我正在从csv创建一个DataFrame，如下所示:
stock = pd.read_csv('data_in/' + filename + '.csv', skipinitialspace=True)
DataFrame有一个日期列。是否有一种方法可以创建一个新的DataFrame(或者只是覆盖现有的DataFrame)，它只包含日期值落在指定日期范围内或两个指定日期值之间的行?
当前回答
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)
其他回答
我觉得最好的选择是使用直接检查，而不是使用loc函数:
df = df[(df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10')]
这对我很管用。
使用切片的loc函数的主要问题是限制应该出现在实际值中，否则将导致KeyError。
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'])。
你也可以用between:
df[df.some_date.between(start_date, end_date)]
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)
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
