Python 使用日期时间索引进行过滤

通过数据帧的日期时间索引，可以轻松获取日期范围：

df[datetime(2018,1,1):datetime(2018,1,10)]

过滤也很简单：

df[ (df['column A'] = 'Done') & (df['column B'] < 3.14 )]

df[（df['column A']='Done'）&（df['column B']<3.14）]

但是，同时按日期范围和任何其他非日期标准过滤的最佳方法是什么

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((200,3)))
df['date'] = pd.date_range('2018-1-1', periods=200, freq='D')
df = df.set_index(['date'])
print(df.loc['2018-2-1':'2018-2-10'])

希望！这将有助于

3个布尔条件

---

如果存在多个布尔掩码，则可以使用：

m1=df.index>“2018-01-01”
m2=df.index<'2018-01-10'
m3=df[“A列”]=“完成”
m4=df[‘B’列]<3.14
#皮尔斯菲尔德数据样本
df=df[np.logical_和.reduce（[m1，m2，m3，m4]）]
打印（df）
A列B列
2018-01-04完成2.533385
2018-01-06完成2.789072
2018-01-08完成2.230017

我在下面这样做是为了筛选两个数据帧是否具有相同的日期

corn_url = 'https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=WPU012202&scale=left&cosd=1971-01-01&coed=2020-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-06-09&revision_date=2020-06-09&nd=1971-01-01'
wheat_url ='https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=WPU0121&scale=left&cosd=1947-01-01&coed=2020-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-06-09&revision_date=2020-06-09&nd=1947-01-01'


corn = pd.read_csv(corn_url,index_col=0,parse_dates=True)
wheat = pd.read_csv(wheat_url,index_col=0, parse_dates=True)

    corn.head()
    PP Index 1982
    DATE    
    1971-01-01  63.4
    1971-02-01  63.6
    1971-03-01  62.0
    1971-04-01  60.8
    1971-05-01  60.2

wheat.head()
PP Index 1982
DATE    
1947-01-01  53.1
1947-02-01  56.5
1947-03-01  68.0
1947-04-01  66.0
1947-05-01  66.7


wheat = wheat[wheat.index > '1970-12-31']

wheat.head()
PP Index 1982
DATE    
1971-01-01  42.6
1971-02-01  42.6
1971-03-01  41.4
1971-04-01  41.7
1971-05-01  41.8

---

发布一个sampledf及其预期dft，它显示如何按日期过滤，但不显示如何同时按其他列过滤。如果我想按日期范围内的行进行筛选，而a列中的值小于3.14，该怎么办？我可以这样做：df[（df.index>datetime（2018,1,1））&（df.indexnp.random.seed([3, 1415]) df = pd.DataFrame({ 'column A': ['Done', 'Not Done'] * 10, 'column B': np.random.randn(20) + np.pi }, pd.date_range('2017-12-25', periods=20)) df column A column B 2017-12-25 Done 1.011868 2017-12-26 Not Done 1.873127 2017-12-27 Done 1.171093 2017-12-28 Not Done 0.882538 2017-12-29 Done 2.792306 2017-12-30 Not Done 3.114638 2017-12-31 Done 3.457829 2018-01-01 Not Done 3.490375 2018-01-02 Done 3.856957 2018-01-03 Not Done 3.912356 2018-01-04 Done 2.533385 2018-01-05 Not Done 3.493983 2018-01-06 Done 2.789072 2018-01-07 Not Done 2.725724 2018-01-08 Done 2.230017 2018-01-09 Not Done 2.999055 2018-01-10 Done 3.888432 2018-01-11 Not Done 1.637436 2018-01-12 Done 3.752955 2018-01-13 Not Done 3.541812

m1 = df.index > '2018-01-01'
m2 = df.index < '2018-01-10'
m3 = df['column A'] == 'Done'
m4 = df['column B'] < 3.14

#piRSquared's data sample
df = df[np.logical_and.reduce([m1, m2, m3, m4])]
print (df)
           column A  column B
2018-01-04     Done  2.533385
2018-01-06     Done  2.789072
2018-01-08     Done  2.230017

corn_url = 'https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=WPU012202&scale=left&cosd=1971-01-01&coed=2020-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-06-09&revision_date=2020-06-09&nd=1971-01-01'
wheat_url ='https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=WPU0121&scale=left&cosd=1947-01-01&coed=2020-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-06-09&revision_date=2020-06-09&nd=1947-01-01'


corn = pd.read_csv(corn_url,index_col=0,parse_dates=True)
wheat = pd.read_csv(wheat_url,index_col=0, parse_dates=True)

    corn.head()
    PP Index 1982
    DATE    
    1971-01-01  63.4
    1971-02-01  63.6
    1971-03-01  62.0
    1971-04-01  60.8
    1971-05-01  60.2

wheat.head()
PP Index 1982
DATE    
1947-01-01  53.1
1947-02-01  56.5
1947-03-01  68.0
1947-04-01  66.0
1947-05-01  66.7


wheat = wheat[wheat.index > '1970-12-31']

wheat.head()
PP Index 1982
DATE    
1971-01-01  42.6
1971-02-01  42.6
1971-03-01  41.4
1971-04-01  41.7
1971-05-01  41.8