列举出文件夹下需要的所有xx格式文档
import os
# 第一种情况：当前文件夹下存在，不遍历子文件夹
docLabels = [f for f in os.listdir(filepath) if f.endswith('.xml')]
#第二种情况：遍历当前文件夹及其所有子文件夹
def print_dir(path_list, filepath, pattern):
for i in os.listdir(filepath):
path = os.path.join(filepath, i)
if os.path.isdir(path):
print_dir(path_list, path, pattern)
if path.endswith(pattern):
path_list.append(path)
return path_list
path_list = []
path_list = print_dir(path_list, filepath, ".xxx")
for path in tqdm(path_list, ncols=70):
xxx
pandas追加写csv
data = {'vid':data['vid'], 'text':data['token'], 'final_label':json.dumps(final_label)}
df = pd.DataFrame(data, index = [0]) # data是json数据
if os.path.exists(filepath):
df.to_csv(filepath, header=0, mode='a', index=False, sep=',', encoding='utf-8-sig')
else:
df.to_csv(filepath, mode='a', index=False, sep=',', encoding='utf-8-sig')
dataframe追加写列
data.loc[index, 'celebrity'] = str(name_list)
读tsv文件
data = pd.read_csv(path, sep='\t', dtype=object, header=None, error_bad_lines=False)
data.columns = ['gid', 'uid', 'uri', 'label', 'inference', 'result', 'msg']
json转tsv
with open('test.json', 'r') as f:
json_data = f.readlines()
for data in tqdm(json_data, ncols=70):
data = json.loads(json.loads(data))
data['label'] = label_dic[data['poi_id']]
del data['poi_id']
head = ['uri','ai_ocr_sentence','backend_type_name','comment_cnt_all']
# 第一次打开文件时，第一行写入表头
path = 'train.tsv'
if not os.path.exists(path):
with open(path, "w", newline='', encoding='utf-8') as csvfile: # newline='' 去除空白行
writer = csv.DictWriter(csvfile, fieldnames=head, delimiter='\t') # 写字典的方法
writer.writeheader() # 写表头的方法
# 接下来追加写入内容
with open(path, "a", newline='', encoding='utf-8') as csvfile: # newline='' 一定要写，否则写入数据有空白行
writer = csv.DictWriter(csvfile, fieldnames=head, delimiter='\t')
writer.writerow(data) # 按行写入数据
tsv转json
lines = pd.read_csv(path, sep='\t', dtype=object, header=None, error_bad_lines=False)
lines.columns = ['vid', 'cid', 'album', 'cid_title', 'score', 'item_id', 'item_title', 'vv_all']
lines = lines.drop_duplicates()
for i in tqdm(range(len(lines)), ncols=70):
line = lines.iloc[i].to_json()
line = json.loads(line)
pickle读写
with open('vid_dic.pkl','wb') as in_data:
pickle.dump(vid_dic, in_data, pickle.HIGHEST_PROTOCOL)
with open('vid_dic.pkl','rb') as out_data:
vid_dic = pickle.load(out_data)
参数
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--hdfs_path", type=str, default='hdfs://haruna/home/byte_arnold_hl_vc/user/name.1997/')
parser.add_argument("--save_path", type=str, default = '')
args = parser.parse_args()
os.system('hdfs dfs -get {}'.format(args.hdfs_path))
os.system('hdfs dfs -put {} {}'.format(item, args.save_path))
字典（dict）按键（key）和值（value）排序
- 字典按键排序：
>>> dic = {'a':2, 'b':1}
>>> d = sorted(dic.items(), key = lambda k: k[0])
>>> print(d)
[('a', 2), ('b', 1)]
- 字典按值排序：
>>> dic = {'a':2, 'b':1}
>>> d = sorted(dic.items(), key = lambda k: k[1])
>>> print(d)
[('b', 1), ('a', 2)]
打印进度条
方法一：print()函数实现
print("\r", "---- 处理到第" + str(j) + "个", end="", flush=True)
方法二：tqdm库
for patent in tqdm(patent_list, ncols=10):
pass
数据分布统计
def status(x) :
return pd.Series([x.min(),x.quantile(.25),x.median(),x.quantile(.75),x.mean(),x.max(),x.var(),x.std(),x.skew(),x.kurt()],
index=['最小值','25%分位数','中位数','75%分位数','均值','最大值','方差','标准差','偏度','峰度'])
字符串与日期转换
- 将列表中的日期转为字符串
# 输出时间戳对应的年月日信息
test['日期'].apply(lambda x: print(x.year, x.month, x.day))
# 将时间戳转换为字符串
myString = test['日期'].apply(lambda x: x.strftime('%Y-%m-%d'))
- 字符串转时间（年月日）
published_time = "20" + pub_time[0]
published_time = datetime.datetime.strptime(published_time,'%Y%m%d').date()
写json
with open('train_data.json', 'a') as f:
json.dump(info_dic, f)
f.write('\n')
# f.write(json.dumps(info_dic, ensure_ascii=False)+'\n')
统计出现次数并存入字典
date_dic = {}
if not date in date_dic:
date_dic[date]=1
else:
date_dic[date]=date_dic[date]+1
使用RandomForestClassifier查看不同特征的重要程度
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=200, criterion='entropy', max_depth=4)
