创建方法
import pandas as pd
import numpy as np
s = pd.Series([1,2,7,4,5])
print(s)
0 1
1 2
2 7
3 4
4 5
dtype: int64
# 自定义索引
s = pd.Series([1,2,7,4,5],index=['A','B','C','D','E'])
print(s)
A 1
B 2
C 7
D 4
E 5
dtype: int64
# 定义name
s = pd.Series([1,2,7,4,5],index=['A','B','C','D','E'],name='月')
print(s)
A 1
B 2
C 7
D 4
E 5
Name: 月, dtype: int64
# 通过字典创建
s = pd.Series({"a":1,"b":2,"c":3})
print(s)
s1 = pd.Series(s,index=["a","c"])
print(s1)
a 1
b 2
c 3
dtype: int64
a 1
c 3
dtype: int64
属性
print(s.index)
Index(['a', 'b', 'c'], dtype='object')
print(s.values)
[1 2 3]
print(s.shape,s.ndim,s.size)
(3,) 1 3
print(s.dtype)
int64
s.name = 'test'
print(s.name)
test
print(s.loc["a"]) # 显式索引
1
print(s.iloc[2]) # 隐式索引
3
print(s.loc["a":"c"])
a 1
b 2
c 3
Name: test, dtype: int64
print(s.at['a']) # at不支持切片
print(s.iat[2])
1
3
访问数据
# 直接访问
print(s['a'])
1
print(s[s>1]) # 布尔索引
b 2
c 3
Name: test, dtype: int64
print(s.head()) # 默认返回前五行
a 1
b 2
c 3
Name: test, dtype: int64
print(s.tail()) # 默认后五行
print(s.head(2))
a 1
b 2
c 3
Name: test, dtype: int64
a 1
b 2
Name: test, dtype: int64
常用方法
s1 = pd.Series([10,2,np.nan,None,3,4,5],index=['A','B','C','D','E','F','G'],name='date')
print(s1)
A 10.0
B 2.0
C NaN
D NaN
E 3.0
F 4.0
G 5.0
Name: date, dtype: float64
s1.head()
A 10.0
B 2.0
C NaN
D NaN
E 3.0
Name: date, dtype: float64
s1.tail(2)
F 4.0
G 5.0
Name: date, dtype: float64
s1.describe()
count 5.000000
mean 4.800000
std 3.114482
min 2.000000
25% 3.000000
50% 4.000000
75% 5.000000
max 10.000000
Name: date, dtype: float64
# 获取非缺失值的元素个数
print(s1.count())
5
# 获取索引
print(s1.keys()) # 方法
print(s1.index) # 属性
Index(['A', 'B', 'C', 'D', 'E', 'F', 'G'], dtype='object')
Index(['A', 'B', 'C', 'D', 'E', 'F', 'G'], dtype='object')
# 判断
print(s1.isna()) # 检查每个元素是否为缺失值
print(s1.isin([4,5,6])) # 检查每个元素是否在参数集合中
A False
B False
C True
D True
E False
F False
G False
Name: date, dtype: bool
A False
B False
C False
D False
E False
F True
G True
Name: date, dtype: bool
print(s1.mean())
print(s1.std())
print(s1.var())
print(s1.sum())
print(s1.max())
print(s1.min())
print(s1.median()) # 缺失值都不参与运算
4.8
3.1144823004794877
9.700000000000001
24.0
10.0
2.0
4.0
s1.sort_values()
B 2.0
E 3.0
F 4.0
G 5.0
A 10.0
C NaN
D NaN
Name: date, dtype: float64
print(s1.quantile(0.25))
3.0
# 众数
print(s1.mode())
0 2.0
1 3.0
2 4.0
3 5.0
4 10.0
Name: date, dtype: float64
print(s1.value_counts()) # 每个元素的计数
date
10.0 1
2.0 1
3.0 1
4.0 1
5.0 1
Name: count, dtype: int64
# 去重
s1.drop_duplicates() # 缺失值也会被去重
A 10.0
B 2.0
C NaN
E 3.0
F 4.0
G 5.0
Name: date, dtype: float64
s1.sort_index() # 按索引排序
A 10.0
B 2.0
C NaN
D NaN
E 3.0
F 4.0
G 5.0
Name: date, dtype: float64
s1.sort_values() # 按值排序
B 2.0
E 3.0
F 4.0
G 5.0
A 10.0
C NaN
D NaN
Name: date, dtype: float64
s1.diff() # 计算前后两个之间的差值
A NaN
B -8.0
C NaN
D NaN
E NaN
F 1.0
G 1.0
Name: date, dtype: float64
