Shift and lag operations in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of shift operation on a pandas Series

What is the output of the following code snippet?

Data Analysis Python

import pandas as pd
s = pd.Series([10, 20, 30, 40, 50])
result = s.shift(2)
print(result)

A

0    10
1    20
2    30
3    40
4    50
dtype: int64

B

0     NaN
1     NaN
2    10.0
3    20.0
4    30.0
dtype: float64

C

0    30.0
1    40.0
2    50.0
3     NaN
4     NaN
dtype: float64

D

0    NaN
1    10.0
2    20.0
3    30.0
4    40.0
dtype: float64

💡 Hint

Shift moves values down by the number specified, filling new spots with NaN.

The shift(2) moves all values down by 2 places. The first two positions become NaN, and the rest are the original values shifted down.

❓ data_output

intermediate

Result of lag operation with fill value

Given the DataFrame below, what is the output of the lag operation with fill value 0?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'A': [5, 10, 15, 20]})
df['lag_A'] = df['A'].shift(1, fill_value=0)
print(df)

A

    A  lag_A
0   5      0
1  10      5
2  15     10
3  20     15

B

    A  lag_A
0   5    NaN
1  10      5
2  15     10
3  20     15

C

    A  lag_A
0   5      0
1  10      0
2  15      5
3  20     10

D

    A  lag_A
0   5      5
1  10     10
2  15     15
3  20     20

💡 Hint

The fill_value replaces NaN created by the shift.

The shift(1) moves values down by 1. The first row gets the fill_value 0 instead of NaN.

❓ visualization

advanced

Visualizing shift effect on time series data

Which plot correctly shows the original and shifted time series data?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series([3, 6, 9, 12, 15])
s_shifted = s.shift(1)
plt.plot(s, label='Original')
plt.plot(s_shifted, label='Shifted')
plt.legend()
plt.show()

A Line plot with original series starting at 3 and shifted series starting with NaN at index 0

B Bar plot with original and shifted series overlapping exactly

C Scatter plot with shifted series values all zero

D Line plot with shifted series values ahead of original by one index

💡 Hint

Shifted series moves values down, so first value is missing (NaN).

The line plot shows original values starting at index 0, shifted values start with NaN at index 0 and values from original shifted down by one index.

🔧 Debug

advanced

Identify error in shift usage

What error will this code raise and why? import pandas as pd s = pd.Series([1, 2, 3]) result = s.shift('2')

Data Analysis Python

import pandas as pd
s = pd.Series([1, 2, 3])
result = s.shift('2')

A KeyError because '2' is not a valid index

B ValueError because string '2' is not a valid shift value

C No error, shift converts string '2' to integer 2 automatically

D TypeError because shift expects an integer or timedelta, not a string

💡 Hint

Check the type of argument shift accepts.

shift expects an integer or timedelta. Passing a string causes a TypeError.

🧠 Conceptual

expert

Understanding difference between shift and lag in time series

Which statement best describes the difference between shift and lag operations in time series data?

A Shift and lag are identical operations with different names in pandas

B Shift moves data up or down by a specified number of periods, while lag always moves data backward by one period

C Shift moves data by a specified number of periods and can move forward or backward; lag specifically refers to moving data backward by one or more periods

D Lag moves data forward in time, shift moves data backward

💡 Hint

Think about direction and flexibility of shift vs lag.

Shift is a general operation that can move data forward or backward by any number of periods. Lag is a specific case of shift moving data backward (to past periods).