shift() for lagging data in Pandas - Step-by-Step Execution

Concept Flow - shift() for lagging data

Start with DataFrame

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Call shift() method

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Move data down by n rows

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Fill top rows with NaN

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Return shifted DataFrame

The shift() method moves data down by a specified number of rows, creating lagged data with NaN at the top.

Execution Sample

Pandas

import pandas as pd

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

This code creates a lagged column 'lag1' by shifting the 'value' column down by 1 row.

Execution Table

Step	DataFrame 'value'	Action	Resulting 'lag1' Column
Initial	[10, 20, 30, 40, 50]	Original data	[NaN, NaN, NaN, NaN, NaN] (not created yet)
1	[10, 20, 30, 40, 50]	Shift 'value' down by 1	[NaN, 10, 20, 30, 40]
2	[10, 20, 30, 40, 50]	Assign shifted data to 'lag1'	Column 'lag1' added with shifted values
End	[10, 20, 30, 40, 50]	Final DataFrame	{'value': [10,20,30,40,50], 'lag1': [NaN,10,20,30,40]}

💡 Shift completed by moving data down 1 row; top row filled with NaN.

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']	Not created	[NaN, 10, 20, 30, 40]	[NaN, 10, 20, 30, 40]

Key Moments - 2 Insights

Why does the first value in the lagged column become NaN?

Because shift(1) moves all values down by one row, the top row has no previous value to fill it, so pandas fills it with NaN as shown in execution_table step 1.

Does shift() change the original 'value' column?

No, shift() returns a new Series with shifted data. The original 'value' column remains unchanged, as seen in variable_tracker where 'df["value"]' stays the same.

Visual Quiz - 3 Questions

Look at the execution_table at step 1, what is the value of 'lag1' in the second row?

A 10

B 20

C NaN

D 30

💡 Hint

Check the 'Resulting lag1 Column' at step 1 in the execution_table.

At which step is the 'lag1' column added to the DataFrame?

A Step 1

B Step 2

C Initial

D End

💡 Hint

Look at the 'Action' column in execution_table rows to see when assignment happens.

If we change shift(1) to shift(2), what will be the value of 'lag1' in the third row?

A NaN

B 20

C 10

D 30

💡 Hint

Think about shifting data down by 2 rows; check variable_tracker for shift(1) and imagine shifting further.

Concept Snapshot

shift(n) moves data down by n rows
Top n rows become NaN (no data above)
Used to create lagged features in time series
Original data stays unchanged
Returns a new Series or DataFrame column

Full Transcript

The shift() method in pandas moves data down by a specified number of rows, creating lagged data. For example, shifting by 1 moves all values down one row, and the top row becomes NaN because there is no previous data. This is useful for time series analysis to compare current values with past values. The original data column remains unchanged, and the shifted data is returned as a new column or Series. In the example, the 'value' column is shifted by 1 to create a 'lag1' column with the previous row's values, and the first row of 'lag1' is NaN.