Question

我正在尝试生成带有日期的随机数据（整数），以便可以在其上实践熊猫数据分析命令并绘制时间序列图。

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是否有任何随机数据帧生成器可以生成类似日期间隔为一个月的东西？

Answer 1

您可以使用pandas.util.testing

import pandas.util.testing as testing
import numpy as np
np.random.seed(1)

testing.N, testing.K = 5, 3  # Setting the rows and columns of the desired data

print testing.makeTimeDataFrame(freq='MS')
>>>
                   A         B         C
2000-01-01 -0.488392  0.429949 -0.723245
2000-02-01  1.247192 -0.513568 -0.512677
2000-03-01  0.293828  0.284909  1.190453
2000-04-01 -0.326079 -1.274735 -0.008266
2000-05-01 -0.001980  0.745803  1.519243

或者，如果您需要对生成的随机值进行更多控制，则可以使用

import numpy as np
import pandas as pd
np.random.seed(1)

rows,cols = 5,3
data = np.random.rand(rows,cols) # You can use other random functions to generate values with constraints
tidx = pd.date_range('2019-01-01', periods=rows, freq='MS') # freq='MS'set the frequency of date in months and start from day 1. You can use 'T' for minutes and so on
data_frame = pd.DataFrame(data, columns=['a','b','c'], index=tidx)
print data_frame
>>>
                   a         b         c
2019-01-01  0.992856  0.217750  0.538663
2019-02-01  0.189226  0.847022  0.156730
2019-03-01  0.572417  0.722094  0.868219
2019-04-01  0.023791  0.653147  0.857148
2019-05-01  0.729236  0.076817  0.743955

Answer 2

在DataFrame构造函数中使用numpy.random.rand或numpy.random.randint函数：

np.random.seed(2019)
N = 10
rng = pd.date_range('2019-01-01', freq='MS', periods=N)
df = pd.DataFrame(np.random.rand(N, 3), columns=['temp','depth','acceleration'], index=rng)

print (df)
                temp     depth  acceleration
2019-01-01  0.903482  0.393081      0.623970
2019-02-01  0.637877  0.880499      0.299172
2019-03-01  0.702198  0.903206      0.881382
2019-04-01  0.405750  0.452447      0.267070
2019-05-01  0.162865  0.889215      0.148476
2019-06-01  0.984723  0.032361      0.515351
2019-07-01  0.201129  0.886011      0.513620
2019-08-01  0.578302  0.299283      0.837197
2019-09-01  0.526650  0.104844      0.278129
2019-10-01  0.046595  0.509076      0.472426

如果需要整数：

np.random.seed(2019)
N = 10
rng = pd.date_range('2019-01-01', freq='MS', periods=N)
df = pd.DataFrame(np.random.randint(20, size=(10, 3)), 
                  columns=['temp','depth','acceleration'], 
                  index=rng)

print (df)
            temp  depth  acceleration
2019-01-01     8     18             5
2019-02-01    15     12            10
2019-03-01    16     16             7
2019-04-01     5     19            12
2019-05-01    16     18             5
2019-06-01    16     15             1
2019-07-01    14     12            10
2019-08-01     0     11            18
2019-09-01    15     19             1
2019-10-01     3     16            18

生成带有日期的随机时间序列数据

2 个答案: