Pandas模块的数据结构主要有两：1、Series ；2、DataFrame ，下面将分别从这两方面介绍：
(1) Series结构介绍和操作示例：1.1、介绍
The Series is the primary building block of pandas and represents a one-dimensional labeled array based on the NumPy ndarray; 大概就是说Series结构是基于NumPy的ndarray结构，是一个一维的标签矩阵（感觉跟python里的字典结构有点像）
1.2、相关操作
# coding:utf-8
print "1/**************"
'''
创建
pd.Series([list]，index=[list])//以list为参数，参数为一list;index为可选参数，若不填则默认index从0开始；
若添则index长度与value长度相等
'''
import pandas as pd
s=pd.Series([1,2,3,4,5], index=['a','b','c','f','e'])
print s
'''
1/**************
a 1
b 2
c 3
f 4
e 5
dtype: int64
'''
print "2/**************"
'''
创建
pd.Series({dict})//以一字典结构为参数
'''
import pandas as pd
s=pd.Series({'a':3,'b':4,'c':5,'f':6,'e':8})
print s
'''
2/**************
a 3
b 4
c 5
e 8
f 6
dtype: int64
'''
print "3/**************"
'''
取值
s[index] or s[[index的list]]
取值操作类似数组，当取不连续的多个值时可以以一list为参数
'''
import pandas as pd
import numpy as np
v=np.random.random_sample(50)
s=pd.Series(v)
s1=s[[3,7,33]]
s2=s[1:5]
s3=s[49]
print "s1\n",s1
print "s2\n",s2
print "s3\n",s3
'''
3/**************
s1
3 0.751252
7 0.608317
33 0.612134
dtype: float64
s2
1 0.079671
2 0.192029
3 0.751252
4 0.280966
dtype: float64
s3
0.273614509027
'''
print "4/**************"
'''
head(n);.tail(n)//取出头n行或尾n行，n为可选参数，若不填默认5
'''
import pandas as pd
import numpy as np
v=np.random.random_sample(50)
s=pd.Series(v)
print "s.head()\n",s.head()
print "s.head(1)\n",s.head(1)
print "s.tail(3)\n",s.tail(3)
'''
4/**************
s.head()
0 0.585155
1 0.938871
2 0.107134
3 0.479727
4 0.641377
dtype: float64
s.head(1)
0 0.585155
dtype: float64
s.tail(3)
47 0.527761
48 0.649811
49 0.499103
dtype: float64
'''
print "5/**************"
'''
.index; .values//取出index 与values ,返回list
Size、shape、uniqueness、counts of values
'''
import pandas as pd
import numpy as np
v=[10,3,2,2,np.nan]
v=pd.Series(v);
print "v.index",v.index
print "v.values",v.values
print "len():",len(v) # Series长度,包括NaN
print "shape():",np.shape(v) # 矩阵形状，（，）
print "count():",v.count() # Series长度，不包括NaN
print "unique():",v.unique() # 出现不重复values值
print "value_counts():\n",v.value_counts() # 统计value值出现次数
'''
5/**************
v.index RangeIndex(start=0, stop=5, step=1)
v.values [ 10. 3. 2. 2. nan]
len(): 5
shape(): (5,)
count(): 4
unique(): [ 10. 3. 2. nan]
value_counts():
2.0 2
3.0 1
10.0 1
dtype: int64
'''
print "6/**************"
'''
加运算
相同index的value相加，若index并非共有的则该index对应value变为NaN
'''
import pandas as pd
s1=pd.Series([1,2,3,4],index=[1,2,3,4])
s2=pd.Series([1,1,1,1])
s3=s1+s2
print s3
'''
6/**************
0 NaN
1 2.0
2 3.0
3 4.0
4 NaN
dtype: float64
'''
2.1、介绍
DataFrame unifies two or more Series into a single data structure.Each Series then represents a named column of the DataFrame, and instead of each column having its own index, the DataFrame provides a single index and the data in all columns is aligned to the master index of the DataFrame. 这段话的意思是，DataFrame提供的是一个类似表的结构，由多个Series组成，而Series在DataFrame中叫columns。
2.2、 相关操作
# coding:utf-8
'''
create
pd.DataFrame()
参数：
1、二维array；
2、Series 列表；
3、value为Series的字典
注：若创建使用的参数中，array、Series长度不一样时，对应index的value值若不存在则为NaN
'''
print "1/**************"
import pandas as pd
import numpy as np
s1=np.array([1,2,4])
s2=np.array([5,6,7,8])
print s1
print type(s1)
# 1、二维array；
df=pd.DataFrame([s1,s2])
print df
'''
1/**************
[1 2 4]
