Resampler

方法

min

返回重新采样后的最小值。

dataframe.Resampler.min(skipna=True, numeric_only=False) #包含参数"skipna"，代表是否忽略缺失值，默认为"True"；参数"numeric_only"，代表是否只计算数值列，默认为"False"。

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
return r1.min() #以5秒为采样周期，返回各个采样周期内的最小值

max

返回重新采样后的最大值。

dataframe.Resampler.max(skipna=True, numeric_only=False)

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
return r1.max()

sum

返回重新采样后的总和。

dataframe.Resampler.sum(skipna=True, numeric_only=False)

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
return r1.sum()

std

返回重新采样后的标准差。

dataframe.Resampler.std(skipna=True, numeric_only=False, ddof=n) #参数"ddof"，表示用于标准差计算的自由度调整（Delta Degrees of Freedom）。n代表自由度调整数值，具体指从样本数量中减去的数值。默认情况下，ddof=1，表示计算样本标准差。如果需要计算总体标准差，可以将ddof设置为0。

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
return r1.std()

var

返回重新采样后的方差。

dataframe.Resampler.var(skipna=True, numeric_only=False, ddof=n)

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
return r1.var()

mean

返回重新采样后的平均值。

dataframe.Resampler.mean(skipna=True, numeric_only=False)

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
return r1.mean()

apply

将函数应用到重新采样后的数据。

dataframe.Resampler.apply()

#示范
d1 = DataFrame({
  "timeline": [
    Timestamp('2013-01-01 09:00:00'),
    Timestamp('2013-01-01 09:00:01'),
    Timestamp('2013-01-01 09:00:02'),
    Timestamp('2013-01-01 09:00:03'),
    Timestamp('2013-01-01 09:00:04'),
    Timestamp('2013-01-01 09:00:05'),
    Timestamp('2013-01-01 09:00:06'),
    Timestamp('2013-01-01 09:00:07'),
    Timestamp('2013-01-01 09:00:08'),
    Timestamp('2013-01-01 09:00:09'),
  ],
  "price": [
    4, 6, 1, 4, 7, 1, 5, 8, 2, 9,
  ]
})
r1 = d1[["price"]].resample('5s', timeline=d1["timeline"])
def cal_mean(x):
  return x["price"].mean()
return r1.apply(cal_mean)