python - 如何计算 Pandas 数据框中连续行之间的差异？

Question

我有一个数据框 df，包含三列:count_a、count_b 和 date ;计数是 float ，日期是 2015 年的连续天数。

我正在尝试找出 count_a 和 count_b 列中每一天的计数之间的差异 — 也就是说，我正在尝试计算每一天之间的差异这两列的行和前一行。我已将日期设置为索引，但我无法弄清楚如何执行此操作；有一些关于使用 pd.Series 和 pd.DataFrame.diff 的提示，但我没有找到适用的答案或说明集。

我有点卡住了，希望能在这里得到一些指导。

这是我的数据框的样子:

df=pd.Dataframe({'count_a': {Timestamp('2015-01-01 00:00:00'): 34175.0,
  Timestamp('2015-01-02 00:00:00'): 72640.0,
  Timestamp('2015-01-03 00:00:00'): 109354.0,
  Timestamp('2015-01-04 00:00:00'): 144491.0,
  Timestamp('2015-01-05 00:00:00'): 180355.0,
  Timestamp('2015-01-06 00:00:00'): 214615.0,
  Timestamp('2015-01-07 00:00:00'): 250096.0,
  Timestamp('2015-01-08 00:00:00'): 287880.0,
  Timestamp('2015-01-09 00:00:00'): 332528.0,
  Timestamp('2015-01-10 00:00:00'): 381460.0,
  Timestamp('2015-01-11 00:00:00'): 422981.0,
  Timestamp('2015-01-12 00:00:00'): 463539.0,
  Timestamp('2015-01-13 00:00:00'): 505395.0,
  Timestamp('2015-01-14 00:00:00'): 549027.0,
  Timestamp('2015-01-15 00:00:00'): 595377.0,
  Timestamp('2015-01-16 00:00:00'): 649043.0,
  Timestamp('2015-01-17 00:00:00'): 707727.0,
  Timestamp('2015-01-18 00:00:00'): 761287.0,
  Timestamp('2015-01-19 00:00:00'): 814372.0,
  Timestamp('2015-01-20 00:00:00'): 867096.0,
  Timestamp('2015-01-21 00:00:00'): 920838.0,
  Timestamp('2015-01-22 00:00:00'): 983405.0,
  Timestamp('2015-01-23 00:00:00'): 1067243.0,
  Timestamp('2015-01-24 00:00:00'): 1164421.0,
  Timestamp('2015-01-25 00:00:00'): 1252178.0,
  Timestamp('2015-01-26 00:00:00'): 1341484.0,
  Timestamp('2015-01-27 00:00:00'): 1427600.0,
  Timestamp('2015-01-28 00:00:00'): 1511549.0,
  Timestamp('2015-01-29 00:00:00'): 1594846.0,
  Timestamp('2015-01-30 00:00:00'): 1694226.0,
  Timestamp('2015-01-31 00:00:00'): 1806727.0,
  Timestamp('2015-02-01 00:00:00'): 1899880.0,
  Timestamp('2015-02-02 00:00:00'): 1987978.0,
  Timestamp('2015-02-03 00:00:00'): 2080338.0,
  Timestamp('2015-02-04 00:00:00'): 2175775.0,
  Timestamp('2015-02-05 00:00:00'): 2279525.0,
  Timestamp('2015-02-06 00:00:00'): 2403306.0,
  Timestamp('2015-02-07 00:00:00'): 2545696.0,
  Timestamp('2015-02-08 00:00:00'): 2672464.0,
  Timestamp('2015-02-09 00:00:00'): 2794788.0},
 'count_b': {Timestamp('2015-01-01 00:00:00'): nan,
  Timestamp('2015-01-02 00:00:00'): nan,
  Timestamp('2015-01-03 00:00:00'): nan,
  Timestamp('2015-01-04 00:00:00'): nan,
  Timestamp('2015-01-05 00:00:00'): nan,
  Timestamp('2015-01-06 00:00:00'): nan,
  Timestamp('2015-01-07 00:00:00'): nan,
  Timestamp('2015-01-08 00:00:00'): nan,
  Timestamp('2015-01-09 00:00:00'): nan,
  Timestamp('2015-01-10 00:00:00'): nan,
  Timestamp('2015-01-11 00:00:00'): nan,
  Timestamp('2015-01-12 00:00:00'): nan,
  Timestamp('2015-01-13 00:00:00'): nan,
  Timestamp('2015-01-14 00:00:00'): nan,
  Timestamp('2015-01-15 00:00:00'): nan,
  Timestamp('2015-01-16 00:00:00'): nan,
  Timestamp('2015-01-17 00:00:00'): nan,
  Timestamp('2015-01-18 00:00:00'): nan,
  Timestamp('2015-01-19 00:00:00'): nan,
  Timestamp('2015-01-20 00:00:00'): nan,
  Timestamp('2015-01-21 00:00:00'): nan,
  Timestamp('2015-01-22 00:00:00'): nan,
  Timestamp('2015-01-23 00:00:00'): nan,
  Timestamp('2015-01-24 00:00:00'): 71.0,
  Timestamp('2015-01-25 00:00:00'): 150.0,
  Timestamp('2015-01-26 00:00:00'): 236.0,
  Timestamp('2015-01-27 00:00:00'): 345.0,
  Timestamp('2015-01-28 00:00:00'): 1239.0,
  Timestamp('2015-01-29 00:00:00'): 2228.0,
  Timestamp('2015-01-30 00:00:00'): 7094.0,
  Timestamp('2015-01-31 00:00:00'): 16593.0,
  Timestamp('2015-02-01 00:00:00'): 27190.0,
  Timestamp('2015-02-02 00:00:00'): 37519.0,
  Timestamp('2015-02-03 00:00:00'): 49003.0,
  Timestamp('2015-02-04 00:00:00'): 63323.0,
  Timestamp('2015-02-05 00:00:00'): 79846.0,
  Timestamp('2015-02-06 00:00:00'): 101568.0,
  Timestamp('2015-02-07 00:00:00'): 127120.0,
  Timestamp('2015-02-08 00:00:00'): 149955.0,
  Timestamp('2015-02-09 00:00:00'): 171440.0}})

Answer 1

diff 应该给出期望的结果:

>>> df.diff()
count_a  count_b
2015-01-01      NaN      NaN
2015-01-02    38465      NaN
2015-01-03    36714      NaN
2015-01-04    35137      NaN
2015-01-05    35864      NaN
....
2015-02-07   142390    25552
2015-02-08   126768    22835
2015-02-09   122324    21485