pandas dataframe中的数据选择4

import pandas as pd
import numpy as np

df= pd.read_csv('data/sample_data.csv',index_col=0)
df

         state  color   food    age height  score
Jane      NY    blue    Steak   30  165     4.6
Niko      TX    green   Lamb    2   70      8.3
Aaron     FL    red     Mango   12  120     9.0
Penelope  AL    white   Apple   4   80      3.3
Dean      AK    gray    Cheese  32  180     1.8
Christina  TX   black   Melon   33  172     9.5
Cornelia    TX  red     Beans   69  150      2.2

对比

df[2:6]

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上边的例子选择了行数字索引2--6的数据

df['Jane':'Aaron']

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上边的例子选择了行Jane--Aaron的数据

df.iloc[2:6]

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df.loc['Jane':'Aaron']

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显然下边的用法更加清晰
同理对比一下Series

f=df['food']

f

Jane          Steak
Niko           Lamb
Aaron         Mango
Penelope      Apple
Dean         Cheese
Christina     Melon
Cornelia      Beans
Name: food, dtype: object

f[2:4]

Aaron       Mango
Penelope    Apple
Name: food, dtype: object

f.iloc[2:4]

Aaron       Mango
Penelope    Apple
Name: food, dtype: object

f['Niko':'Dean']

Niko          Lamb
Aaron        Mango
Penelope     Apple
Dean        Cheese
Name: food, dtype: object

f.loc['Niko':'Dean']

Niko          Lamb
Aaron        Mango
Penelope     Apple
Dean        Cheese
Name: food, dtype: object

f['Dean']

'Cheese'

f.loc['Dean']

'Cheese'

f[['Dean','Jane','Niko']]

Dean    Cheese
Jane     Steak
Niko      Lamb
Name: food, dtype: object

f.loc[['Dean','Jane','Niko']]

Dean    Cheese
Jane     Steak
Niko      Lamb
Name: food, dtype: object

说明pandas 是灵活的，但是为了记忆及使用方便，统一都使用iloc，loc
默认情况下从csv读取数据生成的默认行索引

df1=pd.read_csv('data/sample_data.csv')

df1

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df1.index

RangeIndex(start=0, stop=7, step=1)

df1.loc[[1,3,5],['food','state']]

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df1.iloc[[1,3,5],[3,1]]

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上边是使用名字索引，还是使用数字索引比较清晰，一看就知道，不用进行思考

df1.iloc[:3]

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df1.loc[:3]

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注意上两个例子的区别，在进行切片选择是，iloc不包含最后一个，是[) 前闭后开区间， 而 loc 则是[] 闭区间