Extracting date components (year, month, day) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Extracting date components (year, month, day)

Start with date column

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Extract year from date

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Extract month from date

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Extract day from date

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Store or display extracted components

We start with a date column, then extract year, month, and day one by one, and finally store or show these parts.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2024-06-01', '2023-12-25'])})
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
print(df)

This code creates a table with dates and adds columns for year, month, and day extracted from those dates.

Execution Table

Step	Action	Input	Output/Result
1	Create DataFrame with date column	['2024-06-01', '2023-12-25']	DataFrame with 'date' column of datetime type
2	Extract year from 'date'	2024-06-01, 2023-12-25	[2024, 2023] stored in 'year' column
3	Extract month from 'date'	2024-06-01, 2023-12-25	[6, 12] stored in 'month' column
4	Extract day from 'date'	2024-06-01, 2023-12-25	[1, 25] stored in 'day' column
5	Print DataFrame	DataFrame with 'date', 'year', 'month', 'day'	Shows table with all columns and values

💡 All date components extracted and stored; DataFrame ready for use.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
df	None	date column with ['2024-06-01', '2023-12-25']	Added 'year' column [2024, 2023]	Added 'month' column [6, 12]	Added 'day' column [1, 25]	DataFrame with 'date', 'year', 'month', 'day' columns

Key Moments - 2 Insights

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

The .dt accessor tells pandas we want to work with datetime properties of the column. Without it, pandas won't know to extract date parts. See execution_table step 2-4.

What happens if the 'date' column is not in datetime format?

Extraction will fail or give errors because .dt only works on datetime types. You must convert strings to datetime first, as done in step 1.

Visual Quiz - 3 Questions

Look at the execution table at step 3, what values are stored in the 'month' column?

A [2024, 2023]

B [6, 12]

C [1, 25]

D ['2024-06-01', '2023-12-25']

💡 Hint

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

At which step does the DataFrame get the 'day' column added?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for the action 'Extract day from date' in the execution_table.

If the 'date' column was not converted to datetime, what would happen when extracting year?

A Extraction gives an error or wrong result

B Year column will be all zeros

C Extraction works normally

D Month column will be extracted instead

💡 Hint

Refer to key_moments about the importance of datetime type for .dt accessor.

Concept Snapshot

Extract year, month, day from a datetime column using pandas:
Use df['date'].dt.year, .dt.month, .dt.day
Make sure 'date' is datetime type first
Adds new columns with extracted parts
Useful for time-based analysis

Full Transcript

We start with a DataFrame containing a date column. We convert the date strings to datetime type so pandas can understand them. Then, using the .dt accessor, we extract the year, month, and day parts from each date. These parts are stored in new columns in the DataFrame. Finally, we print the DataFrame to see all columns together. This process helps us break down dates into useful pieces for analysis.