pandas 中链式索引 选择数据1

import pandas as pd
import numpy as np
from tabulate import tabulate

pandas 中链式索引 选择数据1

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

df

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• 链式索引选择数据,示例1

df[['food','age','color']]['age']

Jane         30
Niko          2
Aaron        12
Penelope      4
Dean         32
Christina    33
Cornelia     69
Name: age, dtype: int64

+链式索引选择数据,示例2

df.loc[:,['food','color','age']]['age']

Jane         30
Niko          2
Aaron        12
Penelope      4
Dean         32
Christina    33
Cornelia     69
Name: age, dtype: int64

• 链式索引选择数据,示例3

df.loc[:,['food','color','age']].loc[:,'age']

Jane         30
Niko          2
Aaron        12
Penelope      4
Dean         32
Christina    33
Cornelia     69
Name: age, dtype: int64

• 链式索引选择数据,示例4

a=['food','color','age']
b=['age']
dd=df[a][b]
dd

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注意:上例返回的Dataframe,而不是Series

• 链式索引选择数据,示例5

a=['Niko','Dean'],['food','color','age']
b=['age','color']
dd=df.loc[a][b]
dd

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• 链式索引选择数据,使用iloc,示例6

df.iloc[2:5].iloc[:,-3:]

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• 链式索引选择数据,示例7

df[df['age']>16]['score']

Jane         4.6
Dean         1.8
Christina    9.5
Cornelia     2.2
Name: score, dtype: float64

注意：以上示例都是非习惯用法，复杂度增加了不少
下面演示习惯用法

• 习惯用法1
  选择1列，如下

df['age']

Jane         30
Niko          2
Aaron        12
Penelope      4
Dean         32
Christina    33
Cornelia     69
Name: age, dtype: int64

• 习惯用法2

df.loc[['Niko','Cornelia'],['height','color']]
# df.loc[['Niko', 'Cornelia'], ['state', 'height', 'color']][['height', 'color']] -非习惯用法,尽量不用

• 习惯用法3

df.iloc[2:5,-3:] # 前行，后列

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• 习惯用法4

df.loc['Niko':'Dean',['age','food']]

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• 习惯用法5

df.loc[df['age']>16,'score']

Jane         4.6
Dean         1.8
Christina    9.5
Cornelia     2.2
Name: score, dtype: float64

链式索引调用不好的原因：
原因1：
例如： df.loc[['Aaron', 'Dean', 'Christina']][['age', 'food']]
这个要执行2个操作
1.df.loc[['Aaron', 'Dean', 'Christina']]
2.在1的基础上进行[['age', 'food']] 操作
执行2次
而
df.loc[['Aaron', 'Dean', 'Christina'], ['age', 'food']]
只执行1次
原因2：
产生副本，例子如下

df[df['age'] > 10]['score'] = 99

E:\software\anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

df

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赋值没有效果，实际上发生了如下操作

df_tm=df[df['age']>10]
df_tm['score']=99

E:\software\anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

df_tm

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df

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**实际上给副本赋值了
正确方式 如下
**

df.loc[df['age']>10,'score']=99
df

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