shift/diff
shift moves values up or down and diff subtracts the previous value, both handy for change over time.
In this page:
Syntax
df["column"].shift(1)
df["column"].diff()shift/diff
shift(1) moves data down one row so each row can see the previous value. diff() is the difference from the prior row and pct_change() gives the percentage change. Negative shift values look forward.
Note:
pct_change is diff divided by the previous value.
Example: shift/diff
import pandas as pd
s = pd.Series([100, 110, 99, 120])
print(s.shift(1).tolist())
print(s.diff().tolist())
print(s.pct_change().round(3).tolist())
# Output:
# [nan, 100.0, 110.0, 99.0]
# [nan, 10.0, -11.0, 21.0]
# [nan, 0.1, -0.1, 0.212]Related Topics
Common Mistakes
- Forgetting the first row becomes NaN
- Shifting the wrong direction
- Using shift on unsorted data
Chapter Summary
- shift moves values
- diff gives change
- pct_change gives relative change
- The first row becomes NaN
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