Is there a well-recognized way to map specific events (with a timestamp/s) to a pandas dataframe with a...
0
$begingroup$
So let's say I have this dataframe:
date_rng = pd.date_range(start='1/1/2018', end='1/08/2018', freq='T')
df = pd.DataFrame(date_rng2, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng2)))
df['event'] = df['data'].apply(lambda x: '')
df.head(15)
And then this next one, specifying specific events:
df2 = pd.DataFrame(columns=['Event', 'Start Time', 'Stop Time'])
e1 = {
'Event': 'Malfunction',
'Start Time': pd.to_datetime('2018-01-01 00:34:00'),
'Stop Time': pd.to_datetime('2018-01-01 00:39:00')
}
e2 = {
'Event': 'Cleaning',
'Start Time': pd.to_datetime('2018-01-01 01:02:00'),
'Stop Time': pd.to_datetime('2018-01-01 09:29:00')
}
df2 = df2.append(e1, ignore_index=True)
df2 = df2.append(e2, ignore_index=True)
df2
I am trying all sorts of weird and wonderful combinations of apply and applymap to try and get the first dataframe to display events, mapped from the second one. My question is: Is there a standardized methodology for doing something like this?
time-series pandas
asked 2 days ago
P. TurnerP. Turner
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
$begingroup$
So let's say I have this dataframe:
date_rng = pd.date_range(start='1/1/2018', end='1/08/2018', freq='T')
df = pd.DataFrame(date_rng2, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng2)))
df['event'] = df['data'].apply(lambda x: '')
df.head(15)
And then this next one, specifying specific events:
df2 = pd.DataFrame(columns=['Event', 'Start Time', 'Stop Time'])
e1 = {
'Event': 'Malfunction',
'Start Time': pd.to_datetime('2018-01-01 00:34:00'),
'Stop Time': pd.to_datetime('2018-01-01 00:39:00')
}
e2 = {
'Event': 'Cleaning',
'Start Time': pd.to_datetime('2018-01-01 01:02:00'),
'Stop Time': pd.to_datetime('2018-01-01 09:29:00')
}
df2 = df2.append(e1, ignore_index=True)
df2 = df2.append(e2, ignore_index=True)
df2
I am trying all sorts of weird and wonderful combinations of apply and applymap to try and get the first dataframe to display events, mapped from the second one. My question is: Is there a standardized methodology for doing something like this?
time-series pandas
asked 2 days ago
P. TurnerP. Turner
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
So let's say I have this dataframe:
date_rng = pd.date_range(start='1/1/2018', end='1/08/2018', freq='T')
df = pd.DataFrame(date_rng2, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng2)))
df['event'] = df['data'].apply(lambda x: '')
df.head(15)
And then this next one, specifying specific events:
df2 = pd.DataFrame(columns=['Event', 'Start Time', 'Stop Time'])
e1 = {
'Event': 'Malfunction',
'Start Time': pd.to_datetime('2018-01-01 00:34:00'),
'Stop Time': pd.to_datetime('2018-01-01 00:39:00')
}
e2 = {
'Event': 'Cleaning',
'Start Time': pd.to_datetime('2018-01-01 01:02:00'),
'Stop Time': pd.to_datetime('2018-01-01 09:29:00')
}
df2 = df2.append(e1, ignore_index=True)
df2 = df2.append(e2, ignore_index=True)
df2
I am trying all sorts of weird and wonderful combinations of apply and applymap to try and get the first dataframe to display events, mapped from the second one. My question is: Is there a standardized methodology for doing something like this?
time-series pandas
asked 2 days ago
P. TurnerP. Turner
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
So let's say I have this dataframe:
date_rng = pd.date_range(start='1/1/2018', end='1/08/2018', freq='T')
df = pd.DataFrame(date_rng2, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng2)))
df['event'] = df['data'].apply(lambda x: '')
df.head(15)
And then this next one, specifying specific events:
df2 = pd.DataFrame(columns=['Event', 'Start Time', 'Stop Time'])
e1 = {
'Event': 'Malfunction',
'Start Time': pd.to_datetime('2018-01-01 00:34:00'),
'Stop Time': pd.to_datetime('2018-01-01 00:39:00')
}
e2 = {
'Event': 'Cleaning',
'Start Time': pd.to_datetime('2018-01-01 01:02:00'),
'Stop Time': pd.to_datetime('2018-01-01 09:29:00')
}
df2 = df2.append(e1, ignore_index=True)
df2 = df2.append(e2, ignore_index=True)
df2
I am trying all sorts of weird and wonderful combinations of apply and applymap to try and get the first dataframe to display events, mapped from the second one. My question is: Is there a standardized methodology for doing something like this?
time-series pandas
time-series pandas
asked 2 days ago
P. TurnerP. Turner
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
P. TurnerP. Turner
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
P. TurnerP. Turner
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
P. TurnerP. Turner
1
asked 2 days ago
P. TurnerP. Turner
1
1
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
P. Turner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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