Shift and lag operations in Data Analysis Python - Step-by-Step Execution

Concept Flow - Shift and lag operations

Start with DataFrame

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Choose column to shift

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Apply shift(n)

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Values move down by n rows

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Top n rows become NaN

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Result: shifted column

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Use shifted column for lag analysis

Shift moves data down by n rows, creating lagged versions of columns for time-based comparisons.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'value': [10, 20, 30, 40, 50]})
df['lag1'] = df['value'].shift(1)
print(df)

This code creates a lagged column by shifting the 'value' column down by 1 row.

Execution Table

Step	DataFrame 'value' column	Shift(1) applied	Resulting 'lag1' column
Initial	[10, 20, 30, 40, 50]	No	[NaN, NaN, NaN, NaN, NaN]
Apply shift(1)	[10, 20, 30, 40, 50]	Yes	[NaN, 10, 20, 30, 40]
Print df	[10, 20, 30, 40, 50]	Yes	[NaN, 10, 20, 30, 40]

💡 Shift completed: values moved down by 1, top row is NaN because no previous value exists.

Variable Tracker

Variable	Start	After shift(1)	Final
df['value']	[10, 20, 30, 40, 50]	[10, 20, 30, 40, 50]	[10, 20, 30, 40, 50]
df['lag1']	[NaN, NaN, NaN, NaN, NaN]	[NaN, 10, 20, 30, 40]	[NaN, 10, 20, 30, 40]

Key Moments - 2 Insights

Why does the first row of the lagged column become NaN after shift(1)?

Because shift(1) moves values down by one row, the first row has no previous value to fill it, so it becomes NaN as shown in execution_table step 'Apply shift(1)'.

What happens if we use shift(-1) instead of shift(1)?

Shift(-1) moves values up by one row, so the last row becomes NaN because it has no next value. This is the opposite direction of shift(1).

Visual Quiz - 3 Questions

Look at the execution_table at step 'Apply shift(1)'. What is the value of df['lag1'] in the third row?

A 10

B 30

C 20

D NaN

💡 Hint

Check the 'Resulting lag1 column' at step 'Apply shift(1)' in execution_table.

At which step does the 'lag1' column first get its shifted values?

A Initial

B Apply shift(1)

C Print df

D Final

💡 Hint

Look at the 'Shift(1) applied' column in execution_table to see when shift happens.

If we change shift(1) to shift(2), what will happen to the first two rows of 'lag1'?

A They will be NaN

B They will contain the first two values of 'value' column

C They will contain the last two values of 'value' column

D They will be zeros

💡 Hint

Recall that shifting by n moves values down by n rows, so top n rows become NaN.

Concept Snapshot

Shift and lag operations move data up or down in a column.
Use df['col'].shift(n) to move data down by n rows (lag).
Top n rows become NaN because no data exists above.
Useful for comparing current and past values in time series.
Negative n shifts data up (lead).
Always check for NaNs after shifting.

Full Transcript

Shift and lag operations in data science move data in a column up or down by a number of rows. Using pandas in Python, the shift() function moves values down by n rows, creating a lag effect. For example, shift(1) moves all values down by one row, making the first row NaN because it has no previous value. This is useful to compare current data with past data in time series analysis. Negative shifts move data up, creating lead values. After shifting, the new rows created at the edges are filled with NaN to indicate missing data. This visual trace showed step-by-step how the shift function changes the data frame and how variables update.