Cleaning Updated November 2026

pd.to_datetime

pd.to_datetime(df["col"]) converts strings or numbers into datetime64 values, after which the .dt accessor exposes parts such as .dt.year, .dt.month and .dt.day_name(). Pass format="%d/%m/%Y" for an explicit layout and errors="coerce" to turn unparseable values into NaT.

Syntax

pd.to_datetime(arg, format=None, errors="raise", dayfirst=False, utc=False, unit=None)

Part	What it is
`format`	strftime pattern such as "%Y-%m-%d" or "%d/%m/%Y %H:%M"; faster and unambiguous
`errors`	"raise" (default) or "coerce" to set invalid values to NaT
`dayfirst`	True to read 03/04/2024 as 3 April
`unit`	"s" or "ms" when converting Unix timestamps

Examples

import pandas as pd

df = pd.DataFrame({"order_date": ["2024-03-15", "2024-03-16", "2024-04-02"]})
df["order_date"] = pd.to_datetime(df["order_date"])
df["order_date"].dt.month.tolist()

Result: [3, 3, 4]

df["weekday"] = df["order_date"].dt.day_name()
df["ym"] = df["order_date"].dt.strftime("%Y-%m")
df

Result: order_date weekday ym 0 2024-03-15 Friday 2024-03 1 2024-03-16 Saturday 2024-03 2 2024-04-02 Tuesday 2024-04

pd.to_datetime(pd.Series(["15/03/2024", "bad"]), format="%d/%m/%Y", errors="coerce")

Result: 0 2024-03-15 1 NaT dtype: datetime64[ns]

Common mistakes

Day and month swapped: ambiguous dates like 03/04/2024 are read month-first; pass format or dayfirst=True.
AttributeError: Can only use .dt accessor with datetimelike values: the column is still text; run pd.to_datetime first.
ValueError on one bad row stops the whole conversion: use errors="coerce" and inspect the NaT rows afterwards.

Practice pd.to_datetime

These drills check your answer on live data, free, no account needed.

Dates with to_datetime and dtThe `joined` column holds dates as text. Return a DataFrame with the columns `user`, `month` (the month number) and `weekday` (the full day name, like Monday).

Questions

How do I extract the year and month into columns?

df["year"] = df["d"].dt.year and df["month"] = df["d"].dt.month. For a period label use df["d"].dt.to_period("M").

How do I compute the difference between two dates?

Subtracting two datetime columns gives a timedelta; (df["end"] - df["start"]).dt.days returns whole days.

How do I parse dates while reading a CSV?

pd.read_csv(path, parse_dates=["order_date"]) converts the column during loading.

resampleHow to resample a time series in pandas: monthly, weekly or daily buckets with sum or mean, upsample with ffill, use a date column with on, and new aliases.
astypeHow to change a column type in pandas with astype and pd.to_numeric: text to numbers, float to int, strings, categories, nullable Int64, bad values and NaN.
pd.read_csvHow to read a CSV file in pandas with pd.read_csv: separators, headers, index columns, parsing dates, selecting columns, dtypes and the errors people hit.
shift and diffHow to use shift and diff in pandas to get the previous or next row, compute the change and percent change between periods, and lag within groups.