Python从零开始第三章数据处理与分析python.query()函数
Python从零开始第三章数据处理与分析python.query()函数
用户1359560
发布于 2019-04-01 15:07:24
发布于 2019-04-01 15:07:24
文章被收录于专栏：生信小驿站
=============================================== 本文主要介绍使用python.query()函数对数据框进行（挑选行）的操作
- 构建数据框
import pandas as pd
d={
'name':['a','n','c','d','e','f'],
'Gender':['male','female','male','male','female','female'],
'age':[23,24,24,22,21,20],
'hight':[173,174,164,172,161,160],
'weight1':[53,74,44,62,71,60],
'weight2':[53,64,54,66,81,50]
}
df=pd.DataFrame(d)
df
Out[6]:
name Gender age hight weight1 weight2
0 a male 23 173 53 53
1 n female 24 174 74 64
2 c male 24 164 44 54
3 d male 22 172 62 66
4 e female 21 161 71 81
5 f female 20 160 60 50- 一般来说如果进行行挑选，可以进行的操作是：
df[df.age==24]
Out[13]:
name Gender age hight weight1 weight2
1 n female 24 174 74 64
2 c male 24 164 44 54
df[(df.age==24 )&( df.hight ==174)]
Out[14]:
name Gender age hight weight1 weight2
1 n female 24 174 74 64- 但是如果用python.query函数，则更加有逻辑且代码更优雅
df.query("age==24")
Out[20]:
name Gender age hight weight1 weight2
1 n female 24 174 74 64
2 c male 24 164 44 54
df.query("age==24").query('hight==174')
Out[21]:
name Gender age hight weight1 weight2
1 n female 24 174 74 64
Out[29]:
name Gender age hight weight1 weight2
0 a male 23 173 53 53
2 c male 24 164 44 54
3 d male 22 172 62 66
df.query('index > 2')
Out[47]:
name Gender age hight weight1 weight2
3 d male 22 172 62 66
4 e female 21 161 71 81
5 f female 20 160 60 50
df.query('Gender =="male" and name =="a"')
Out[30]:
name Gender age hight weight1 weight2
0 a male 23 173 53 53
df.query('Gender =="male" and age<24')
Out[31]:
name Gender age hight weight1 weight2
0 a male 23 173 53 53
3 d male 22 172 62 66- 除此之外，query（）函数还可以进行不同列之间的值对比：
df.query('weight1 > weight2')
Out[22]:
name Gender age hight weight1 weight2
1 n female 24 174 74 64
5 f female 20 160 60 50本文参与 腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。
原始发表：2019.03.30 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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