I am dealing with timestamps that, according to Google's documentation, are:
A timestamp in RFC3339 UTC "Zulu" format, accurate to nanoseconds. Example: "2014-10-02T15:01:23.045123456Z".
So, for example, if the string is '2019-11-06T06:24:42.558008Z', then pd.to_datetime('2019-11-06T06:24:42.558008Z',infer_datetime_format=True) works and returns Timestamp('2019-11-06 06:24:42.558008').
However, letting Pandas infer the format is slow, and I have many rows of data. What would I pass the format parameter to help speed up the processing?
You could use to_datetime with utc=True + tz_convert:
import pandas as pd
utc = pd.to_datetime('2019-11-06T06:24:42.558008Z', utc=True).tz_convert(None)
inferred = pd.to_datetime('2019-11-06T06:24:42.558008Z', infer_datetime_format=True)
print(utc == inferred)
Output
True
From the documentation on tz_convert:
A tz of None will convert to UTC and remove the timezone information.
Note that only doing:
utc = pd.to_datetime('2019-11-06T06:24:42.558008Z', utc=True) # or pd.to_datetime('2019-11-06T06:24:42.558008Z')
throws a TypeError exception when comparing with inferred:
TypeError: Cannot compare tz-naive and tz-aware timestamps
Related
I have a column of years from the sunspots dataset.
I want to convert column 'year' in integer e.g. 1992 to datetime format then find the time delta and eventually compute total seconds (cumulative) to represent the time index column of a time series.
I am trying to use the following code but I get the error
TypeError: dtype datetime64[ns] cannot be converted to timedelta64[ns]
sunspots_df['year'] = pd.to_timedelta(pd.to_datetime(sunspots_df['year'], format='%Y') ).dt.total_seconds()
pandas.Timedelta "[r]epresents a duration, the difference between two dates or times." So you're trying to get Python to tell you the difference between a particular datetime and...nothing. That's why it's failing.
If it's important that you store your index this way (and there may be better ways), then you need to pick a start datetime and compute the difference to get a timedelta.
For example, this code...
import pandas as pd
df = pd.DataFrame({'year': [1990,1991,1992]})
diff = (pd.to_datetime(df['year'], format='%Y') - pd.to_datetime('1990', format='%Y'))\
.dt.total_seconds()
...returns a series whose values are seconds from January 1st, 1990. You'll note that it doesn't invoke pd.to_timedelta(), because it doesn't need to: the result of the subtraction is automatically a pd.timedelta column.
I'm curious about how to use pandas to deal with this sort of info in a .csv file:
2022-08-11 11:50:01 America/Los_Angeles
My goal is to extract the date, hour and minute, and the timezone info for further analysis.
I have tried to lift out the date and time using:
df['Date'] = pd.to_datetime(df['datetime']).dt.date
but got an error because of the string at the end. Other than extracting the date and time using specific indices, is there any better and quicker way? Thank you so much.
pandas cannot handle a datetime column with different timezones. You can start by splitting the datetime and timezone in separate columns:
df[['datetime', 'timezone']] = df['datetime'].str.rsplit(' ', n=1, expand=True)
df['datetime'] = pd.to_datetime(df['datetime']) # this column now has the datetime64[ns] type
Now you are able to do the following:
df['date_only'] = df['datetime'].dt.date
If you want to express all local date/times in America/Los_Angeles time:
df['LA_datetime'] = df.apply(lambda x: x['datetime'].tz_localize(tz=x['timezone']).tz_convert('America/Los_Angeles'), axis = 1)
You can change America/Los_Angeles to the timezone of your liking.
The objective of this post is to be able to convert the columns [‘Open Date’, 'Close date’] to timestamp format
I have tried with the functions / examples from these links with any results.
Convert datetime to timestamp in Neo4j
Convert datetime pandas
Pandas to_dict() converts datetime to Timestamp
Really appreciate any ideas / comments / examples on how to do so.
Data Base Image
Column Characteristics:
Open Date datetime64[ns] and pandas.core.series.Series
Close date datetime64[ns] and pandas.core.series.Series
Finally I been using these libraries
import pandas as pd
import numpy as np
from datetime import datetime, date, time, timedelta
You convert first to numpy array by values and transform (cast) to int64 - output is in nanoseconds , which means divide by 10 ** 9:
df['open_ts'] = df['Open_Date'].datetime.values.astype(np.int64)
df['close_ts'] = df['Close_Date'].datetime.values.astype(np.int64)
OR
If you want to avoid using numpy, you can also try:
df['open_ts'] = pd.to_timedelta(df['Open_Date'], unit='ns').dt.total_seconds().astype(int)
df['close_ts'] = pd.to_timedelta(df['Close_Date'], unit='ns').dt.total_seconds().astype(int)
Try them and report it back here
I am dealing with time objects saved as strings in the form 57:44.6 (second, minute, hour).I am trying to convert the column elements to datetime using pd_todatetime. There results are Nat. How can i change the format of the string to HH:MM:SS (6:44:57)before converting?
provide the appropriate format specifier '%S:%M.%H'. Ex:
import pandas as pd
s = pd.Series(['57:44.6'])
dts = pd.to_datetime(s, format='%S:%M.%H')
# dts
# 0 1900-01-01 06:44:57
# dtype: datetime64[ns]
I have dates in this format: 2015-02-02 14:19:00.
I use this code:
dateparse = lambda dates: pd.datetime.strptime(dates, '%Y/%m/%d %H:%M:%S')
df = pd.read_csv('3df_uniti.csv', parse_dates=True, index_col='date', date_parser=dateparse)
df.head()
but it doesn't work because it gives me the follow error:
time data does not match format
Can you help me to set the right format?
Your format uses / instead of -. Try changing it to %Y-%m-%d %H:%M:%S.