Time series analysis patterns in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of resampling time series data

What is the output of this code that resamples daily data to monthly sums?

Pandas

import pandas as pd

dates = pd.date_range('2023-01-01', periods=6, freq='D')
data = pd.Series([1, 2, 3, 4, 5, 6], index=dates)
result = data.resample('M').sum()
print(result)

A

2023-01-01    21
Freq: M, dtype: int64

B

2023-01-31    6
Freq: M, dtype: int64

C

2023-01-31    21
Freq: M, dtype: int64

D

2023-01-06    21
Freq: D, dtype: int64

💡 Hint

Monthly resampling sums all daily values in January.

The code sums all values from Jan 1 to Jan 6. Since the frequency is monthly ('M'), the result is indexed by the last day of January with the sum 1+2+3+4+5+6=21.

❓ data_output

intermediate

Number of missing values after forward fill

Given this time series with missing values, how many missing values remain after forward filling?

Pandas

import pandas as pd
import numpy as np

dates = pd.date_range('2023-01-01', periods=5, freq='D')
data = pd.Series([np.nan, 2, np.nan, 4, np.nan], index=dates)
filled = data.ffill()
missing_count = filled.isna().sum()
print(missing_count)

A 1

B 0

C 2

D 3

💡 Hint

Forward fill replaces NaN with previous non-NaN value. The first NaN has no previous value.

The first value is NaN and has no previous value to fill from, so it remains NaN. The other NaNs get filled. So 1 missing value remains.

❓ visualization

advanced

Identifying seasonality in time series plot

Which plot best shows clear seasonality in monthly sales data?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(0)
dates = pd.date_range('2022-01-01', periods=24, freq='M')
sales = 10 + 5 * np.sin(2 * np.pi * dates.month / 12) + np.random.normal(0, 1, 24)
data = pd.Series(sales, index=dates)
plt.plot(data.index, data.values)
plt.title('Monthly Sales with Seasonality')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.show()

A A histogram of sales values

B A scatter plot with random points and no pattern

C A bar chart with steadily increasing values

D A line plot showing repeating peaks every 12 months

💡 Hint

Seasonality means repeating patterns at regular intervals.

The line plot shows a smooth wave pattern repeating every 12 months, indicating seasonality.

🧠 Conceptual

advanced

Effect of differencing on time series data

What is the main effect of applying first-order differencing to a time series?

A It increases the variance of the series

B It removes trend and stabilizes the mean

C It smooths the series by averaging values

D It adds seasonality to the series

💡 Hint

Think about what subtracting consecutive values does to a trending series.

First-order differencing subtracts each value from the previous one, removing linear trends and making the mean more stable.

🔧 Debug

expert

Error raised by incorrect time series indexing

What error does this code raise when trying to access a time series value?

Pandas

import pandas as pd

dates = pd.date_range('2023-01-01', periods=3, freq='D')
data = pd.Series([10, 20, 30], index=dates)
value = data['2023-01-04']
print(value)

A KeyError

B IndexError

C TypeError

D ValueError

💡 Hint

Check if the date key exists in the index.

The date '2023-01-04' is not in the index, so accessing it raises a KeyError.