Pandas Convert from Datetime to Integer Timestamp

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

Pandas: convert date 'object' to int

The reason why test_df['Date'].astype(int) gives you an error is that your dates still contain hyphens "-". First suppress them by doing test_df['Date'].str.replace("-",""), then you can apply your first method to the resulting series. So the whole solution would be :

test_df['Date'].str.replace("-","").astype(int)
Note that this won't work if your "Date" column is not a string object, typically when Pandas has already parsed your series as TimeStamp. In this case you can use :

test_df['Date'].dt.strftime("%Y%m%d").astype(int)

Convert a timestamp to int using astype(int)

For ns like native format for numpy/pandas use:

d = pd.to_datetime(1613260800000000000)

native = int(d.timestamp() * 10**9)
print (native)
1613260800000000000

print (pd.to_datetime(native))
2021-02-14 00:00:00

If need convert column:

time = pd.to_datetime(1613260800000000000)

df = pd.DataFrame({'start_time':[time, time]})

print (df['start_time'].astype(np.int64))
0    1613260800000000000
1    1613260800000000000
Name: start_time, dtype: int64

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

How to convert to Pandas datetime to date to integer in pandas?

Subtract date from column by Series.sub and convert timedeltas to days by Series.dt.days:

df['days'] = pd.to_datetime(df['date']).sub(pd.Timestamp('2010-01-01')).dt.days
print (df)
                      date  days
0  2018-08-27 00:00:00.000  3160
1  2018-08-26 00:00:00.000  3159
2  2018-08-24 00:00:00.000  3157
3  2018-08-24 00:00:00.000  3157
4  2018-08-24 00:00:00.000  3157
5  2018-08-24 00:00:00.000  3157
6  2018-08-23 00:00:00.000  3156
7  2018-08-23 00:00:00.000  3156
8  2018-08-20 00:00:00.000  3153
9  2018-08-20 00:00:00.000  3153

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