pandas笔记

#!/usr/bin/env python# -*- coding: UTF-8 -*-"""@author: XiangguoSun@contact: [e-mail]@file: learn_pandas.py@time: 2017/3/8 8:18@software: PyCharm"""import numpy as npfrom pandas import Series, DataFrameimport pandas as pd'''1,基本数据结构''''''1.1 Series: dic+array'''obj_dic={'a':1,'b':2,'c':3}objd=Series(obj_dic)obj = Series([4,7,-5,3],index=['a','b','c','d'])print obj.index,obj.valuesprint obj[['a','c']]print 'b' in objobj_na = Series(obj,index=['a','b','c','d','add'])print obj_naprint obj_na.isnull()  # also pd.isnull(obj_na)print obj_na.notnull()  # also pd.notnull(obj_na)print obj_na.nameprint obj_na.index.nameobj_na.index=['x','y','z','o','p']obj_na.name='my_table'obj_na.index.name='my_index'print obj_na'''1.2 DataFrame'''data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],        'year': [2000, 2001, 2002, 2001, 2002],        'pop': [1.5, 1.7, 3.6, 2.4, 2.9]        }df = DataFrame(data, columns=['year', 'state', 'pop', 'debt'], index=['one', 'two', 'three', 'four', 'five'])print dfprint df.ix['three']df['five']=np.arange(5)print df#增加和删除列df['new_column']=df.state == 'Ohio'print dfdel df['new_column']print df.columns#嵌套字典pop = {'Nevada':{2001:2.4,2002:2.9},       'Ohio':{2000:1.5,2001:1.7,2002:3.6}       }data = DataFrame(pop)print dataprint data.Tprint DataFrame(pop,index=[2001,2002,2003])data.index.name='sunxiangguo'data.columns.name = 'state'print dataprint data.values#返回二维ndarray#索引对象index：不常用，略'''index对象不可修改''''''2,基本功能'''#重新索引obj = Series([4.5,7,-2,4],index=['b','a','c','d'])print objobj2 = obj.reindex(['a','b','c','d','e'])print obj2obj3 = obj.reindex(['a','b','c','d','e'], fill_value=0)print obj3#插值处理obj = Series(['blue','perple','yellow'],index=[0,2,4])print objobj2 = obj.reindex(range(8),method='ffill')  # 前向插值print obj2print obj.reindex(range(7),method='pad') # equal to ffillprint obj.reindex(range(7),method='bfill') # 后向插值print obj.reindex(range(7),method='backfill') # equal to bfill

# 丢弃指定轴上的项obj = Series(np.arange(5), index=['a', 'b', 'c', 'd', 'e'])new_obj = obj.drop('c')print objprint new_objprint obj.drop(['c', 'd'])data = DataFrame(np.arange(16).reshape((4, 4)),                 index=['Ohio', 'Colorado', 'Utah', 'New York'],                 columns=['one', 'two', 'three', 'four']                 )print dataprint data.drop(['Colorado', 'Ohio'])print data.drop('two', axis=1)print data.drop(['two', 'four'], axis=1)# 索引、选取和过滤data = Series(np.arange(4), index=['a', 'b', 'c', 'd'])print dataprint data['b']print data[1]print data[2:4]print data[['b','a','d']]print data[[1,3]]print data[data<2]print data['a':'c']data = DataFrame(np.arange(16).reshape((4, 4)),                 index=['Ohio', 'Colorado', 'Utah', 'New York'],                 columns=['one', 'two', 'three', 'four'])print dataprint data['two']  # 选取列print data[['three', 'one']]  # 选取列print data[:2]  # 注意[]里面是切片或者布尔型数组时，选取的不再是列，而是行print data[data['three'] > 5]  # 注意[]里面是切片或者布尔型数组时，选取的不再是列，而是行print data.ix[:2, :2]  # 同时选取行和列print data.ix[1:3]  # 选取一组行print data.xs('Ohio')  # 根据标签选取若干行print data.xs(range(1, 4), axis=1)  # 根据标签选取若干列'''print data.icol(2)print data.irow(0)这两个已经被下面两行代码取代'''print data.iloc[:, 2]print data.iloc[0]