How to Use Excel's MINUTE Function in Pandas

Excel's MINUTE function extracts the minute from a time value.

This page explains how to implement Excel's MINUTE function in Python using pandas.

Understanding the Minute Extraction Formula in Excel#

The MINUTE function in Excel returns the minute of a time value, ranging from 0 to 59.

=MINUTE(serial_number)

MINUTE Excel Syntax

Examples

Implementing the Minute Extraction function in Pandas#

Recreating Excel's MINUTE function behavior in Python requires a combination of pandas operations. Here are some common implementations:

Extracting Minute from Datetime#

In Excel, if you have a datetime value, you can use the MINUTE function to return the minute component. Similarly, in pandas, you can use the `.dt` accessor followed by the `minute` attribute.

For example, in Excel you might use =MINUTE(A2). In pandas:

df['Minute'] = df['Datetime_Column'].dt.minute

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Converting string to datetime and then extracting the minute#

Often, Pandas will infer the data type of your column as string, even if the data to you looks like a date, ie: 1/2/23 12:30:00. In these cases, you need to convert the string to datetime before extracting the minute.

To do this in pandas, first use `pd.to_datetime` to convert the column to a datetime column, and then extract the minute:

# Convert the string to datetime
df['Datetime_Column'] = pd.to_datetime(df['String_Column'])

# Extract the minute from the datetime column
df['Minute'] = df['Datetime_Column'].dt.minute

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Grouping Data by Minute#

There are situations where you want to aggregate data based on minute. In Excel, you might use a pivot table after extracting the minute. Similarly, in pandas, after extracting the minute, you can use the `groupby` method

For example, if you have a column called 'Date' and a column called 'Website Traffic', you might want to group the data by minute and sum the traffic for each minute.

df['Minute'] = df['Date'].dt.minute
grouped_data = df.groupby('Minute').agg({'Website Traffic': 'sum'}).reset_index()

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Common mistakes when using MINUTE in Python#

While implementing the MINUTE function equivalent in pandas, a few common pitfalls might occur. Here's how to navigate them.

Incorrect datatypes#

The `.dt` accessor is exclusive to pandas Series with datetime64 data types. Using it on non-datetime columns will raise an AttributeError.

For example, if you have a column called 'Date', but it actually has an object data type, you'll need to convert it to datetime before using the `.dt` accessor. You can check the data type of a column using `df.dtypes`.

# Ensure the column is of datetime dtype
df['Datetime_Column'] = pd.to_datetime(df['Datetime_Column'])
df['Minute'] = df['Datetime_Column'].dt.minute

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Forgetting to Handle Null Values#

If your dataset has missing or NaT (Not-a-Timestamp) values in the datetime column, trying to extract the minute from them will result in NaN (Not a Number) values. Make sure to handle or filter them out as necessary.

# Drop rows with NaT values before extracting minute
df.dropna(subset=['Datetime_Column'], inplace=True)
df['Minute'] = df['Datetime_Column'].dt.minute

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