一.pandas数据结构

1.Series

Series是一种一维的数组型对象，它包含了一个值序列，并且包含了数据标签，称为索引

该结构能够存储各种数据类型，比如字符数，整数，浮点数，python对象等

Series用name和index属性来描述数据值

Series是一维数据结构，维数不可改变

obj = pd.Series([1,2,3,4])
obj.values
obj2 = pd.Series([5,6,7,8],index=['a','b','c','d'])
obj2[obj2 > 0]
obj2 * 2
'b' in obj2

 创建一个空Series对象

s = pd.Series()

ndarray创建Series对象

data = np.array(['a','b','c','d'])
s = pd.Series(data,index=[1,2,3,4])

 dict创建Series对象

data = {'a':0,'b':1,'c':2}
s = pd.Series(data)

标量创建Series对象，必须提供索引 

s = pd.Series(5,index = [0,1,2,3])

2.DataFrame

是一种二维表格型数据的结构，既有行索引，又有列索引，行索引是index，列索引是columns。在创建该结构时，可以指定相应的索引值

data = {'state':['ohio','ohio','ohio','nada','nada','nada'],'year':[2000,2001,2002,2001,2002,2003],'pop':[1.1,1.2,1.3,2.1,2.2,2.3]
frame = pd.DataFrame(data)
frame.head()
pd.DataFrame(data,columns=['year','state','pop'])
frame['state']
frame.year

创建空的DataFrame对象

df = pd.DataFrame()

 列表创建DataFrame对象

data = [1,2,3,4]
df = pd.DataFrame(data)data = [['alex',10],['bob',12],['clark',14]]
df = pd.DataFrame(data,columns=['Name','Age'])

 字典嵌套列表创建DataFrame对象

data = {'Name':['tom','jack','steve','ricky'],'Age':[23,24,25,26]}
df = pd.DataFrame(data)

 列表嵌套字典创建DataFrame对象

data = [{'a':1,'b':2},{'a':5,'b':10,'c':20}]
df = pd.DataFrame(data)

Series创建DataFrame对象

d = {'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['a','b','c','d'])}
df = pd.DataFrame(d)

二.基本功能

1.重建索引

obj = pd.Series([1,2,3,4],index=['a','b','c','d'])
obj2 = obj.reindex(['a','b','c','d','e'])

2.轴向上删除条目

obj = pd.Series(np.arange(5),index=['a','b','c','d','e'])
obj2 = obj.drop('c')
obj.drop(['d','c'])

3.索引，选择与过滤

obj = pd.Series(np.arange(4),index=['a','b','c','d'])
obj['b']
obj[2:4]
obj[['b','c','d']]
obj[obj<2]
obj['b':'c'] = 5data = pd.DataFrame(np.arange(16).reshape((4,4)),index=['ohio','colo','utah','new'],columns=['one','two','three','four'])
data['two']
data[['three','one']]
data[:2]
data[data['three']>5]
data < 5
data[data < 5] = 0
data.loc['colo',['two','three']]
data.iloc[2,[3,0,1]]
data.iloc[2]
data.iloc[[1,2],[3,0,1]]
data.loc[:'utah','two']
data.iloc[:,:3][data.three>5]

三.描述性统计的概述与计算

df = pd.DataFrame([1,np.nan],[3,-1][np.nan,np.nan],[0,3]],index=['a','b','c','d'],columns=['one','two'])
df.sum()
df.sum(axis='columns')
df.mean()
df.describe()

1.相关性和协方差

returns = price.pct_change()
returns.tail()
returns.corr()
returns.cov()

2.唯一值，计数和成员属性

obj = pd.Series(['c','a','d','a','a','b','b','c','c'])
uniques = obj.unique()
obj.value_counts()
mask = obj.isin(['b','c'])
obj[mask]