灵感来自unutbu

print(df.dtypes)                                 #Make sure the format is 'object'. Rerunning this after index will not show values.
columnName = 'YourColumnName'
df[columnName+'index'] = df[columnName]          #Create a new column for index
df.set_index(columnName+'index', inplace=True)   #To build index on the timestamp/dates
df.loc['2020-09-03 01:00':'2020-09-06']          #Select range from the index. This is your new Dataframe.

你可以用pd.date_range()和Timestamp来做。 假设你已经使用parse_dates选项读取了一个带日期列的csv文件:

df = pd.read_csv('my_file.csv', parse_dates=['my_date_col'])

然后你可以定义一个日期范围索引:

rge = pd.date_range(end='15/6/2020', periods=2)

然后通过地图根据日期过滤你的值:

df.loc[df['my_date_col'].map(lambda row: row.date() in rge)]

有两种可能的解决方案:

使用布尔掩码，然后使用df.loc[掩码] 将日期列设置为DatetimeIndex，然后使用df[start_date: end_date]

使用布尔掩码:

确保df['date']是dtype datetime64[ns]的Series:

df['date'] = pd.to_datetime(df['date'])  

制作一个布尔掩码。Start_date和end_date可以是datetime.datetimes， np。datetime64s pd。时间戳，甚至是datetime字符串:

#greater than the start date and smaller than the end date
mask = (df['date'] > start_date) & (df['date'] <= end_date)

选择子数据帧:

df.loc[mask]

或者重新赋值给df

df = df.loc[mask]

例如,

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((200,3)))
df['date'] = pd.date_range('2000-1-1', periods=200, freq='D')
mask = (df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10')
print(df.loc[mask])

收益率

            0         1         2       date
153  0.208875  0.727656  0.037787 2000-06-02
154  0.750800  0.776498  0.237716 2000-06-03
155  0.812008  0.127338  0.397240 2000-06-04
156  0.639937  0.207359  0.533527 2000-06-05
157  0.416998  0.845658  0.872826 2000-06-06
158  0.440069  0.338690  0.847545 2000-06-07
159  0.202354  0.624833  0.740254 2000-06-08
160  0.465746  0.080888  0.155452 2000-06-09
161  0.858232  0.190321  0.432574 2000-06-10

使用DatetimeIndex:

如果要按日期进行大量选择，则设置 首先将日期列作为索引。然后可以使用按日期选择行 df.loc [start_date: end_date]。

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((200,3)))
df['date'] = pd.date_range('2000-1-1', periods=200, freq='D')
df = df.set_index(['date'])
print(df.loc['2000-6-1':'2000-6-10'])

收益率

                   0         1         2
date                                    
2000-06-01  0.040457  0.326594  0.492136    # <- includes start_date
2000-06-02  0.279323  0.877446  0.464523
2000-06-03  0.328068  0.837669  0.608559
2000-06-04  0.107959  0.678297  0.517435
2000-06-05  0.131555  0.418380  0.025725
2000-06-06  0.999961  0.619517  0.206108
2000-06-07  0.129270  0.024533  0.154769
2000-06-08  0.441010  0.741781  0.470402
2000-06-09  0.682101  0.375660  0.009916
2000-06-10  0.754488  0.352293  0.339337

而Python列表索引，例如seq[start:end]包括开始，但不包括结束，相比之下，Pandas df。Loc [start_date: end_date]在结果中包含两个端点，如果它们在索引中。但是，start_date和end_date都不能在索引中。

还要注意pd。Read_csv有一个parse_dates参数，您可以使用该参数将日期列解析为datetime64s。因此，如果使用parse_dates，则不需要使用df['date'] = pd.to_datetime(df['date'])。

另一种方法是使用pandas.DataFrame.query()方法。让我给你们看一个关于下面数据帧的例子，叫做df。

>>> df = pd.DataFrame(np.random.random((5, 1)), columns=['col_1'])
>>> df['date'] = pd.date_range('2020-1-1', periods=5, freq='D')
>>> print(df)
      col_1       date
0  0.015198 2020-01-01
1  0.638600 2020-01-02
2  0.348485 2020-01-03
3  0.247583 2020-01-04
4  0.581835 2020-01-05

作为参数，使用条件进行过滤，如下所示:

