Get datetimes from multiple columns

Sometimes, datetime data is split across columns. A dataset might have a date and a time column, or a date may be split into year, month, and day columns.

A column in this version of the survey data has been split so that dates are in one column, Part2StartDate, and times are in another, Part2StartTime. Your task is to use read_excel()'s parse_dates argument to combine them into one datetime column with a new name.

pandas has been imported as pd.

This exercise is part of the course

Streamlined Data Ingestion with pandas

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Exercise instructions

Create a dictionary, datetime_cols indicating that the new column Part2Start should consist of Part2StartDate and Part2StartTime.
Load the survey response file, supplying the dictionary to the parse_dates argument to create a new Part2Start column.
View summary statistics about the new Part2Start column with the describe() method.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create dict of columns to combine into new datetime column
datetime_cols = {"Part2Start": ____}


# Load file, supplying the dict to parse_dates
survey_data = pd.read_excel("fcc_survey_dts.xlsx",
                            ____)

# View summary statistics about Part2Start
print(survey_data.Part2Start.describe())

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