Return to natural numbers from a logarithmic difference in python

Question

I'm working with time series data and have transformed numbers to logarithmic differences with numpy.

df['dlog']= np.log(df['columnx']).diff()

Then I made predictions with that transformation.

How can I return to normal numbers?

Answer 1

• Reversing the transformation shouldn't be necessary, because columnx still exists in df
• .diff() calculates the difference of a Series element compared with another element in the Series.
  • The first row of dlog is NaN. Without a "base" number (e.g. np.log(764677)) there is not a way to step back that transformation

df = pd.DataFrame({'columnx': [np.random.randint(1_000_000) for _ in range(100)]})
df['dlog'] = np.log(df.columnx).diff()

Output:

 columnx      dlog
  764677       NaN
  884574  0.145653
  621005 -0.353767
  408960 -0.417722
  248456 -0.498352

Undo np.log with np.exp

• Use np.exp to transform from a logarithmic to linear scale.

df = pd.DataFrame({'columnx': [np.random.randint(1_000_000) for _ in range(100)]})
df['log'] = np.log(df.columnx)
df['linear'] = np.exp(df.log)

Output:

 columnx        log    linear
  412863  12.930871  412863.0
  437565  12.988981  437565.0
  690926  13.445788  690926.0
  198166  12.196860  198166.0
  427894  12.966631  427894.0

Further Notes:

• Without a reproducible set, it's not possible to offer further solutions
  • You can include some data: How to make good reproducible pandas examples
  • Include the code used to transform the data: Minimal, Reproducible Example
• Another option is, produce the predictions without taking np.log