相关疑难解决方法(0)

我接受了我的系列并将其强制转换为dtype =的日期时间列datetime64[ns](虽然只需要一天的分辨率......不确定如何更改).

import pandas as pd
df = pd.read_csv('somefile.csv')
column = df['date']
column = pd.to_datetime(column, coerce=True)

但绘图不起作用:

ipdb> column.plot(kind='hist')
*** TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('float64')

我想绘制一个直方图,只显示按周,月或年的日期计数.

当然有办法做到这一点pandas？

我有DataFrame几个时间序列:

         divida    movav12       var  varmovav12
Date                                            
2004-01       0        NaN       NaN         NaN
2004-02       0        NaN       NaN         NaN
2004-03       0        NaN       NaN         NaN
2004-04      34        NaN       inf         NaN
2004-05      30        NaN -0.117647         NaN
2004-06      44        NaN  0.466667         NaN
2004-07      35        NaN -0.204545         NaN
2004-08      31        NaN -0.114286         NaN
2004-09      30        NaN -0.032258         NaN
2004-10      24        NaN -0.200000         NaN
2004-11      41        NaN  0.708333         NaN
2004-12      29  24.833333 -0.292683         NaN
2005-01      31  27.416667  0.068966    0.104027
2005-02      28  29.750000 -0.096774    0.085106
2005-03      27 …

假设有以下内容DataFrame:

rng = pd.date_range('1/1/2011', periods=72, freq='H')
np.random.seed(10)
n = 10
df = pd.DataFrame(
    {
        "datetime": np.random.choice(rng,n),
        "cat": np.random.choice(['a','b','b'], n),
        "val": np.random.randint(0,5, size=n)
        }
    )

如果我现在groupby:

gb = df.groupby(['cat','datetime']).sum()

我得到cat每小时的总数:

cat datetime            val
a   2011-01-01 00:00:00 1
    2011-01-01 09:00:00 3
    2011-01-02 16:00:00 1
    2011-01-03 16:00:00 1
b   2011-01-01 08:00:00 4
    2011-01-01 15:00:00 3
    2011-01-01 16:00:00 3
    2011-01-02 04:00:00 4
    2011-01-02 05:00:00 1
    2011-01-02 12:00:00 4

但是,我希望有类似的东西:

cat datetime   val
a   2011-01-01 4
    2011-01-02 1
    2011-01-03 …

说我有以下值：

                                   money_spent
time                 
2014-10-06 17:59:40.016000-04:00      1.832128
2014-10-06 17:59:41.771000-04:00      2.671048
2014-10-06 17:59:43.001000-04:00      2.019434
2014-10-06 17:59:44.792000-04:00      1.294051
2014-10-06 17:59:48.741000-04:00      0.867856

我希望能衡量每秒钟花费的金钱2。更具体地说，对于输出中的每个时间戳，我需要查看最近2秒钟内花费的资金。

当我做：

df.resample('2S', how='last')

我得到：

                                money_spent
time               
2014-10-06 17:59:40-04:00          2.671048
2014-10-06 17:59:42-04:00          2.019434
2014-10-06 17:59:44-04:00          1.294051
2014-10-06 17:59:46-04:00               NaN
2014-10-06 17:59:48-04:00          0.867856

这不是我所期望的。首先，请注意，再采样DF的第一项是2.671048，但那是在时间17:59:40，即使按照原来的数据帧，没钱花了还没有。那怎么可能？

如何从这个csv中获取使用Python/pandas的5分钟数据？每隔5分钟我会尝试在5分钟的间隔内获得DATE,TIME,OPEN,HIGH,LOW,CLOSE,VOLUME.

DATE       TIME     OPEN    HIGH    LOW     CLOSE   VOLUME
02/03/1997 09:04:00 3046.00 3048.50 3046.00 3047.50 505          
02/03/1997 09:05:00 3047.00 3048.00 3046.00 3047.00 162          
02/03/1997 09:06:00 3047.50 3048.00 3047.00 3047.50 98           
02/03/1997 09:07:00 3047.50 3047.50 3047.00 3047.50 228          
02/03/1997 09:08:00 3048.00 3048.00 3047.50 3048.00 136          
02/03/1997 09:09:00 3048.00 3048.00 3046.50 3046.50 174          
02/03/1997 09:10:00 3046.50 3046.50 3045.00 3045.00 134          
02/03/1997 09:11:00 3045.50 3046.00 3044.00 3045.00 43           
02/03/1997 09:12:00 3045.00 3045.50 3045.00 3045.00 214          
02/03/1997 09:13:00 3045.50 3045.50 3045.50 3045.50 8            
02/03/1997 09:14:00 …