构造Series
obj=Series([4,5,-7,7])
obj
Out[140]:
0 4
1 5
2 -7
3 7
dtype: int64
obj.index
Out[141]: RangeIndex(start=0, stop=4, step=1)
obj.values
Out[142]: array([ 4, 5, -7, 7], dtype=int64)
构造同时指定索引
obj2=Series([4,5,-7,7],index=['a','b','c','d'])
obj2
Out[144]:
a 4
b 5
c -7
d 7
dtype: int64
读取
obj2.a
Out[148]: 4
obj2['a']
Out[149]: 4
数学运算
obj2[obj2>0]
Out[150]:
a 4
b 5
d 7
dtype: int64
obj2*2
Out[151]:
a 8
b 10
c -14
d 14
dtype: int64
np.exp(obj2)
Out[152]:
a 54.598150
b 148.413159
c 0.000912
d 1096.633158
dtype: float64
'b' in obj2
Out[153]: True
't' in obj2
Out[154]: False
由字典变成Series
sdata={'ohio':3500,'texas':71000,'oregon':16000,'utah':500}
obj3=Series(sdata)
obj3
Out[159]:
ohio 3500
oregon 16000
texas 71000
utah 500
dtype: int64替换字典的index,与California 对应的sdata 没有，显示是NaN
states=['california','ohio','oregon','texas']
sdata={'ohio':3500,'texas':71000,'oregon':16000,'utah':500}
obj4=Series(sdata,index=states)
obj4
Out[170]:
california NaN
ohio 3500.0
oregon 16000.0
texas 71000.0
dtype: float64
判断数据是否缺失
pd.isnull(obj4)
Out[171]:
california True
ohio False
oregon False
texas False
dtype: bool
pd.notnull(obj4)
Out[172]:
california False
ohio True
oregon True
texas True
dtype: bool
判断缺失的另一种表达
obj4.notnull()
Out[176]:
california False
ohio True
oregon True
texas True
dtype: bool
obj4.isnull()
Out[177]:
california True
ohio False
oregon False
texas False
dtype: bool
obj3
Out[18]:
ohio 3500
oregon 16000
texas 71000
utah 500
dtype: int64
obj4
Out[19]:
california NaN
ohio 3500.0
oregon 16000.0
texas 71000.0
dtype: float64
obj3+obj4
Out[20]:
california NaN
ohio 7000.0
oregon 32000.0
texas 142000.0
utah NaN
dtype: float64
obj4.name='population'
obj4.index.name='state'
obj4
Out[30]:
state
california NaN
ohio 3500.0
oregon 16000.0
texas 71000.0
Name: population, dtype: float64
obj=Series([4,7,-5,3])
obj
Out[43]:
0 4
1 7
2 -5
3 3
dtype: int64
obj.index=['bob','steve','jeff','ryan']
obj
Out[45]:
bob 4
steve 7
jeff -5
ryan 3
dtype: int64
