pandas如何使用列表和字典创建 Series
前言：
pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。
为了让大家对pandas的操作更加熟练，我整理了一些关于pandas的小操作，会依次为大家展示
今天我将先为大家如何关于pandas如何使用列表和字典创建 series。
01 使用列表创建 series
import pandas as pd ser1 = pd.series([1.5, 2.5, 3, 4.5, 5.0, 6]) print(ser1)
output:
0 1.5
1 2.5
2 3.0
3 4.5
4 5.0
5 6.0
dtype: float64
02 使用 name 参数创建 series
import pandas as pd ser2 = pd.series(["india", "canada", "germany"], name="countries") print(ser2)
output:
0 india
1 canada
2 germany
name: countries, dtype: object
03 使用简写的列表创建 series
import pandas as pd ser3 = pd.series(["a"]*4) print(ser3)
output:
0 a
1 a
2 a
3 a
dtype: object
04 使用字典创建 series
import pandas as pd
ser4 = pd.series({"india": "new delhi",
"japan": "tokyo",
"uk": "london"})
print(ser4)
output:
india new delhi
japan tokyo
uk london
dtype: object
05 如何使用 numpy 函数创建 series
import pandas as pd import numpy as np ser1 = pd.series(np.linspace(1, 10, 5)) print(ser1) ser2 = pd.series(np.random.normal(size=5)) print(ser2)
output:
0 1.00
1 3.25
2 5.50
3 7.75
4 10.00
dtype: float64
0 -1.694452
1 -1.570006
2 1.713794
3 0.338292
4 0.803511
dtype: float64
06 如何获取 series 的索引和值
import pandas as pd
import numpy as np
ser1 = pd.series({"india": "new delhi",
"japan": "tokyo",
"uk": "london"})
print(ser1.values)
print(ser1.index)
print("\n")
ser2 = pd.series(np.random.normal(size=5))
print(ser2.index)
print(ser2.values)
output:
['new delhi' 'tokyo' 'london']
index(['india', 'japan', 'uk'], dtype='object')
rangeindex(start=0, stop=5, step=1)
[ 0.66265478 -0.72222211 0.3608642 1.40955436 1.3096732 ]
07 如何在创建 series 时指定索引
import pandas as pd
values = ["india", "canada", "australia",
"japan", "germany", "france"]
code = ["ind", "can", "aus", "jap", "ger", "fra"]
ser1 = pd.series(values, index=code)
print(ser1)
output:
ind india
can canada
aus australia
jap japan
ger germany
fra france
dtype: object
08 如何获取 series 的大小和形状
import pandas as pd
values = ["india", "canada", "australia",
"japan", "germany", "france"]
code = ["ind", "can", "aus", "jap", "ger", "fra"]
ser1 = pd.series(values, index=code)
print(len(ser1))
print(ser1.shape)
print(ser1.size)
output:
6
(6,)
6
09 如何获取 series 开始或末尾几行数据
head()函数：
import pandas as pd
values = ["india", "canada", "australia",
"japan", "germany", "france"]
code = ["ind", "can", "aus", "jap", "ger", "fra"]
ser1 = pd.series(values, index=code)
print("-----head()-----")
print(ser1.head())
print("\n\n-----head(2)-----")
print(ser1.head(2))
output:
-----head()-----
ind india
can canada
aus australia
jap japan
ger germany
dtype: object
-----head(2)-----
ind india
can canada
dtype: object
tail()函数：
import pandas as pd
values = ["india", "canada", "australia",
"japan", "germany", "france"]
code = ["ind", "can", "aus", "jap", "ger", "fra"]
ser1 = pd.series(values, index=code)
print("-----tail()-----")
print(ser1.tail())
print("\n\n-----tail(2)-----")
print(ser1.tail(2))
output:
-----tail()-----
can canada
aus australia
jap japan
ger germany
fra france
dtype: object
-----tail(2)-----
ger germany
fra france
dtype: object
take()函数：
import pandas as pd
values = ["india", "canada", "australia",
"japan", "germany", "france"]
code = ["ind", "can", "aus", "jap", "ger", "fra"]
ser1 = pd.series(values, index=code)
print("-----take()-----")
print(ser1.take([2, 4, 5]))
output:
-----take()-----
aus australia
ger germany
fra france
dtype: object
10 使用切片获取 series 子集
import pandas as pd
num = [000, 100, 200, 300, 400, 500, 600, 700, 800, 900]
idx = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
series = pd.series(num, index=idx)
print("\n [2:2] \n")
print(series[2:4])
print("\n [1:6:2] \n")
print(series[1:6:2])
print("\n [:6] \n")
print(series[:6])
print("\n [4:] \n")
print(series[4:])
print("\n [:4:2] \n")
print(series[:4:2])
print("\n [4::2] \n")
print(series[4::2])
print("\n [::-1] \n")
print(series[::-1])
output：
[2:2]
c 200
d 300
dtype: int64
[1:6:2]
b 100
d 300
f 500
dtype: int64
[:6]
a 0
b 100
c 200
d 300
e 400
f 500
dtype: int64
[4:]
e 400
f 500
g 600
h 700
i 800
j 900
dtype: int64
[:4:2]
a 0
c 200
dtype: int64
[4::2]
e 400
g 600
i 800
dtype: int64
[::-1]
j 900
i 800
h 700
g 600
f 500
e 400
d 300
c 200
b 100
a 0
dtype: int64
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