How to find rate of change across successive rows using time and data columns after grouping by a different column using pandas?

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I have a pandas DataFrame of the form:


ID_col time_in_hours data_col
  1        62.5         4
  1        40           3
  1        20           3
  2        30           1
  2        20           5
  3        50           6

What I want to be able to do is, find the rate of change of data_col by using the time_in_hours column. Specifically,

rate_of_change = (data_col[i+1] - data_col[i]) / abs(time_in_hours[ i +1] - time_in_hours[i])

Where i is a given row and the rate_of_change is calculated separately for different IDs

Effectively, I want a new DataFrame of the form:


ID_col time_in_hours data_col  rate_of_change
  1        62.5         4          NaN
  1        40           3         -0.044
  1        20           3          0
  2        30           1          NaN
  2        20           5          0.4
  3        50           6          NaN

How do I go about this?

You can use groupby:

s = df.groupby('ID_col').apply(lambda dft: dft['data_col'].diff() / dft['time_in_hours'].diff().abs())
s.index = s.index.droplevel()


0         NaN
1   -0.044444
2    0.000000
3         NaN
4    0.400000
5         NaN
dtype: float64

pandas.DataFrame.diff, DataFrame.groupby · pandas. Periods to shift for calculating difference, accepts negative values. Take difference over rows (0) or columns (1). Returns Percent change over given number of periods. DataFrame.shift. Shift index by desired number of periods with an optional time freq. Difference with previous column. df.pivot_table(index='Date',columns='Groups',aggfunc=sum) results in. data Groups one two Date 2017-1-1 3.0 NaN 2017-1-2 3.0 4.0 2017-1-3 NaN 5.0 Personally I find this approach much easier to understand, and certainly more pythonic than a convoluted groupby operation. Then if you want the format specified you can just tidy it up:

You can actually get around the groupby + apply given how your DataFrame is sorted. In this case, you can just check if the ID_col is the same as the shifted row.

So calculate the rate of change for everything, and then only assign the values back if they are within a group.

import numpy as np

mask = df.ID_col == df.ID_col.shift(1)
roc = (df.data_col - df.data_col.shift(1))/np.abs(df.time_in_hours - df.time_in_hours.shift(1))

df.loc[mask, 'rate_of_change'] = roc[mask] 
   ID_col  time_in_hours  data_col  rate_of_change
0       1           62.5         4             NaN
1       1           40.0         3       -0.044444
2       1           20.0         3        0.000000
3       2           30.0         1             NaN
4       2           20.0         5        0.400000
5       3           50.0         6             NaN

How to find Percentage Change in pandas, So you are interested to find the percentage change in your data. using pandas pct_change() api and how it can be used with different data Increment to use from time series API (e.g. 'M' or BDay()) The first row will be NaN since that is the first value for column A, B and C. pct_change in groupby. Where i is a given row and the rate_of_change is calculated separately for different IDs. Effectively, I want a new DataFrame of the form: new_df. ID_col time_in_hours data_col rate_of_change 1 62.5 4 NaN 1 40 3 -0.044 1 20 3 0 2 30 1 NaN 2 20 5 0.4 3 50 6 NaN How do I go about this?

You can use pandas.diff:

   lambda x: x['data_col'].diff() / x['time_in_hours'].diff().abs())

1       0         NaN
        1   -0.044444
        2    0.000000
2       3         NaN
        4    0.400000
3       5         NaN
dtype: float64

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