>>> start_date, end_date = '2020-01-02', '2020-01-04'
>>> print(df.query('date >= @start_date and date <= @end_date'))
      col_1       date
1  0.244104 2020-01-02
2  0.374775 2020-01-03
3  0.510053 2020-01-04

如果你不想包含边界，只需要像下面这样改变条件:

>>> print(df.query('date > @start_date and date < @end_date'))
      col_1       date
2  0.374775 2020-01-03

import pandas as pd

technologies = ({
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas","Hadoop","Spark"],
    'Fee' :[22000,25000,23000,24000,26000,25000,25000],
    'Duration':['30days','50days','55days','40days','60days','35days','55days'],
    'Discount':[1000,2300,1000,1200,2500,1300,1400],
    'InsertedDates':["2021-11-14","2021-11-15","2021-11-16","2021-11-17","2021-11-18","2021-11-19","2021-11-20"]
               })
df = pd.DataFrame(technologies)
print(df)

使用pandas.DataFrame.loc按日期过滤行

方法1:

    mask = (df['InsertedDates'] > start_date) & (df['InsertedDates'] <= end_date)

    df2 = df.loc[mask]
    print(df2)

方法2:

    start_date = '2021-11-15'
    end_date = '2021-11-19'
    after_start_date = df["InsertedDates"] >= start_date
    before_end_date = df["InsertedDates"] <= end_date
    between_two_dates = after_start_date & before_end_date


    df2 = df.loc[between_two_dates]
    print(df2)

使用pandas.DataFrame.query()选择数据帧行

start_date = '2021-11-15'
end_date   = '2021-11-18'
df2 = df.query('InsertedDates >= @start_date and InsertedDates <= @end_date')
print(df2)

使用datafframe .query()选择两个日期之间的行

start_date = '2021-11-15'
end_date = '2021-11-18'
df2 = df.query('InsertedDates > @start_date and InsertedDates < @end_date')
print(df2)

pandas.Series.between()函数使用两个日期

df2 = df.loc[df["InsertedDates"].between("2021-11-16", "2021-11-18")]
print(df2)

使用DataFrame.isin()在两个日期之间选择数据帧行

df2 = df[df["InsertedDates"].isin(pd.date_range("2021-11-15", "2021-11-17"))]
print(df2)

Pandas 0.22有一个between()函数。 使回答这个问题更容易，代码更可读。

# create a single column DataFrame with dates going from Jan 1st 2018 to Jan 1st 2019
df = pd.DataFrame({'dates':pd.date_range('2018-01-01','2019-01-01')})

假设你想获取2018年11月27日至2019年1月15日之间的日期:

# use the between statement to get a boolean mask
df['dates'].between('2018-11-27','2019-01-15', inclusive=False)

0    False
1    False
2    False
3    False
4    False

# you can pass this boolean mask straight to loc
df.loc[df['dates'].between('2018-11-27','2019-01-15', inclusive=False)]

    dates
331 2018-11-28
332 2018-11-29
333 2018-11-30
334 2018-12-01
335 2018-12-02

注意包含的参数。当你想要明确你的范围时，这非常有用。注意当设置为True时，我们也会返回2018年11月27日:

df.loc[df['dates'].between('2018-11-27','2019-01-15', inclusive=True)]

    dates
330 2018-11-27
331 2018-11-28
332 2018-11-29
333 2018-11-30
334 2018-12-01

这种方法也比前面提到的isin方法快:

%%timeit -n 5
df.loc[df['dates'].between('2018-11-27','2019-01-15', inclusive=True)]
868 µs ± 164 µs per loop (mean ± std. dev. of 7 runs, 5 loops each)


%%timeit -n 5

df.loc[df['dates'].isin(pd.date_range('2018-01-01','2019-01-01'))]
1.53 ms ± 305 µs per loop (mean ± std. dev. of 7 runs, 5 loops each)

但是，它并不比当前接受的unutbu提供的答案快，只有在掩码已经创建的情况下。但如果掩码是动态的，需要一遍又一遍地重新分配，我的方法可能会更有效:

# already create the mask THEN time the function

start_date = dt.datetime(2018,11,27)
end_date = dt.datetime(2019,1,15)
mask = (df['dates'] > start_date) & (df['dates'] <= end_date)

%%timeit -n 5
df.loc[mask]
191 µs ± 28.5 µs per loop (mean ± std. dev. of 7 runs, 5 loops each)