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.

Practice

1. What does the shift() function do in pandas?

easy

A. It changes the data type of a column.

B. It moves data up or down to compare current and past values.

C. It removes missing values from the dataset.

D. It sorts the data in ascending order.

Solution

1. Step 1: Understand the purpose of shift()

   The shift() function moves data up or down in a column, creating a lag or lead effect.

2. Step 2: Compare with other options

   Sorting, removing missing values, or changing data types are different pandas functions, not shift().

3. Final Answer:

   It moves data up or down to compare current and past values. -> Option B

4. Quick Check:

   shift() moves data for lagging [OK]

Hint: Remember: shift() moves data to compare past values [OK]

Common Mistakes:

• Confusing shift() with sort()
• Thinking shift() removes missing data
• Assuming shift() changes data types

2. Which of the following is the correct syntax to shift a pandas Series named data by 2 rows downward?

easy

A. data.shift(2)

B. data.shift(-2)

C. data.shift(0)

D. data.shift()

Solution

1. Step 1: Understand shift() parameter

   The argument inside shift() is the number of periods to move. Positive values shift downward (lag), negative shift upward (lead).

2. Step 2: Apply to the question

   To shift downward by 2 rows, use data.shift(2). Negative or zero values do not match the requirement.

3. Final Answer:

   data.shift(2) -> Option A

4. Quick Check:

   Positive number shifts down = data.shift(2) [OK]

Hint: Positive number in shift() moves data down [OK]

Common Mistakes:

• Using negative number to shift downward
• Omitting the argument defaults to shift by 1
• Using zero which means no shift

3. Given the pandas Series:

import pandas as pd
data = pd.Series([10, 20, 30, 40, 50])
shifted = data.shift(1)

What is the value of shifted at index 2?

medium

A. NaN

B. 30.0

C. 20.0

D. 10.0

Solution

1. Step 1: Understand shift(1) effect on Series

   Shifting by 1 moves all values down by one index. The value at index 2 in shifted is the original value at index 1.

2. Step 2: Find original value at index 1

   Original data at index 1 is 20, so shifted at index 2 is 20.0 (float due to NaN presence).

3. Final Answer:

   20.0 -> Option C

4. Quick Check:

   shift(1) moves index 1 value to index 2 [OK]

Hint: Value at index i after shift(1) is original at i-1 [OK]

Common Mistakes:

• Confusing index positions after shift
• Expecting integer type instead of float
• Thinking shifted index 2 is original index 2

4. What is wrong with this code snippet?

import pandas as pd
data = pd.Series([5, 10, 15])
shifted = data.shift('1')

medium

A. shift() cannot be used on Series objects.

B. shift() requires a second argument for axis.

C. The data list must be a DataFrame, not a Series.

D. The argument to shift() should be an integer, not a string.

Solution

1. Step 1: Check shift() argument type

   The shift() function expects an integer or a timedelta, not a string.

2. Step 2: Identify error cause

   Passing a string like '1' causes a TypeError because pandas cannot interpret it as a number of periods.

3. Final Answer:

   The argument to shift() should be an integer, not a string. -> Option D

4. Quick Check:

   shift() needs integer periods, not string [OK]

Hint: Use integer, not string, inside shift() [OK]

Common Mistakes:

• Passing string instead of integer to shift()
• Thinking shift() only works on DataFrames
• Forgetting shift() default axis is 0

5. You have a DataFrame df with a column 'sales'. You want to create a new column 'sales_change' that shows the difference between current sales and sales one day before. Which code correctly achieves this?

hard

A. df['sales_change'] = df['sales'] - df['sales'].shift(1)

B. df['sales_change'] = df['sales'].shift(1) - df['sales']

C. df['sales_change'] = df['sales'].diff(-1)

D. df['sales_change'] = df['sales'].shift(-1) - df['sales']

Solution

1. Step 1: Understand the goal

   We want the change from previous day to current day, so current minus previous.

2. Step 2: Use shift(1) to get previous day sales

   df['sales'].shift(1) gives previous day's sales aligned with current row.

3. Step 3: Calculate difference

   Subtract previous sales from current sales: df['sales'] - df['sales'].shift(1).

4. Final Answer:

   df['sales_change'] = df['sales'] - df['sales'].shift(1) -> Option A

5. Quick Check:

   Current minus previous = sales_change [OK]

Hint: Subtract shifted column from current for change [OK]

Common Mistakes:

• Reversing subtraction order
• Using negative shift which looks ahead
• Using diff(-1) which is future difference