CODEWITHSUNDEEP
pythonpandaspandas-groupby
madsthaks
madsthaks

Reputation: 2181

Issues with grouping pandas dataframe by hour

First off, my data set is shown below

here

What I'd like to do is group my columns by pickup_datetime hour. I've found related questions on here but for some reason the solution doesn't seem to work. I've included my attemps below.

I first started off with this:

df["dropoff_datetime"] = pd.to_datetime(df["dropoff_datetime"])
df["pickup_datetime"] = pd.to_datetime(df["pickup_datetime"])

test = df.groupby(df.hour).sum()

And I got the following error:

AttributeError: 'DataFrame' object has no attribute 'hour'

Then I tried this:

test = df.groupby(df.dropoff_datetime.hour).sum()

And I got the following error:

AttributeError: 'Series' object has no attribute 'hour'

I'm a bit confused because it seems like my situation is the same as the question linked above. I'm not sure why I am getting errors though. Any help would be much appreciated

Upvotes: 3

Views: 5343

Answers (2)

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210862

we can use Series.dt.hour accessor:

test = df.groupby(df['pickup_datetime'].dt.hour).sum()

Here is an example describing the difference:

In [136]: times = pd.to_datetime(['2017-08-01 13:13:13', '2017-08-01 20:20:20'])

In [137]: times
Out[137]: DatetimeIndex(['2017-08-01 13:13:13', '2017-08-01 20:20:20'], dtype='datetime64[ns]', freq=None)

In [138]: type(times)
Out[138]: pandas.core.indexes.datetimes.DatetimeIndex

In [139]: times.hour
Out[139]: Int64Index([13, 20], dtype='int64')

as shown above DatetimeIndex has "direct" .hour accessor, but Series of datetime dtype has .dt.hour accessor:

In [140]: df = pd.DataFrame({'Date': times})

In [141]: df
Out[141]:
                 Date
0 2017-08-01 13:13:13
1 2017-08-01 20:20:20

In [142]: type(df.Date)
Out[142]: pandas.core.series.Series

In [143]: df['Date'].dt.hour
Out[143]:
0    13
1    20
Name: Date, dtype: int64

If we set Date column as an index:

In [146]: df.index = df['Date']

In [147]: df
Out[147]:
                                   Date
Date
2017-08-01 13:13:13 2017-08-01 13:13:13
2017-08-01 20:20:20 2017-08-01 20:20:20

it becomes:

In [149]: type(df.index)
Out[149]: pandas.core.indexes.datetimes.DatetimeIndex

so we can access it directly (without .dt accessor) again:

In [148]: df.index.hour
Out[148]: Int64Index([13, 20], dtype='int64', name='Date')

Upvotes: 6

jezrael
jezrael

Reputation: 862851

Need .dt because working with Series - Series.dt.hour:

test = df.groupby(df.dropoff_datetime.dt.hour).sum()

But if DatetimeIndex, omit it - DatetimeIndex.hour:

test = df.groupby(df.index.hour).sum()

Upvotes: 1

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