to_datetime() can parse a huge variety of common date string formats automatically, but explicitly passing format, like '%Y-%m-%d', both speeds up parsing significantly for large datasets and avoids ambiguity for formats that could be misread, like whether a date means day-first or month-first. The errors parameter controls what happens when a value can't be parsed: 'raise', the default, stops with an exception, 'coerce' replaces unparseable values with NaT, pandas' 'Not a Time' missing-value marker, and 'ignore' leaves them as their original, unconverted value.
1Understanding pd.to_datetime()
to_datetime() can parse a huge variety of common date string formats automatically, but explicitly passing format, like '%Y-%m-%d', both speeds up parsing significantly for large datasets and avoids ambiguity for formats that could be misread, like whether a date means day-first or month-first. The errors parameter controls what happens when a value can't be parsed: 'raise', the default, stops with an exception, 'coerce' replaces unparseable values with NaT, pandas' 'Not a Time' missing-value marker, and 'ignore' leaves them as their original, unconverted value.
Always pass an explicit format string to to_datetime() when you know it — it's both significantly faster for large datasets and removes any ambiguity about how to interpret an inherently ambiguous date string.
import pandas as pd
dates = pd.to_datetime(["2026-01-15", "2026-02-20"])
print(dates)2Practical Example
Here is a real-world application of pd.to_datetime() showing how it is used in production Pandas code.
import pandas as pd
df = pd.DataFrame({"date": ["2026-01-15", "not a date", "2026-03-01"]})
df["date"] = pd.to_datetime(df["date"], errors="coerce")
print(df)3Best Practices
Follow these guidelines when working with pd.to_datetime():
1. Pass format explicitly whenever you know the exact date format, for both speed and to avoid ambiguous parsing
2. Use errors='coerce' when some values might not be valid dates and should become NaT rather than stopping the whole conversion
3. Convert date columns to proper datetime dtype right after loading data, rather than working with them as plain strings throughout your analysis
Tip: Always pass an explicit format string to to_datetime() when you know it — it's both significantly faster for large datasets and removes any ambiguity about how to interpret an inherently ambiguous date string.
import pandas as pd
dates = pd.to_datetime(["2026-01-15", "2026-02-20"])
print(dates)