Índice
Limpe os dados de habitação em segunda mão
Salvar em arquivo após a limpeza
1. Cena de combate real
Como usar os dados de habitação de segunda mão limpos por Pandas e armazenar arquivos
2. Pontos de conhecimento
sintaxe básica do python
Arquivo Python lido e gravado
limpeza de dados pandas
3. Combate real de novato
Arquivos antes da limpeza
ler arquivo fonte
def do_clean_data(self):
# 执行清洗
clean_data_raw_file_path = self.fileManger.get_data_file_path(self.clean_data_raw_file)
if not os.path.isfile(clean_data_raw_file_path):
# 确认文件存在
self.logger.error("需要清洗的文件不存在")
print("需要清洗的文件不存在")
return False
# 存储清洗数据数组, 先把文件头部存储
new_row_arr = []
# 从清洗文件读取需要清洗的数据
raw_df = pd.read_csv(clean_data_raw_file_path, encoding=self.encoding)
for idx in tqdm.trange(len(raw_df.values)):
row = raw_df.values[idx]
if self.check_row_is_valid(row):
# 检查 row 的合法性
new_row = self.clean_row(row)
new_row_arr.append(new_row)
# 保存文件
self.save_to_clean_file(new_row_arr)Limpe os dados de habitação em segunda mão
from base_cleaner import BaseDataCleaner
import time
import platform
class Tao365Cleaner(BaseDataCleaner):
# 逐行读取文件，进行数据清洗
clean_data_raw_file = "tao365_detail.csv" # 要执行数据清洗的文件
clean_data_result_file = 'tao365_clean.csv' # 数据清洗的结果文件
clean_data_result_file_head = ['标题', '价格', '每平方价格', '小区', '地址', '房屋户型', '建筑面积', '所在楼层', '房屋朝向', '建筑年代', '建成年份', '原始房屋户型'] # 数据清洗的结果文件的头部信息
def check_row_is_valid(self, raw_row):
# 检查当前行是否有效
area = '区' in raw_row[4]
if area == False:
return False
year = '年' in raw_row[9]
if year == False:
return False
return True
def clean_row(self, raw_row):
# 执行单行清洗
# self.logger.info("当前清洗数据: ", raw_row)
# print(raw_row)
# 标题
title = raw_row[0]
# 价格
price = self.get_price(raw_row[1])
# 每平方价格
avg_price_per_square_meter = self.get_avg_price_per_square_meter(raw_row[2])
# 小区
housing = self.get_community(raw_row[3])
# 地址
area = self.get_area(raw_row[4])
# 房屋户型
house_type = self.get_room_count(raw_row[5])
house_type_2 = raw_row[5]
# 建筑面积
acreage = self.get_area_size(raw_row[6])
# 所在楼层
level = self.get_floor(raw_row[7])
# 房屋朝向
direction = raw_row[8]
# 年代
year = self.get_year(raw_row[9])
# 建成年份
time = self.get_house_age(year)
# 数据转换
new_row = [title, price, avg_price_per_square_meter, housing, area, house_type, acreage, level, direction, year, time, house_type_2]
return new_row
def get_price(self, row):
# 售价
price = row.replace('万', '')
return int(float(price))
def get_avg_price_per_square_meter(self, row):
# 每平方米均价
avg_price_per_square_meter = row.replace('元/m²', '')
return avg_price_per_square_meter
def get_area(self, row):
# 获取属于哪个区
area = row[:row.index('区')]
return area + '区'
def get_community(self, row):
# 获取属于哪个小区
return row
def get_room_count(self, row):
# 获取房型是几室
room_count = row[:row.index('室')]
return room_count
def get_area_size(self, row):
# 获取面积
area_size = row.replace('㎡', '')
return area_size
def get_floor(self, row):
# 获取所属楼层
floor = row[row.index('/'):]
return floor.replace('/', '').replace('层', '')
def get_year(self, row):
# 获取建筑年代
return row.replace('年', '')
def get_house_age(self, row):
# 获取房屋年龄建筑年代
localtime = time.localtime(time.time())
year = int(localtime[0]) - int(row)
return year
def test(self):
# 测试
raw_row = ['娥眉新村 4层 110平米', '460.4万', '70769元/m²', '北极西村', '玄武区玄武门北极西村21号', '4室1厅2卫', '65㎡', '高楼层/3层', '南北', '2020年']
print(raw_row)
new_row = self.clean_row(raw_row)
print(new_row)
if __name__ == '__main__':
print("数据清洗开始")
raw_row = ['娥眉新村 4层 110平米', '460.4万', '70769元/m²', '北极西村', '玄武区玄武门北极西村21号', '4室1厅2卫',
'65㎡', '高楼层/3层', '南北', '2020年']
cleaner = Tao365Cleaner()
# cleaner.test()
cleaner.do_clean_data()
print("数据清洗完成")
print("python 版本", platform.python_version())
Salvar em arquivo após a limpeza
def save_to_clean_file(self, data_arr):
# 保存到清洗的文件
file_path = self.fileManger.get_data_file_path(self.clean_data_result_file)
# 初始化数据
frame = pd.DataFrame(data_arr)
frame.columns = self.clean_data_result_file_head
frame.to_csv(file_path, encoding=self.encoding, index=None)
self.logger.debug("清洗文件保存完成")resultado da operação
executar captura de tela
Limpeza de dados iniciada
100%|██████████| 9/9 [00:00<?, ?it/s]
Limpeza de dados concluída
python versão 3.9.10Processo finalizado com código de saída 0
arquivo de resultado 
link de recurso
https://download.csdn.net/download/qq_39816613/87374646
Combate real novato, continue aprendendo!