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大数据统计笔记
# -*- coding: utf-8 -*-import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport jsons = pd.Series([1,3,5,np.nan,6,8]);print(s);s = pd.Series([1,3],["a","b"]);print(s);#循环查询日期范围内数据dates = pd.date_range(‘20130101‘,periods=6);print(dates);df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list(‘ABCD‘))print("LIMIT:============");print(df.head(3));print(df.tail(3));print("反转XY显示 :============");print(df.T);#排序#df.sort_index(axis=1, ascending=False)#df.sort(columns=‘B‘) ########################选择器#############################print("指定列 :============");print(df[[‘A‘,‘B‘]]);print("df[行范围,列范围]:============");print(df[0:3]);print("指定索引主键 :============");print(df[‘20130103‘:‘20130104‘]);print("布尔过滤 :============");print(df[df.A>0.5]);#print(df[0:3,0:1]);########################距阵操作 #############################print("距阵操作 :============");print(df*2);print(np.exp(df));########################TABLE 元素##############################以二维数组显示#df.values#列描述#df.columns#索引#df.index #对象类型#df.dtypesprint("字典(JSON)转换距阵 :============");sdata = {‘Ohio‘: 35000, ‘Texas‘: 71000, ‘Oregon‘: 16000, ‘Utah‘: 5000};df = pd.Series(sdata);print(df);sdata = [{‘Ohio‘: 35000, ‘Texas‘: 71000, ‘Oregon‘: 16000, ‘Utah‘: 5000}];df = pd.DataFrame(sdata);df.columns = [‘Ohio‘, ‘Texas‘, ‘Oregon‘, ‘Utah‘];print(df);df = df.fillna(0) #将缺失值都替换为0#入门#http://pandas.pydata.org/pandas-docs/stable/10min.html#http://pda.readthedocs.org/en/latest/chp5.html#blog#http://cloga.info/#wat_e_eb3d32d8-f59a-4a08-bf96-6f706d89c097_zss_#大数据#http://www.17bigdata.com/?cat=22#优化#http://1.aisensiy.sinaapp.com/2014/03/%E6%9C%80%E8%BF%91%E4%BD%BF%E7%94%A8-pandas-%E7%9A%84%E6%80%BB%E7%BB%93/df.to_csv(‘E:\\py\\foo.csv‘)######################JSON###########################print("josn====================");j = [{‘0‘:{"a":"a"},‘1‘:{"b":"b"}}];elevations = json.dumps(j);df = pd.read_json(elevations );print(df);
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