to_datetime() for parsing dates in Pandas - Mini Project: Build & Apply

Parsing Dates with pandas to_datetime()

📖 Scenario: You work in a small business that tracks customer orders. The order dates are stored as text, but you want to analyze them as real dates to find trends.

🎯 Goal: You will convert a list of order date strings into proper date objects using pandas to_datetime(). This will help you work with dates easily.

📋 What You'll Learn

Create a pandas DataFrame with order IDs and order date strings

Create a variable for the date column name

Use pandas to_datetime() to convert the date strings to datetime objects

Print the DataFrame to see the converted dates

💡 Why This Matters

🌍 Real World

Businesses often receive dates as text from forms or files. Converting them to datetime lets you analyze sales trends, delays, or seasonal patterns.

💼 Career

Data analysts and scientists frequently clean and prepare date data before analysis. Knowing how to parse dates is a key skill in data cleaning.

1

Create the orders DataFrame

Create a pandas DataFrame called orders with two columns: 'order_id' containing [101, 102, 103] and 'order_date' containing ['2024-01-15', '2024-02-20', '2024-03-10'].

Pandas

# Create the orders DataFrame with order_id and order_date columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the two columns and their values.

2

Set the date column name

Create a variable called date_column and set it to the string 'order_date'.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'order_id': [101, 102, 103],
    'order_date': ['2024-01-15', '2024-02-20', '2024-03-10']
})

# Create the variable date_column with value 'order_date'
# Your code here

Hint

Just assign the string 'order_date' to the variable date_column.

3

Convert the order_date strings to datetime

Use pandas to_datetime() to convert the orders[date_column] column to datetime objects. Assign the result back to orders[date_column].

Pandas

import pandas as pd

orders = pd.DataFrame({
    'order_id': [101, 102, 103],
    'order_date': ['2024-01-15', '2024-02-20', '2024-03-10']
})

date_column = 'order_date'

# Convert orders[date_column] to datetime using pd.to_datetime()
# Your code here

Hint

Use pd.to_datetime() on the column and assign it back to the same column.

4

Print the DataFrame with parsed dates

Print the orders DataFrame to see the order_date column as datetime objects.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'order_id': [101, 102, 103],
    'order_date': ['2024-01-15', '2024-02-20', '2024-03-10']
})

date_column = 'order_date'

orders[date_column] = pd.to_datetime(orders[date_column])

# Print the orders DataFrame
# Your code here

Hint

Use print(orders) to display the DataFrame.

Practice

1. What does the pandas.to_datetime() function do?

easy

A. It filters rows based on date values.

B. It converts text or numbers into date objects.

C. It sorts data in ascending order.

D. It converts numbers into strings.

Solution

1. Step 1: Understand the purpose of to_datetime()

   This function is used to change text or numbers that represent dates into actual date objects that pandas can understand.

2. Step 2: Compare options with the function's purpose

   Only It converts text or numbers into date objects. correctly describes this conversion. Other options describe unrelated operations.

3. Final Answer:

   It converts text or numbers into date objects. -> Option B

4. Quick Check:

   to_datetime() = converts to dates [OK]

Hint: to_datetime() changes strings/numbers into dates [OK]

Common Mistakes:

• Thinking it sorts data
• Confusing it with string conversion
• Assuming it filters data

2. Which of the following is the correct syntax to convert a column named 'date_str' in DataFrame df to datetime using pandas?

easy

A. df['date_str'] = pd.to_datetime(df['date_str'])

B. df.to_datetime('date_str')

C. pd.to_datetime(df.date_str, inplace=True)

D. df['date_str'].to_datetime()

Solution

1. Step 1: Identify the correct function call

   The function pd.to_datetime() is called with the column as argument to convert it.

2. Step 2: Check assignment to the DataFrame column

   The converted datetime values must be assigned back to the column to update it, which df['date_str'] = pd.to_datetime(df['date_str']) does correctly.

