Extracting year, month, day in Pandas - Time & Space Complexity

Time Complexity: Extracting year, month, day

O(n)

Understanding Time Complexity

We want to know how the time to extract year, month, and day from dates changes as we have more data.

How does the work grow when the list of dates gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

dates = pd.Series(pd.date_range(start='2020-01-01', periods=1000))
years = dates.dt.year
months = dates.dt.month
days = dates.dt.day

This code creates 1000 dates and extracts the year, month, and day from each date.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Extracting year, month, and day from each date in the series.
• How many times: Once for each date in the list (n times).

How Execution Grows With Input

As the number of dates grows, the work to extract parts grows in the same way.

Input Size (n)	Approx. Operations
10	About 10 extractions
100	About 100 extractions
1000	About 1000 extractions

Pattern observation: The work grows directly with the number of dates.

Final Time Complexity

Time Complexity: O(n)

This means the time to extract year, month, and day grows linearly as the number of dates increases.

Common Mistake

[X] Wrong: "Extracting year, month, and day is constant time no matter how many dates there are."

[OK] Correct: Each date needs to be processed separately, so more dates mean more work.

Interview Connect

Understanding how operations grow with data size helps you explain your code choices clearly and confidently.

Self-Check

"What if we extracted only the year instead of year, month, and day? How would the time complexity change?"

Practice

1. What does the .dt.year attribute do when applied to a pandas datetime column?

easy

A. Extracts the year part from each date in the column

B. Extracts the month part from each date in the column

C. Extracts the day part from each date in the column

D. Converts the column to string format

Solution

1. Step 1: Understand pandas datetime attributes

   The .dt accessor allows extraction of date parts like year, month, and day from datetime columns.

2. Step 2: Identify the function of .dt.year

   The .dt.year extracts the year component from each datetime value in the column.

3. Final Answer:

   Extracts the year part from each date in the column -> Option A

4. Quick Check:

   .dt.year = year extraction [OK]

Hint: Remember: year, month, day are .dt.year, .dt.month, .dt.day [OK]

Common Mistakes:

• Confusing .dt.year with .dt.month or .dt.day
• Trying to use .year directly without .dt
• Not converting column to datetime before extraction

2. Which of the following is the correct syntax to extract the month from a pandas datetime column named date_col?

easy

A. date_col.dt.month

B. date_col.month

C. date_col['month']

D. date_col.get_month()

Solution

1. Step 1: Recall pandas datetime accessor usage

   To extract parts like month, you must use the .dt accessor on a datetime column.

2. Step 2: Identify correct syntax for month extraction

   The correct syntax is date_col.dt.month. Other options either miss .dt or use invalid methods.

3. Final Answer:

   date_col.dt.month -> Option A

4. Quick Check:

   Use .dt before date parts [OK]

Hint: Always use .dt before year/month/day on datetime columns [OK]

Common Mistakes:

• Using .month directly without .dt
• Trying to access month as a dictionary key
• Using non-existent methods like get_month()

3. Given the following code, what will be the output?

import pandas as pd
df = pd.DataFrame({
  'date': pd.to_datetime(['2023-01-15', '2022-12-31', '2024-07-04'])
})
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
df[['year', 'month', 'day']].values.tolist()

medium

A. [[15, 1, 2023], [31, 12, 2022], [4, 7, 2024]]

B. [['2023', '01', '15'], ['2022', '12', '31'], ['2024', '07', '04']]

C. [[2023, 1, 15], [2022, 12, 31], [2024, 7, 4]]

D. Error: Cannot extract year, month, day without conversion

Solution

1. Step 1: Convert strings to datetime

   The pd.to_datetime converts string dates to datetime objects correctly.

2. Step 2: Extract year, month, day

   Using .dt.year, .dt.month, and .dt.day extracts integer parts of the date.

3. Final Answer:

   [[2023, 1, 15], [2022, 12, 31], [2024, 7, 4]] -> Option C

4. Quick Check:

   Extracted parts are integers, not strings [OK]

Hint: Extracted year/month/day are integers, not strings [OK]

Common Mistakes:

• Expecting strings instead of integers
• Mixing order of day, month, year
• Forgetting to convert to datetime first

4. What is wrong with this code snippet?

df['year'] = df['date'].year

Assuming df['date'] is a datetime column.

medium

A. You need to use df.year() instead

B. The column must be converted to string first

C. The code will work fine as is

D. It should be df['date'].dt.year to access year

Solution

1. Step 1: Understand pandas datetime access

   Datetime columns require the .dt accessor to extract parts like year.

2. Step 2: Identify missing .dt accessor

   The code misses .dt, so df['date'].year will raise an error or not work as expected.

3. Final Answer:

   It should be df['date'].dt.year to access year -> Option D

4. Quick Check:

   Use .dt before year/month/day [OK]

Hint: Always use .dt before year/month/day on datetime columns [OK]

Common Mistakes:

• Omitting .dt accessor
• Trying to call year() as a method
• Converting datetime to string unnecessarily

5. You have a DataFrame df with a column 'timestamp' containing mixed string dates and some missing values (NaN). You want to create three new columns: 'year', 'month', and 'day'. Which approach correctly handles conversion and extraction without errors?

hard

A. df['year'] = df['timestamp'].dt.year; df['month'] = df['timestamp'].dt.month; df['day'] = df['timestamp'].dt.day

B. df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce'); df['year'] = df['timestamp'].dt.year; df['month'] = df['timestamp'].dt.month; df['day'] = df['timestamp'].dt.day

C. df['year'] = df['timestamp'].str[:4]; df['month'] = df['timestamp'].str[5:7]; df['day'] = df['timestamp'].str[8:10]

D. df['timestamp'] = df['timestamp'].astype(str); df['year'] = df['timestamp'].dt.year

Solution

1. Step 1: Convert mixed strings and NaN to datetime safely

   Using pd.to_datetime(..., errors='coerce') converts valid strings to datetime and invalid/missing to NaT without error.

2. Step 2: Extract year, month, day using .dt accessor

   After conversion, use .dt.year, .dt.month, and .dt.day to get parts safely, even with NaT values.

3. Final Answer:

   df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce'); df['year'] = df['timestamp'].dt.year; df['month'] = df['timestamp'].dt.month; df['day'] = df['timestamp'].dt.day -> Option B

4. Quick Check:

   Convert with errors='coerce' then extract parts [OK]

Hint: Use pd.to_datetime with errors='coerce' before extracting [OK]

Common Mistakes:

• Not converting strings to datetime first
• Extracting parts from string columns directly
• Ignoring missing values causing errors