rf_clf = clf.fit(x, y)
rf_clf.feature_importances_
读excel
import xlrd
# 读取训练集
def read_excel(filename):
data = xlrd.open_workbook(filename)
table = data.sheet_by_name(u'Sheet1')
abstract_list = table.col_values(0) #第一列内容
title_list = table.row_values(0) #第一行内容
train = [abstract_list, title_list]
return train
写excel
import xlrd
from xlrd import open_workbook
import xlwt
from xlutils.copy import copy
import os
# 写到excel中
def write_excel_xls_append(path, value):
index = len(value) # 获取需要写入数据的行数
workbook = open_workbook(path) # 打开工作簿
sheet = workbook.sheet_by_index(0)
rows_old = sheet.nrows
new_workbook = copy(workbook) # 将xlrd对象拷贝转化为xlwt对象
new_worksheet = new_workbook.get_sheet(0) # 获取转化后工作簿中的第一个表格
for i in range(0, index):
for j in range(0, len(value[i])):
new_worksheet.write(i + rows_old, j,value[i][j]) # 追加写入数据，注意是从i+rows_old行开始写入
new_workbook.save(path) # 保存工作簿
if __name__ == "__main__":
filename = " "
content_list = []
if os.path.exists(filename):
write_excel_xls_append(filename, content_list)
else:
excel = xlwt.Workbook()
sheet = excel.add_sheet("Sheet1")
head = ["abstract", "claims_n", "claim", "result"]
for index, value in enumerate(head):
sheet.write(0, index, value)
for index, value_list in enumerate(content_list, 1):
for i, value in enumerate(value_list):
sheet.write(index, i, value)
excel.save(filename)
读数据库
import pymysql
def read_content(path):
con = pymysql.connect(host="",user="",password="",port=3306,charset="utf8",db="")
cursor = con.cursor()
sql = "SELECT result FROM table_name WHERE id= '' limit 1;"
try:
# 执行sql语句
cursor.execute(sql)
# 获取记录列表
result = cursor.fetchone()
except:
# 如果发生错误则回滚
con.rollback()
cursor.close()
# 关闭数据库连接
con.close()
写数据库
import pymysql
# 保存到数据库
def save_data_to_mysql(val):
con = pymysql.connect(host="",user="",password="",port=3306,charset="utf8",db="")
cursor = con.cursor()
sql="insert into match_result(application_id,filed_time,published_time,location,result)\
VALUES('"+val[0]+"','"+val[1]+"','"+val[2]+"','"+val[3]+"','"+val[4]+"')"
# print(sql)
try:
# 执行sql语句
cursor.execute(sql)
# 提交到数据库执行
con.commit()
except:
# 如果发生错误则回滚
con.rollback()
cursor.close()
# 关闭数据库连接
con.close()
自动解压当前文件夹下所有zip包
import zipfile
import os
def unzip(path, zfile):
file_path = path + os.sep + zfile
desdir = path + os.sep + zfile[:zfile.index('.zip')]
srcfile = zipfile.ZipFile(file_path)
for filename in srcfile.namelist():
srcfile.extract(filename, desdir)
if filename.endswith('.zip'):
# if zipfile.is_zipfile(filename):
path = desdir
zfile = filename
unzip(path, zfile)
# 定位到每个zip文件
def print_dir(filepath):
for i in os.listdir(filepath):
path = os.path.join(filepath, i)
if os.path.isdir(path):
print_dir(path)
if path.endswith(".zip"):
unzip(file_path, path.split('/')[-1])
file_path = "D:/Pythonworkspace/patent/data/Application/2010/"
print_dir(file_path)
读取xml文本
import xml.dom.minidom
#打开xml文档
dom = xml.dom.minidom.parse('C:/Users/asus/Desktop/1.xml')
#得到文档元素对象
root = dom.documentElement
urls = dom.getElementsByTagName('url')
copus = ""
for url in urls:
copus = copus + url.firstChild.data + ";"
# copus.append(url.firstChild.data)
text = "https://www.drugs.com/sfx/nytol-quickcaps-side-effects.html"
if copus.find(text) == 0:
print("已经存在")
python list 和dict的查找效率比较
import time
query_lst = [-60000,-6000,-600,-60,-6,0,6,60,600,6000,60000]
lst = []
dic = {}
for i in range(100000000):
lst.append(i)
dic[i] = 1
start = time.time()
for v in query_lst:
if v in lst:
continue
end1 = time.time()
for v in query_lst:
if v in dic:
continue
end2 = time.time()
print "list search time : %f"%(end1-start)
print "dict search time : %f"%(end2-end1)
运行结果：
list search time : 11.836798
dict search time : 0.000007
list的查找效率远远低于dict的效率，原因在于：python中list对象的存储结构采用的是线性表，因此其查询复杂度为O(n)，而dict对象的存储结构采用的是散列表(hash表)，其在最优情况下查询复杂度为O(1)。
效率：set>dic>list
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