Extracting year, month, day in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

How do you extract the year from a pandas datetime column?

Use the .dt.year attribute on the datetime column. For example, df['date'].dt.year gives the year part of each date.

beginner

What pandas attribute extracts the month from a datetime column?

The .dt.month attribute extracts the month as an integer (1 to 12) from a datetime column.

beginner

How can you get the day of the month from a pandas datetime column?

Use .dt.day on the datetime column to get the day number (1 to 31) for each date.

intermediate

What type should a pandas column be to use .dt.year, .dt.month, or .dt.day?

The column must be of datetime type, like datetime64[ns]. If not, convert it using pd.to_datetime().

beginner

Write a pandas code snippet to create separate columns for year, month, and day from a 'date' column.

df['year'] = df['date'].dt.year df['month'] = df['date'].dt.month df['day'] = df['date'].dt.day

Which pandas attribute extracts the month from a datetime column?

A .dt.year

B .dt.month

C .dt.day

D .dt.hour

The .dt.month attribute extracts the month number from a datetime column.

If a pandas column is not datetime type, what should you do before extracting year, month, or day?

A Convert it using <code>pd.to_datetime()</code>

B Use <code>.astype(int)</code>

C Use <code>.dt.year</code> directly

D Drop the column

You must convert the column to datetime type using pd.to_datetime() before using .dt attributes.

What does df['date'].dt.day return?

A The month number

B The day of the week as a string

C The day of the month as an integer

D The year

.dt.day returns the day number (1 to 31) from each date.

Which data type is required for a pandas column to use .dt.year?

A datetime64[ns]

B int64

C object

D float64

The column must be datetime type like datetime64[ns] to use .dt.year.

How do you create a new column 'year' from a datetime column 'date' in pandas?

A df['year'] = df['date'].astype(int)

B df['year'] = df['date'].year()

C df['year'] = year(df['date'])

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

Use df['date'].dt.year to extract the year and assign it to a new column.

Explain how to extract year, month, and day from a pandas datetime column and why the column type matters.

Think about the .dt accessor and data types.

Write a short pandas code snippet to add three new columns for year, month, and day from a 'date' column.

Use the .dt accessor on the 'date' column.

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