1 Series

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線性的數據結構，series是一個一維數組

Pandas會默認用0到-1來作為series的index，但也可以自己指定index(可以把index理解為dict里面的key)

1.1 創造一個series數據

import pandas as pd import numpy as nps = pd.Series([9, 'zheng', 'beijing', 128])print(s)

• 打印

0? ? ? ? ?9

1? ? ? ? zheng

2? ? ? ? ?beijing

3? ? ? ? 128

dtype: object

• 訪問其中某個數據

print(s[1:2])

# 打印

1? ? zheng

dtype: object

1.2 指定index

import pandas as pd import numpy as nps = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])print(s)

• 打印

1 9 2 zheng 3 beijing e 128 f usa g 990 dtype: object

• 根據索引值找出值

print(s['f'])? ?# usa

1.3 用dictionary構造一個series

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "car": None}sa = pd.Series(s, name="age")print(sa)

• 打印

car NaN jack 19.0 mary 18.0 ton 20.0 Name: age, dtype: float64

• 檢測類型

print(type(sa))? ?# <class 'pandas.core.series.Series'>

1.4 用numpy ndarray構造一個Series

• 生成一個隨機數

import pandas as pd import numpy as npnum_abc = pd.Series(np.random.randn(5), index=list('abcde')) num = pd.Series(np.random.randn(5))print(num) print(num_abc)# 打印 0 -0.102860 1 -1.138242 2 1.408063 3 -0.893559 4 1.378845 dtype: float64 a -0.658398 b 1.568236 c 0.535451 d 0.103117 e -1.556231 dtype: float64

1.5 選擇數據

import pandas as pd import numpy as nps = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index[1,2,3,'e', 'f', 'g'])print(s[1:3]) # 選擇第1到3個，包左不包右 zhehg beijing print(s[[1,3]]) # 選擇第1個和第3個，zheng 128 print(s[:-1]) #選擇第1個到倒數第1個， 9 zheng beijing 128 usa

1.6 操作數據

import pandas as pd import numpy as nps = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])sum = s[1:3] + s[1:3] sum1 = s[1:4] + s[1:4] sum2 = s[1:3] + s[1:4] sum3 = s[:3] + s[1:]print(sum) print(sum1) print(sum2) print(sum3)

• 打印

2 zhengzheng 3 beijingbeijing dtype: object 2 zhengzheng 3 beijingbeijing e 256 dtype: object 2 zhengzheng 3 beijingbeijing e NaN dtype: object 1 NaN 2 zhengzheng 3 beijingbeijing e NaN f NaN g NaN dtype: object

1.7 查找

• 是否存在

USA in s # true

• 范圍查找

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")print(sa[sa>19])

• 中位數

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")print(sa.median()) # 20

• 判斷是否大于中位數

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")print(sa>sa.median())

找出大于中位數的數

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")print(sa[sa > sa.median()])

• 中位數

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")more_than_midian = sa>sa.median()print(more_than_midian)print('---------------------')print(sa[more_than_midian])

1.8 Series賦值

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")print(s)print('-----------------')sa['ton'] = 99print(sa)

1.9 滿足條件的統一賦值

import pandas as pd import numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}sa = pd.Series(s, name="age")print(s) # 打印原字典print('---------------') # 分割線sa[sa>19] = 88 # 將所有大于19的統一改為88print(sa) #打印更改之后的數據print('-------------------') # 分割線print(sa / 2) # 將所有數據除以2

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