Extracting year, month, day in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the year from the 'date' column in the DataFrame.

Pandas

df['year'] = df['date'].dt.[1]

Drag options to blanks, or click blank then click option'

Ayear

Bmonth

Cday

Dhour

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'month' or 'day' instead of 'year'.

Forgetting to use the dt accessor.

The dt.year attribute extracts the year part from datetime values in a pandas Series.

2fill in blank

medium

Complete the code to extract the month from the 'date' column in the DataFrame.

Pandas

df['month'] = df['date'].dt.[1]

Drag options to blanks, or click blank then click option'

Aminute

Bday

Cmonth

Dyear

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'day' or 'year' instead of 'month'.

Forgetting to use the dt accessor.

The dt.month attribute extracts the month part from datetime values in a pandas Series.

3fill in blank

hard

Fix the error in the code to extract the day from the 'date' column in the DataFrame.

Pandas

df['day'] = df['date'].[1]

Drag options to blanks, or click blank then click option'

Aday

Bdt.day()

Cday()

Ddt.day

Attempts:

3 left

💡 Hint

Common Mistakes

Using df['date'].day without dt accessor.

Using parentheses like dt.day() which is invalid.

To extract the day, you must use the dt accessor followed by day without parentheses: dt.day.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 letters.

Pandas

lengths = {word: [1] for word in words if [2] > 3}

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Checking the word instead of its length in the condition.

The dictionary comprehension uses len(word) to get the length and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 3 letters.

Pandas

result = { [1]: [2] for word in words if [3] > 3 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

Using the word instead of its length as value.

Not filtering words by length.

The dictionary comprehension uses word.upper() as keys, len(word) as values, and filters words longer than 3 letters.

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