洋山水 1室1厅 70平 | 卢德强 | 28 | 房东信赖;销售达人;带看活跃;海外顾问 | 西/中楼层/26层 | 5年以上 | 鲁谷 |
| 2012-01-07 | 32 | 远洋山水中街店 | 180.0 | 店经理 | 99% 122 | 2.54 | 远洋山水 1室1厅 70平 | 卢德强 | 28 | 房东信赖;销售达人;带看活跃;海外顾问 | 西/中楼层/26层 | 5年以上 | 鲁谷 |
| 2012-02-13 | 42 | 远洋山水中街店 | 235.0 | 店经理 | 95% 134 | 2.22 | 远洋山水 2室2厅 105平 | 乔璐璐 | 72 | 房东信赖;销售达人 | 南/中楼层/24层 | 5年以上 | 鲁谷 |
data = data.assign(time = (data.index - pd.to_datetime('2012-01-01')).days)
data = data[['time', 'cjdanjia']]
data.head(5)
| time | cjdanjia | |
|---|---|---|
| cjshijian | ||
| 2012-01-07 | 6 | 2.54 |
| 2012-01-07 | 6 | 2.54 |
| 2012-02-13 | 43 | 2.22 |
| 2012-02-15 | 45 | 2.06 |
| 2012-02-19 | 49 | 2.23 |
plt.scatter(data.time, data.cjdanjia)
<matplotlib.collections.PathCollection at 0x119fcb7f0>
X = data.time
Y = data.cjdanjia
from sklearn.linear_model import LinearRegression
model = LinearRegression() model.fit(pd.DataFrame(X), Y)
model = LinearRegression()
model.fit(pd.DataFrame(X), Y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
x = [[0], [1800]]
y = model.predict(pd.DataFrame(x))
plt.figure(figsize = (10, 8))
plt.plot(x, y)
plt.scatter(data.time, data.cjdanjia)
<matplotlib.collections.PathCollection at 0x12068e400>
model2 = LinearRegression()
model2.fit(pd.DataFrame({'x2': X*X, 'x1':X}), Y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
x = np.linspace(0, 1800)
y = model2.predict(pd.DataFrame({'x2': x*x, 'x1':x}))
plt.figure(figsize=(10,8))
plt.plot(x,y,color='r')
plt.scatter(data.time, data.cjdanjia)
<matplotlib.collections.PathCollection at 0x10f357198>
from sklearn.preprocessing import PolynomialFeatures
q3 = PolynomialFeatures(degree=3)
X3 = q3.fit_transform(pd.DataFrame(X))
model3 = LinearRegression()
model3.fit(X3, Y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
x = np.linspace(0, 1800)
x_ = q3.fit_transform(pd.DataFrame(x))
y = model3.predict(x_)
plt.figure(figsize=(10,8))
plt.plot(x,y,color='r')
plt.scatter(data.time, data.cjdanjia)
<matplotlib.collections.PathCollection at 0x11002f860>