<type 'numpy.ndarray'>
0 1 2 3
0 1 2 4 NaN
1 5 6 7 8.0
'''
print "2/**************"
import pandas as pd
import numpy as np
s1=pd.Series(np.array([1,2,3,4]))
s2=pd.Series(np.array([5,6,7,8]))
print s1
print type(s1)
# 2、Series 列表；
df=pd.DataFrame([s1,s2])
print df
print df.shape
'''
2/**************
0 1
1 2
2 3
3 4
dtype: int64
<class 'pandas.core.series.Series'>
0 1 2 3
0 1 2 3 4
1 5 6 7 8
(2, 4)
'''
print "3/**************"
import pandas as pd
import numpy as np
s1=pd.Series(np.array([1,2,3,4]))
s2=pd.Series(np.array([5,6,7,8]))
# 3、value为Series的字典
df=pd.DataFrame({"a":s1,"b":s2});
print df
print df.shape
print df.head(2)
print df.tail(2)
print df.items
print df.keys()
print df.values
print "df['a']\n", df['a']
'''
3/**************
a b
0 1 5
1 2 6
2 3 7
3 4 8
(4, 2)
a b
0 1 5
1 2 6
a b
2 3 7
3 4 8
<bound method DataFrame.iteritems of a b
0 1 5
1 2 6
2 3 7
3 4 8>
Index([u'a', u'b'], dtype='object')
[[1 5]
[2 6]
[3 7]
[4 8]]
df['a']
0 1
1 2
2 3
3 4
'''
'''
b.属性
b.1 .columns :每个columns对应的keys
b.2 .shape:形状，（a，b）,index长度为a,columns数为b
b.3 .index;.values:返回index列表；返回value二维array
b.4 .head();.tail();
'''
print "4/**************"
'''
c.if-then 操作
c.1使用.ix[]
df.ix[条件，then操作区域]
'''
df=pd.DataFrame({"A":[1,2,3,4],"B":[5,6,7,8],"C":[1,1,1,1]})
print df
df.ix[df.A > 1,'B']= -1
print df
'''
4/**************
A B C
0 1 5 1
1 2 6 1
2 3 7 1
3 4 8 1
A B C
0 1 5 1
1 2 -1 1
2 3 -1 1
3 4 -1 1
'''
print "5/**************"
'''
c.2使用numpy.where
np.where(条件，then，else)
'''
df=pd.DataFrame({"A":[1,2,3,4],"B":[5,6,7,8],"C":[1,1,1,1]})
print df
print df.keys()
df["then"]=np.where(df.A<3, 1, 0)
print df
print df.keys()
'''
5/**************
A B C
0 1 5 1
1 2 6 1
2 3 7 1
3 4 8 1
Index([u'A', u'B', u'C'], dtype='object')
A B C then
0 1 5 1 1
1 2 6 1 1
2 3 7 1 0
3 4 8 1 0
Index([u'A', u'B', u'C', u'then'], dtype='object')
'''
'''
d.根据条件选择取DataFrame
'''
print "6/**************"
#d.1 直接取值df.[]
df=pd.DataFrame({"A":[1,2,3,4],"B":[5,6,7,8],"C":[1,1,1,1]})
df=df[df.A>=3]
print df
'''
6/**************
A B C
2 3 7 1
3 4 8 1
'''
print "7/**************"
# d.2 使用.loc[]
df=pd.DataFrame({"A":[1,2,3,4],"B":[5,6,7,8],"C":[1,1,1,1]})
df=df.loc[df.A>2]
print df
'''
7/**************
A B C
2 3 7 1
3 4 8 1
'''
'''
e.Grouping
'''
print "8/**************"
# e.1 groupby 形成group
df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),
'size': list('SSMMMLL'),
'weight': [8, 10, 11, 1, 20, 12, 12],
'adult' : [False] * 5 + [True] * 2});
print df
# 列出动物中weight最大的对应size
group=df.groupby("animal").apply(lambda subf: subf['size'][subf['weight'].idxmax()])
print group
'''
8/**************
adult animal size weight
0 False cat S 8
1 False dog S 10
2 False cat M 11
3 False fish M 1
4 False dog M 20
5 True cat L 12
6 True cat L 12
animal
cat L
dog M
fish M
dtype: object
'''
print "9/**************"
# e.2 使用get_group 取出其中一分组
df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),
'size': list('SSMMMLL'),
'weight': [8, 10, 11, 1, 20, 12, 12],
'adult' : [False] * 5 + [True] * 2});
group=df.groupby("animal")
cat=group.get_group("cat")
print cat
'''
9/**************
adult animal size weight
0 False cat S 8
2 False cat M 11
5 True cat L 12
6 True cat L 12
'''
其他具体操作请参考CookBook
http://pandas.pydata.org/pandas-docs/stable/cookbook.html
参考:# https://blog.csdn.net/u014607457/article/details/51290582