import pandas as pd
import numpy as np

#建立Series物件的方法

# #指定index，可以不按順序，不連續：pd.Series(data, index=index)

x = pd.Series([1,2,3,4], index=[3,4,5,6])
print(x)

#data可以為標量，類似廣播
x = pd.Series("Hanks" ,index = [1,2,4,5])
print(x)

#data可以是字典，index預設是排序的字典鍵,series物件僅僅保留index定義的key-value對
x = pd.Series({3:'
 
c',2:'b',1:'a'},index=[2,3])
print(x)


#建立DataFrame物件的方法
population = {'henan':1000,'shandong':200,'hubei':400}
area = {'henan':98,'shandong':900,'hubei':4000}

population = pd.Series(population)#dataframe裡的物件必須是series
province = pd.DataFrame({'population':population,'area':area})

print(province)

print(province['
 
area'])#與一般多維陣列不同，該操作返回的是一列

#二維陣列建立dataframe物件
abc = pd.DataFrame(np.random.rand(3, 2), columns=['foo', 'bar'],
                          index=['a', 'b', 'c'])

print(abc)


#index：不可變陣列 not mutable 
x = pd.Index([3,2,5,9])
x[4] = 5#該句子報錯：Index does not support mutable operations
print(x)
y = pd.Index([4,6,9,23,3])
 
print(x & y)#交集
print(x | y)#並集
print(x ^ y)#差集

#索引器：loc,iloc和ix
data = pd.Series(['a','b','c'] , index=[1,3,5])
print(data[3])#顯式索引
print(data[1:3])#隱式索引

print(data.loc[1:3])#顯式索引
print(data.iloc[1:3])#隱式索引

#dataframe的取值方法
area = pd.Series({'California': 423967, 'Texas': 695662, 'New York': 141297, 'Florida': 170312,
'Illinois': 149995})
pop = pd.Series({'California': 38332521, 'Texas': 26448193,
                              'New York': 19651127, 'Florida': 19552860,
'Illinois': 12882135})
data = pd.DataFrame({'area':area, 'pop':pop})

print(data.area,data.pop)
# #屬性獲取方法並不總是有用，當列名與方法名重合或者不全為字串時，不可使用屬性方法
print(data.area is data['area'])

data['density'] = data['pop']/data['area']
print(data.values)
print(data.T)

#iloc
print(data.iloc[:3,:2])
#loc
print(data.loc[:'Illinois',:'pop'])
#ix:混合,該功能已經被移除
print(data.ix[:3,:'pop'])


x = np.random.RandomState(43)
print(x)
#pandas計算：一元運算保留索引和列標籤；二元計算自動對其索引進行計算

area = pd.Series({'Alaska': 1723337, 'Texas': 695662, 'California': 423967}, name='area')
population = pd.Series({'California': 38332521, 'Texas': 26448193, 'New York': 19651127}, name='population')

print(area/population)#索引對齊
x = area/population

A = np.random.randint(10,size=(3,4))
print(A)
print(A - A[0])

print(x.isnull())
print(x[x.notnull()])
print(x.dropna())
print(x)


df = pd.DataFrame([[1, np.nan, 2]
                    ,[2, 3 , 5]
                    ,[np.nan , 4 ,6]])
print(df.dropna())
print(df.dropna(axis='columns'))

df[3] = np.nan
print(df)
print(df.dropna(axis='columns' , how='all'))
print(df.dropna(axis='rows' , thresh=3))

#全域性填充
print(df.fillna(9999))
#前值填充
print(df.fillna(method='ffill',axis=1))
#後值填充
print(df.fillna(method='bfill',axis=1))