Resample to Daily or Monthly
Rolling time into bigger buckets.
Resample to Daily or Monthly is a free Data Science Academy lesson on CoddyKit — lesson 3 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.
Too Much Detail to See
Per-second or per-minute data is noisy. Often you really want totals per day or per month. Resampling rolls fine timestamps into bigger buckets. 📊
A Datetime Index First
Resample works on time. So step one is making the datetime column your index with set_index.
df = df.set_index("date")Meet resample
The resample method groups rows by a time rule, much like groupby but for dates. You then pick how to combine each bucket.
df.resample("D") # group by dayDaily Totals
Use the rule "D" for days, then sum. This collapses many timestamps into one clean value per day.
daily = df.resample("D")["sales"].sum()Monthly Summaries
Switch the rule to "ME" for month-end and you get one row per month. Same pattern, bigger bucket.
monthly = df.resample("ME")["sales"].sum()Rules Are Flexible
The frequency string drives everything: "W" for weeks, "h" for hours, "YE" for year-end. One letter changes your whole view.
df.resample("W")["sales"].sum() # weeklyChoose Your Aggregation
Sum is not the only option. Swap in mean, max, or count to answer different questions from the same buckets.
df.resample("D")["temp"].mean()Several Stats at Once
Pass a list to agg to get multiple summaries per bucket in a single tidy table.
df.resample("ME").agg(["sum", "mean"])Downsample vs Upsample
Going to a coarser bucket is downsampling. Going finer, like daily into hourly, is upsampling and creates empty slots to fill.
df.resample("h").asfreq() # upsampleFill the New Gaps
When you upsample, fresh rows arrive empty. Use ffill to carry the last known value forward into them.
df.resample("h").ffill()Smooth, Then Plot
Resampled series plot beautifully because the noise is gone. A monthly line tells a trend story a raw feed never could.
monthly.plot()Quick Check
You have minute-level readings but want one average per day.
Recap: Reshape Time
With a datetime index you used resample to roll data into days, weeks, or months, picked an aggregation, and handled upsampled gaps with ffill. ⏳
Frequently asked questions
Is the “Resample to Daily or Monthly” lesson free?
Yes — the full text of “Resample to Daily or Monthly” 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 “Resample to Daily or Monthly”?
Rolling time into bigger buckets. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Resample to Daily or Monthly” 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