to_datetime() for parsing dates in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of pandas.to_datetime()?

pandas.to_datetime() converts strings or other date-like objects into datetime objects that pandas can understand and work with easily.

beginner

How does to_datetime() handle different date formats?

It tries to automatically detect the format of the date strings. You can also specify the format manually to speed up parsing and avoid errors.

intermediate

What happens if to_datetime() encounters an invalid date string?

By default, it raises an error. But if you set errors='coerce', it will replace invalid dates with NaT (Not a Time), which is pandas' way of marking missing dates.

beginner

How can you convert a whole column of date strings in a DataFrame to datetime?

Use pd.to_datetime() on the column, like df['date'] = pd.to_datetime(df['date']). This converts all values in that column to datetime objects.

beginner

Why is it useful to convert strings to datetime in data analysis?

<p>Datetime objects let you easily do date math, filter by dates, extract parts like year or month, and plot time series data.</p>

What does pd.to_datetime('2024-06-01') return?

A A number representing the date

B A string '2024-06-01'

C An error because the format is unknown

D A datetime object representing June 1, 2024

pd.to_datetime() converts the string to a datetime object representing that date.

How can you tell to_datetime() to ignore errors and set invalid dates as missing?

A Set <code>errors='coerce'</code>

B Set <code>errors='ignore'</code>

C Set <code>format=None</code>

D Set <code>invalid='skip'</code>

Using errors='coerce' replaces invalid parsing with NaT.

Which parameter speeds up parsing when you know the exact date format?

A utc

B errors

C format

D dayfirst

The format parameter lets you specify the date format to speed up parsing.

What type does to_datetime() return when applied to a pandas Series?

A Series of strings

B Series of datetime64[ns] dtype

C List of datetime objects

D DataFrame

It returns a Series with dtype datetime64[ns], which pandas uses for datetime data.

If you have dates like '31/12/2023', which parameter helps interpret day before month?

A dayfirst=True

B format='%d/%m/%Y'

C errors='coerce'

D utc=True

Setting dayfirst=True tells pandas to treat the first number as the day.

Explain how pd.to_datetime() helps when working with date data in pandas.

Think about why dates as strings are hard to work with and how datetime objects solve that.

Describe how you would convert a DataFrame column with mixed date formats and some invalid entries into datetime safely.

Focus on error handling and conversion steps.