有没有一种方法可以将pandas序列/Datetime序列与相同行的numpy矩阵连接起来

Question

我使用numpy创建了一个给定大小的随机矩阵。对于时间序列模拟，我为相应的矩阵创建了一个频率为一个月的时间序列。现在，我想将它们组合起来，并将它们作为pandas数据帧。这就是我目前所拥有的

import numpy as np
import pandas as pd
cols = ['time', 'cases', 'deaths', 'recoveries']
data = np.random.randint(0,50,(50,3))
times = pd.date_range('2019-12-01', periods=50, freq='MS')
df = pd.DataFrame(pd.concat(times, data, ignore_index=True), columns=cols)

这在第8行给出了以下错误-

TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid

因此，我尝试使用times = pd.Series(pd.date_range('2019-12-01', periods=50, freq='MS'))将其转换为系列，但这反过来又产生了错误-

TypeError: first argument must be an iterable of pandas objects, you passed an object of type "Series"

预期O/p-

|   time     |cases|deaths|recoveries|
|------------------------------------|
| 2019-12-01 | 0   | 0    | 0        |
| 2020-01-01 | 1   | 0    | 0        |
| 2020-02-01 | 2   | 1    | 0        |

Answer 1

我建议创建DatetimeIndex列，以便通过pandas的类似日期时间的方法进行处理：

#removed time column
cols = ['cases', 'deaths', 'recoveries']
data = np.random.randint(0,50,(50,3))
#added time in name parameter
times = pd.date_range('2019-12-01', periods=50, freq='MS', name='time')
#removed concat and added index parameter
df = pd.DataFrame(data, columns=cols, index=times)
print (df.head(10))
cases  deaths  recoveries
time                                 
2019-12-01     28      44          25
2020-01-01     21      23          26
2020-02-01     15      17           5
2020-03-01     35       3          42
2020-04-01     46       7           3
2020-05-01     23      47          28
2020-06-01     31      30          34
2020-07-01      8       4          15
2020-08-01     46      14          24
2020-09-01     43      47           6

如果需要列，只添加DataFrame.reset_index:

df = pd.DataFrame(data, columns=cols, index=times).reset_index()
print (df.head(10))
time  cases  deaths  recoveries
0 2019-12-01      2      26          43
1 2020-01-01     43      40          41
2 2020-02-01     23      12          22
3 2020-03-01     43      37          28
4 2020-04-01      7      26          20
5 2020-05-01     19      46          41
6 2020-06-01     43       1           0
7 2020-07-01     19      42           4
8 2020-08-01     14      39          40
9 2020-09-01     15       8          25