Convert Pandas Datetimeindex to Unix Time

pandas datetime to unix timestamp seconds

I think you misunderstood what the argument is for. The purpose of origin='unix' is to convert an integer timestamp to datetime, not the other way.

pd.to_datetime(1.547559e+09, unit='s', origin='unix') 
# Timestamp('2019-01-15 13:30:00')

Here are some options:

Option 1: integer division

Conversely, you can get the timestamp by converting to integer (to get nanoseconds) and divide by 10⁹.

pd.to_datetime(['2019-01-15 13:30:00']).astype(int) / 10**9
# Float64Index([1547559000.0], dtype='float64')

Pros:

• super fast

Cons:

• makes assumptions about how pandas internally stores dates

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Option 2: recommended by pandas

Pandas docs recommend using the following method:

# create test data
dates = pd.to_datetime(['2019-01-15 13:30:00'])

# calculate unix datetime
(dates - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')

[out]:
Int64Index([1547559000], dtype='int64')

Pros:

• "idiomatic", recommended by the library

Cons:

• unweildy
• not as performant as integer division

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Option 3: pd.Timestamp

If you have a single date string, you can use pd.Timestamp as shown in the other answer:

pd.Timestamp('2019-01-15 13:30:00').timestamp()
# 1547559000.0

If you have to cooerce multiple datetimes (where pd.to_datetime is your only option), you can initialize and map:

pd.to_datetime(['2019-01-15 13:30:00']).map(pd.Timestamp.timestamp)
# Float64Index([1547559000.0], dtype='float64')

Pros:

• best method for a single datetime string
• easy to remember

Cons:

• not as performant as integer division

Convert pandas DateTimeIndex to Unix Time?

As DatetimeIndex is ndarray under the hood, you can do the conversion without a comprehension (much faster).

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: from datetime import datetime

In [4]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]
   ...: index = pd.DatetimeIndex(dates)
   ...: 
In [5]: index.astype(np.int64)
Out[5]: array([1335830400000000000, 1335916800000000000, 1336003200000000000], 
        dtype=int64)

In [6]: index.astype(np.int64) // 10**9
Out[6]: array([1335830400, 1335916800, 1336003200], dtype=int64)

%timeit [t.value // 10 ** 9 for t in index]
10000 loops, best of 3: 119 us per loop

%timeit index.astype(np.int64) // 10**9
100000 loops, best of 3: 18.4 us per loop

Convert unix time to readable date in pandas dataframe

These appear to be seconds since epoch.

In [20]: df = DataFrame(data['values'])

In [21]: df.columns = ["date","price"]

In [22]: df
Out[22]: 
<class 'pandas.core.frame.DataFrame'>
Int64Index: 358 entries, 0 to 357
Data columns (total 2 columns):
date     358  non-null values
price    358  non-null values
dtypes: float64(1), int64(1)

In [23]: df.head()
Out[23]: 
         date  price
0  1349720105  12.08
1  1349806505  12.35
2  1349892905  12.15
3  1349979305  12.19
4  1350065705  12.15
In [25]: df['date'] = pd.to_datetime(df['date'],unit='s')

In [26]: df.head()
Out[26]: 
                 date  price
0 2012-10-08 18:15:05  12.08
1 2012-10-09 18:15:05  12.35
2 2012-10-10 18:15:05  12.15
3 2012-10-11 18:15:05  12.19
4 2012-10-12 18:15:05  12.15

In [27]: df.dtypes
Out[27]: 
date     datetime64[ns]
price           float64
dtype: object

pandas datetime to unixtime

I think you can subtract the date 1970-1-1 to create a timedelta and then access the attribute total_seconds:

In [130]:    
s = pd.Series(pd.datetime(2012,1,1))
s

Out[130]:
0   2012-01-01
dtype: datetime64[ns]

In [158]:
(s - dt.datetime(1970,1,1)).dt.total_seconds()

Out[158]:
0    1325376000
dtype: float64

Convert string date to timestamp in Python

>>> import time
>>> import datetime
>>> s = "01/12/2011"
>>> time.mktime(datetime.datetime.strptime(s, "%d/%m/%Y").timetuple())
1322697600.0

Python Convert UTC Datetime in string to unix time

since you mention that you're working with a pandas DataFrame, you can simplify to using

import pandas as pd
import numpy as np

df = pd.DataFrame({'times': ['2/3/15 2:00']})

# to datetime, format is inferred correctly
df['datetime'] = pd.to_datetime(df['times'])

# df['datetime'] 
# 0   2015-02-03 02:00:00
# Name: datetime, dtype: datetime64[ns]

# to Unix time / seconds since 1970-1-1 Z
# .astype(np.int64) on datetime Series gives you nanoseconds, so divide by 1e9 to get seconds
df['unix'] = df['datetime'].astype(np.int64) / 1e9

# df['unix']
# 0    1.422929e+09
# Name: unix, dtype: float64

Column from date time to Unix time-stamp in pandas

With apply

#df.Time_column=pd.to_datetime(df.Time_column)

df.Time_column.apply(lambda x : (x-datetime.datetime(1970,1,1)).total_seconds())

Convert unix time to readable date in pandas dataframe

These appear to be seconds since epoch.

In [20]: df = DataFrame(data['values'])

In [21]: df.columns = ["date","price"]

In [22]: df
Out[22]: 
<class 'pandas.core.frame.DataFrame'>
Int64Index: 358 entries, 0 to 357
Data columns (total 2 columns):
date     358  non-null values
price    358  non-null values
dtypes: float64(1), int64(1)

In [23]: df.head()
Out[23]: 
         date  price
0  1349720105  12.08
1  1349806505  12.35
2  1349892905  12.15
3  1349979305  12.19
4  1350065705  12.15
In [25]: df['date'] = pd.to_datetime(df['date'],unit='s')

In [26]: df.head()
Out[26]: 
                 date  price
0 2012-10-08 18:15:05  12.08
1 2012-10-09 18:15:05  12.35
2 2012-10-10 18:15:05  12.15
3 2012-10-11 18:15:05  12.19
4 2012-10-12 18:15:05  12.15

In [27]: df.dtypes
Out[27]: 
date     datetime64[ns]
price           float64
dtype: object

Pandas: Using Unix epoch timestamp as Datetime index

Convert them to datetime64[s]:

np.array([1368431149, 1368431150]).astype('datetime64[s]')
# array([2013-05-13 07:45:49, 2013-05-13 07:45:50], dtype=datetime64[s])

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