# 常用方法 # 获取某处索引值:s[index](假如index不存在会报KeyError) s.get(index) s.get(index,default) # s.values 返回list结构的所有值 # 过滤查询 判断是否存在index index in s 查询符合某个条件的集合返回Series s[s > 2] # 直接参与计算 s[ s+1 ] 每个value都进行+1操作 # ============================== # 这两个是常用的单独导出 from pandas import Series from utils import util dateList = [1, 3, 5, 6, 8] db = {"No.1": "Wo", "No.2": "Shi", "No.3": "Ni", "No.4": "Da", "No.5": "Ye"} # Creating a Series by passing a list of values, letting pandas create a default integer index s = Series(dateList, index=["A", "B", "C", "D", "E"]) # 索引的长度必须和list的长度一样,否则为[0, ..., len(data) - 1] util.report_tag("Series 处理list结构数据") print "Series data structures is \n", s print "index is ", s.index print "values is ", s.values print "the fist element is ", s[0] print "0~3 element is \n", s[:3] print ">3 element is \n", s[s > 3] # print "通过 s[6] 会报错", s[6] print "通过 s.get(6) return None", s.get(6) print "查看前2行\n", s.head(2) print "查看最后2行\n", s.tail(2) util.report_tag("Series 处理dict数据") s = Series(db) print "Series data structures is \n", s # Series更像是一个dict ,可以直接通过 s[index] 取值 ,判断是否存在index,假如通过s[index]取一个不存在的索引,将会报 KeyError,而用
import MySQLdb from utils import util conn = MySQLdb.Connect( host='db.***.cn', port=3306, user='******', passwd='write', db='db_crm', ) df = pd.read_sql("select phone , order_count from t_user_basic_info limit 1", conn) # df = pd.read_sql("select * from t_user_basic_info limit 1", conn, index_col="phone") print df util.report_tag("保存数据到文件:支持csv,xls,xlsx,hdf,html,txt") df.to_csv("aa.csv") print("已保存到aa.csv") # 需要下载xlwt 模块 df.to_excel("bb.xls", sheet_name='Sheet1') print("已保存到bb.xls") # 需要下载openpyxl 摸块 # df.to_excel("bb.xlsx", index_label='label', merge_cells=False) util.report_tag("保存数据到json串中") json = df.to_json() print(json) util.report_tag("保存数据到数据库") # 保存数据到表里,aa是表名 ,需要安装 sqlalchemy 模块 from sqlalchemy import create_engine
# s.values 返回list结构的所有值 # 过滤查询 判断是否存在index index in s 查询符合某个条件的集合返回Series s[s > 2] # 直接参与计算 s[ s+1 ] 每个value都进行+1操作 # ============================== # 这两个是常用的单独导出 from pandas import Series from utils import util dateList = [1, 3, 5, 6, 8] db = {"No.1": "Wo", "No.2": "Shi", "No.3": "Ni", "No.4": "Da", "No.5": "Ye"} # Creating a Series by passing a list of values, letting pandas create a default integer index s = Series(dateList, index=["A", "B", "C", "D", "E"]) # 索引的长度必须和list的长度一样,否则为[0, ..., len(data) - 1] util.report_tag("Series 处理list结构数据") print "Series data structures is \n", s print "index is ", s.index print "values is ", s.values print "the fist element is ", s[0] print "0~3 element is \n", s[:3] print ">3 element is \n", s[s > 3] # print "通过 s[6] 会报错", s[6] print "通过 s.get(6) return None", s.get(6) print "查看前2行\n", s.head(2) print "查看最后2行\n", s.tail(2) util.report_tag("Series 处理dict数据") s = Series(db) print "Series data structures is \n", s # Series更像是一个dict ,可以直接通过 s[index] 取值 ,判断是否存在index,假如通过s[index]取一个不存在的索引,将会报 KeyError,而用
s = Series(dateList, index=["A", "B", "C", "D", "E"]) # 索引的长度必须和list的长度一样,否则为[0, ..., len(data) - 1] print "Series data structures is \n", s print "index is ", s.index print "values is ", s.values print "the fist element is ", s[0] print "0~3 element is \n", s[:3] print ">3 element is \n", s[s > 3] s = Series(db) print "No.1 is ", s['No.1'] print "Wo is in db", 'No.1' in s # Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns: dates = pd.date_range('20130101', periods=6) df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print dates print df print df.T s = Series(db) util.report_tag("dict to Series:") print(s) util.report_tag("dict to DataFrame") df = DataFrame(db, index=[1, 2, 3, 4, 5]) print df
db = {"No.1": "Wo", "No.2": "Shi", "No.3": "Ni", "No.4": "Da", "No.5": "Ye"} # Creating a Series by passing a list of values, letting pandas create a default integer index s = Series(dateList, index=["A", "B", "C", "D", "E"]) # 索引的长度必须和list的长度一样,否则为[0, ..., len(data) - 1] print "Series data structures is \n", s print "index is ", s.index print "values is ", s.values print "the fist element is ", s[0] print "0~3 element is \n", s[:3] print ">3 element is \n", s[s > 3] s = Series(db) print "No.1 is ", s['No.1'] print "Wo is in db", 'No.1' in s # Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns: dates = pd.date_range('20130101', periods=6) df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print dates print df print df.T s = Series(db) util.report_tag("dict to Series:") print(s) util.report_tag("dict to DataFrame") df = DataFrame(db, index=[1, 2, 3, 4, 5]) print df