Parse Strings Into Datetimes
to_datetime and format handling.
Parse Strings Into Datetimes is a free Data Science Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Data Science Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Dates Hide as Text
When you load a CSV, dates usually arrive as plain strings. They look right but pandas treats them as text, so date math just won't work yet. 📅
Enter to_datetime
The fix is one function: pd.to_datetime. It scans your text and turns each value into a real timestamp pandas can compute with.
import pandas as pd
ts = pd.to_datetime("2024-03-15")
print(ts)Convert a Whole Column
Pass a Series and you get back a column of timestamps. This is the everyday move right after loading a table with a date field.
df["date"] = pd.to_datetime(df["date"])Check the dtype
After converting, the column's dtype becomes datetime64. That single check tells you the parse actually worked and date math is now unlocked.
print(df["date"].dtype) # datetime64[ns]Many Formats, One Call
to_datetime is smart: it reads ISO dates, slashes, and even written months automatically. For tidy ISO strings you rarely need to tell it anything else.
pd.to_datetime(["2024-01-01", "Jan 2, 2024"])Spell Out the Format
When dates are ambiguous, give an explicit format string. It removes guessing and runs much faster on big columns.
pd.to_datetime("15/03/2024", format="%d/%m/%Y")Day-First Dates
Is 03/04 March or April? Set dayfirst=True so pandas reads the day before the month, matching most European date styles.
pd.to_datetime("03/04/2024", dayfirst=True)Bad Values Without Crashing
One messy cell can break a whole parse. Use errors="coerce" to turn unparseable values into NaT instead of raising an error.
pd.to_datetime("not-a-date", errors="coerce") # NaTMeet NaT
NaT is the datetime version of NaN: a missing timestamp. You can spot these failed parses with isna and decide how to handle them.
df["date"].isna().sum() # count failed parsesCombine Date and Time Parts
Have separate date and hour columns? Concatenate them into one string, then parse once. You end up with a single clean timestamp.
pd.to_datetime(df["day"] + " " + df["time"])Parse at Read Time
You can skip a step by parsing during import. Pass parse_dates to read_csv and the column arrives as datetimes already.
pd.read_csv("sales.csv", parse_dates=["date"])Quick Check
Your date column has a few junk values you want to keep as missing.
Recap: You Can Parse Dates
You turned text into real timestamps with pd.to_datetime, controlled formats, handled bad values with coerce, and even parsed on load. Date math is now within reach. 🎉
Frequently asked questions
Is the “Parse Strings Into Datetimes” lesson free?
Yes — the full text of “Parse Strings Into Datetimes” is free to read here on the web, and the Data Science Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Data Science Academy course, upgrade to CoddyKit PRO.
What will I learn in “Parse Strings Into Datetimes”?
to_datetime and format handling. You practise Data Science Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Data Science Academy?
No prior experience is required. Data Science Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Parse Strings Into Datetimes” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Data Science Academy lesson?
Yes. Every Data Science Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Parse Strings Into Datetimes
- Extract Year, Month, and Weekday
- Resample to Daily or Monthly
- Time Zones and Date Ranges