Pandas中的选择

1.选择

更多细节可见官方文档

import pandas as pd
ID = [1,2,3]
Name = ['Student_001','Student_002','Student_003']
Age = [16,26,33]
Score = [87,92,100]
# 自定义的索引名称
index = ['x','y','z']

df = pd.DataFrame({'ID':ID,'Name':Name,'Age':Age,'Score':Score})

自定义索引	ID	Name	Age	AgeScore
x	1	Student_001	16	87
y	2	Student_002	26	92
z	3	Student_003	33	100

1.1单个值的选择

df.at[ ]

Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.

和 loc 类似，都是使用标签(即名称<自己命名的字符>)进行索引

at = df.at['x','ID']
# 或at = df['ID'].at['x']
out:1

df.iat[ ]

Similar to iloc, in that both provide integer-based lookups. Useiat if you only need to get or set a single value in a DataFrame or Series.

和 iloc 类似，都是基于整数进行索引，即 [0-length-1]

iat = df.iat[0,0] #0行0列，从0开始；类似于线性代数的矩阵
# 或iat = df['ID'],iat[0]
out:1

1.2整行 (row) 和整列 (column) 的选择

df.loc[row(名称),column(名称) ]

Access a group of rows and columns by label(s) or a boolean array. .loc[] is primarily label based, but may also be used with a boolean array.

注意：通过标签(label)进行索引

loc = df.loc[:,'Age'] #选择Age整列，结果以Series的形式显示

df.iloc[ row,column]

.iloc[] is primarily integer position based (from 0 tolength-1 of the axis), but may also be used with a boolean array.

row 和column 的值均为 [0~length-1]

iloc = df.iloc[:,2] #选择Age整列

结果均为下图形式：

PS:关于 df.ix[ ] 的说明：在 pandas 的 1.0.0 版本开始，移除了 Series.ix and DataFrame.ix 方法使用 DataFrame 的 loc 方法或者iloc 方法进行替换！