3. Final Answer:

   df['date_str'] = pd.to_datetime(df['date_str']) -> Option A

4. Quick Check:

   Use pd.to_datetime() and assign back [OK]

Hint: Use pd.to_datetime(column) and assign back [OK]

Common Mistakes:

• Calling to_datetime as a DataFrame method
• Using inplace parameter which does not exist
• Not assigning the result back to the column

3. What will be the output of this code?

import pandas as pd
s = pd.Series(['01-12-2023', '02-11-2023'])
dates = pd.to_datetime(s, dayfirst=True)
print(dates.dt.month.tolist())

medium

A. [2, 1]

B. [1, 2]

C. [11, 12]

D. [12, 11]

Solution

1. Step 1: Understand the input strings and dayfirst=True

   The strings are '01-12-2023' and '02-11-2023'. With dayfirst=True, '01-12-2023' means 1st December 2023 (month=12), and '02-11-2023' means 2nd November 2023 (month=11).

2. Step 2: Extract the month values

   Using dates.dt.month.tolist() extracts the month numbers as a list: [12, 11].

3. Final Answer:

   [12, 11] -> Option D

4. Quick Check:

   dayfirst=True swaps day and month [OK]

Hint: dayfirst=True means day is first in date string [OK]

Common Mistakes:

• Ignoring dayfirst and reading month first
• Mixing up month numbers
• Confusing string format with default parsing

4. Identify the error in this code snippet:

import pandas as pd
s = pd.Series(['2023/12/01', '2023/11/02'])
dates = pd.to_datetime(s, format='%Y-%m-%d')
print(dates)

medium

A. The to_datetime function requires dayfirst=True here.

B. The Series should be converted using astype('datetime64').

C. The format string does not match the date strings.

D. The date strings are missing time information.

Solution

1. Step 1: Compare date strings with format string

   The date strings use slashes '/' like '2023/12/01', but the format string expects dashes '-' as in '%Y-%m-%d'. This mismatch causes a parsing error.

2. Step 2: Understand the impact of format mismatch

   Because the format does not match the input, pandas will raise an error or fail to parse correctly.

3. Final Answer:

   The format string does not match the date strings. -> Option C

4. Quick Check:

   Format must match date string exactly [OK]

Hint: Match format string exactly to date string separators [OK]

Common Mistakes:

• Using wrong separators in format
• Assuming astype converts strings to dates
• Adding dayfirst when not needed

5. You have a DataFrame df with a column 'date_str' containing dates in mixed formats like '2023-12-01' and '01/12/2023'. How can you convert this column to datetime correctly?

hard

A. Use pd.to_datetime(df['date_str'], dayfirst=True, errors='coerce') to parse with day first and convert errors to NaT.

B. Use pd.to_datetime(df['date_str'], format='%Y-%m-%d') to force one format.

C. Use df['date_str'].astype('datetime64') to convert automatically.

D. Use pd.to_datetime(df['date_str'], exact=true) to parse exactly.

Solution

1. Step 1: Understand mixed date formats

   Since the column has mixed formats (ISO and day/month/year), specifying a single format will fail for some entries.

2. Step 2: Use flexible parsing with dayfirst=True and handle errors

   Using dayfirst=True helps interpret dates like '01/12/2023' correctly. Setting errors='coerce' converts unparseable dates to NaT instead of error.

3. Step 3: Avoid forcing a single format or using astype

   Forcing a format or using astype will cause errors or wrong parsing with mixed formats.

4. Final Answer:

   Use pd.to_datetime(df['date_str'], dayfirst=True, errors='coerce') -> Option A

5. Quick Check:

   Flexible parsing with dayfirst and error handling [OK]

Hint: Use dayfirst=True and errors='coerce' for mixed formats [OK]

Common Mistakes:

• Forcing a single format on mixed data
• Using astype for string to datetime
• Ignoring errors causing exceptions