pandas中有关series操作

import pandas as pd

#使用默认index
t = pd.Series([1,4,5,6,7])
print(t)
print(type(t))


#自己设置index
t2 = pd.Series([1,4,5,6,7],index=list("abcde"))
print(t2)
print(type(t2))

#修改t2的值的类型为float
print("*"*50)
tf = t2.astype(float) #必须要重新赋给另一个变量，原来的t2是不会变的
print(tf)
print(type(tf))




#index超出值的长度会报错
#t3 = pd.Series([1,4,5,6,7],index=list("abcdefg"))
#print(t3)
#print(type(t3))


#通过字典来设置Series的值和index
temp_dict = {"a1":12,"a2":3,"a3":5}
t4 = pd.Series(temp_dict)
print(t4)
print(t4.dtype)


#取series里的值
print(t4["a1"])#通过索引来取
print(t4[1]) #通过位置来取
print(t4[:2]) #取连续的前两行
print(t4[[0,2]]) #取不连续的
print(t4[["a1","a2"]])
#print(t4[["a12","a22"]]) #没有时，会报错
print(t4[t4>6]) # 布尔索引，选中t4中值大于6的


#取出索引
print("*"*50)
print(t4.index)
print(type(t4.index))#<class 'pandas.core.indexes.base.Index'>可以迭代的
print(len(t4))
print(list(t4.index))#还可以使用list这种强制类型转换
print(list(t4.index)[:2]) #取前2个


#取出值
print("*"*50)
print(t4.values)
print(type(t4.values))

结果

C:\Users\Alienware\machineLearnings\Scripts\python.exe C:/Users/Alienware/PycharmProjects11/machineLearnings/pandas练习/pandasPractice.py
0    1
1    4
2    5
3    6
4    7
dtype: int64
<class 'pandas.core.series.Series'>
a    1
b    4
c    5
d    6
e    7
dtype: int64
<class 'pandas.core.series.Series'>
**************************************************
a    1.0
b    4.0
c    5.0
d    6.0
e    7.0
dtype: float64
<class 'pandas.core.series.Series'>
a1    12
a2     3
a3     5
dtype: int64
int64
12
3
a1    12
a2     3
dtype: int64
a1    12
a3     5
dtype: int64
a1    12
a2     3
dtype: int64
a1    12
dtype: int64
**************************************************
Index(['a1', 'a2', 'a3'], dtype='object')
<class 'pandas.core.indexes.base.Index'>
3
['a1', 'a2', 'a3']
['a1', 'a2']
**************************************************
[12  3  5]
<class 'numpy.ndarray'>

Process finished with exit code 0

pandas中有关series操作

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