Extracting year, month, day in Pandas - Step-by-Step Execution

Concept Flow - Extracting year, month, day

Start with datetime column

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Use .dt accessor

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Extract year/month/day

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Create new columns with extracted values

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Use extracted data for analysis

We start with a datetime column, use pandas .dt accessor to get year, month, and day, then store these in new columns for easy use.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2024-06-20'])})
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
print(df)

This code creates a DataFrame with dates and extracts year, month, and day into new columns.

Execution Table

Step	Action	Code	Result
1	Create DataFrame with date column	df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2024-06-20'])})	df with 'date' column: [2023-01-15, 2024-06-20]
2	Extract year from 'date'	df['year'] = df['date'].dt.year	year column: [2023, 2024]
3	Extract month from 'date'	df['month'] = df['date'].dt.month	month column: [1, 6]
4	Extract day from 'date'	df['day'] = df['date'].dt.day	day column: [15, 20]
5	Print final DataFrame	print(df)	DataFrame: date year month day 0 2023-01-15 2023 1 15 1 2024-06-20 2024 6 20

💡 All date parts extracted and stored; execution ends after printing DataFrame.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
df	empty	date column with 2 dates	year column added	month column added	day column added	final DataFrame with date, year, month, day columns

Key Moments - 2 Insights

Why do we use .dt before year, month, or day?

The .dt accessor tells pandas we want to work with datetime properties of the column. Without .dt, pandas won't know to extract parts like year or month. See execution_table step 2.

Can we extract year, month, day directly without converting to datetime?

No, the column must be datetime type. pd.to_datetime converts strings to datetime. Without this, .dt.year etc. will cause errors. See execution_table step 1.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of 'month' after step 3?

A [1, 6]

B [15, 20]

C [2023, 2024]

D ['2023-01-15', '2024-06-20']

💡 Hint

Check the 'Result' column for step 3 in the execution_table.

At which step is the 'day' column added to the DataFrame?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for the action mentioning 'Extract day' in the execution_table.

If the 'date' column was not converted to datetime, what would happen when running step 2?

A It would extract year correctly

B It would raise an error

C It would extract month instead

D It would create empty columns

💡 Hint

Refer to key_moments about the need for datetime type before using .dt accessor.

Concept Snapshot

Extract year, month, day from datetime column in pandas:
- Ensure column is datetime type (use pd.to_datetime)
- Use .dt.year, .dt.month, .dt.day to get parts
- Assign to new columns for easy access
- Useful for time-based analysis and filtering

Full Transcript

We start with a pandas DataFrame containing a date column. First, we convert the date strings to datetime type using pd.to_datetime. Then, using the .dt accessor, we extract the year, month, and day parts from the datetime column. Each extracted part is stored in a new column in the DataFrame. Finally, we print the DataFrame showing the original date and the extracted year, month, and day columns. This process helps us work easily with parts of dates for analysis.