Split a Dataframe per time interval - pandas

Question

I have a dataframe in the following format:
timestamp,name,age
2020-03-01 00:00:01,nick
2020-03-01 00:00:01,john
2020-03-01 00:00:02,nick
2020-03-01 00:00:02,john
2020-03-01 00:00:04,peter
2020-03-01 00:00:05,john
2020-03-01 00:00:10,nick
2020-03-01 00:00:12,john
2020-03-01 00:00:54,hank
2020-03-01 00:01:03,peter
I am trying to split this dataframe into many dataframes based on a time interval (for example 1 minute) and append the results into a dictionary.
I am trying:
df = pd.read_csv('/home/antonis/repos/newtest.csv')
minutesplit = {n: g.reset_index()
for n, g in df.set_index('timestamp').groupby(pd.Grouper(key='timestamp',freq='1Min'))}
but an error occurs like:
KeyError: 'The grouper name timestamp is not found'
Does anyone know what am I doing wrong?

Answer 1

First convert column timestamp to datetimes by parameter parse_dates:
df = pd.read_csv('/home/antonis/repos/newtest.csv', parse_dates=['timestamp'])
And then convert to index by set_index with convert index to column by reset_index is not necessary, Grouper is use parameter key for looking for column timestamp:
Here are keys in dictionary of DataFrames created by datetimes, so for seelcting is used pd.Timestamp:
minutesplit = {n: g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='1Min'))}
print (minutesplit[pd.Timestamp('2020-03-01 00:01')])
timestamp name age
9 2020-03-01 00:01:03 peter NaN
If keys convert to strings YYYY-MM-DD HH:MM is used strftime:
minutesplit = {n.strftime('%Y-%m-%d %H:%M'): g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='1Min'))}
print (minutesplit['2020-03-01 00:01'])
timestamp name age
9 2020-03-01 00:01:03 peter NaN
If need list of DataFrames:
minutesplit = [g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='1Min'))]
print (minutesplit[1])
timestamp name age
9 2020-03-01 00:01:03 peter NaN