1.数据读取
import pandas as pd
import numpy as np
import pymongo
data = pd.DataFrame(pd.read_excel('000.xlsx', index=False))
client = pymongo.MongoClient("mongodb://XX:XXXXX@192.168.3.7:2018",connect=False)
db = client["test"]
table = db["python"]
df = pd.DataFrame(list(table.find()))
可以从excel,csv,mongo数据之类的读取数据
2.遍历
for i in range(data.index.max()):
if any([
'missing' in data.loc[i,:].values,
data.loc[i,'hour'] not in range(25),
]):
print('已删除存在异常值 %s 行数据'%i)
data.drop([i],inplace=True)
for i in range(0,len(df)):
info = df.loc[i].to_dict()
3. 去空(NA)
3.1直接去除
from numpy import nan as NA
data=Series([1,NA,3.5,NA,7])
print(data.dropna())
#至少2个NA才删除
print(data.dropna(thresh=2))
3.2 用中位数或者平均数进行填充
df = df.fillna(df.median())
print(df.fillna(df.mean()))
4.对字段进行处理
def get_salary(salary):
s = 0
if "-" in salary:
for part in salary.split("-"):
if "万" in part:
q = float(part[:-1]) * 10000
else:
q = float(part[:-1]) * 1000
s += q
return int(s/2.0)
else:
return np.nan
df["salary"] = df["salary"].apply(get_salary)
df.head()
df["company"]=df["company"].apply(lambda x :x.split("/")[0].strip('"'))
5.删除重复
df["company"].drop_duplicates()
6.只留部分
df.loc[:,["address","company"]]
df_c = df_c.iloc[:,[4,5]]
del data["name_grade"]
del data["info_grade"]
7. 排序
df.sort_values(by='col1', ascending=False)
8. isin
mask = df['A'].isin([1]) #括号中必须为list
9. merge
df1 = pd.DataFrame({'name':['kate', 'herz', 'catherine', 'sally'], 'age':[25, 28, 39, 35]})
df2 = pd.DataFrame({'name_t':['kate', 'herz', 'catherine', 'sally'], 'score':[70, 60, 90, 100]})
print(pd.merge(df1, df2, left_on="name", right_on="name_t").drop('name_t', axis=1))
10.保存为csv,或者到mongo
df["company"].drop_duplicates().to_csv("company.csv",encoding="utf-8")
db[MONGO_TABLE].insert(row.to_dict())