## Convert Timedelta to Int in Pandas

This tutorial will discuss converting the timedelta to int using the dt attribute in Pandas.

### Convert the timedelta to int Using the dt Attribute in Pandas

To convert the timedelta to an integer value, we can use the pandas library’s dt attribute. The dt attribute allows us to extract components of the timedelta . For example, we can extract the year, month, day, minutes, or seconds using the dt attribute. To do that, we need to write the component’s name after the dt attribute. To show all the components of the timedelta variable, we can use the components attribute. For example, let’s create a time series using the pandas Series attribute and show its components using the components attribute.

As you can see, the components attribute showed all the components of the time series. The timedelta_range() attribute is used in the above code to create the time series. We can define the starting and ending point and the frequency at which the time will change inside the timedelta_range() attribute. We can extract any of these components using the name of that component. For example, let’s extract the days component from the above time series. See the code below.

You can extract any component you want from the above time series. We can also convert the timedelta to an integer by dividing it by the timedelta of one day or by extracting its integer part using the astype() attribute. For example, let’s create a timedelta object and convert it into an integer to get the value of the day using NumPy . See the code below.

## Time deltas#

Timedeltas are differences in times, expressed in difference units, e.g. days, hours, minutes, seconds. They can be both positive and negative.

Timedelta is a subclass of datetime.timedelta , and behaves in a similar manner, but allows compatibility with np.timedelta64 types as well as a host of custom representation, parsing, and attributes.

### Parsing#

You can construct a Timedelta scalar through various arguments, including ISO 8601 Duration strings.

DateOffsets ( Day, Hour, Minute, Second, Milli, Micro, Nano ) can also be used in construction.

Further, operations among the scalars yield another scalar Timedelta .

#### to_timedelta#

Using the top-level pd.to_timedelta , you can convert a scalar, array, list, or Series from a recognized timedelta format / value into a Timedelta type. It will construct Series if the input is a Series, a scalar if the input is scalar-like, otherwise it will output a TimedeltaIndex .

You can parse a single string to a Timedelta:

or a list/array of strings:

The unit keyword argument specifies the unit of the Timedelta if the input is numeric:

If a string or array of strings is passed as an input then the unit keyword argument will be ignored. If a string without units is passed then the default unit of nanoseconds is assumed.

#### Timedelta limitations#

pandas represents Timedeltas in nanosecond resolution using 64 bit integers. As such, the 64 bit integer limits determine the Timedelta limits.

### Operations#

You can operate on Series/DataFrames and construct timedelta64[ns] Series through subtraction operations on datetime64[ns] Series, or Timestamps .

Operations with scalars from a timedelta64[ns] series:

Series of timedeltas with NaT values are supported:

Elements can be set to NaT using np.nan analogously to datetimes:

Operands can also appear in a reversed order (a singular object operated with a Series):

min, max and the corresponding idxmin, idxmax operations are supported on frames:

min, max, idxmin, idxmax operations are supported on Series as well. A scalar result will be a Timedelta .

You can fillna on timedeltas, passing a timedelta to get a particular value.

You can also negate, multiply and use abs on Timedeltas :

### Reductions#

Numeric reduction operation for timedelta64[ns] will return Timedelta objects. As usual NaT are skipped during evaluation.

### Frequency conversion#

Timedelta Series and TimedeltaIndex , and Timedelta can be converted to other frequencies by astyping to a specific timedelta dtype.

For timedelta64 resolutions other than the supported “s”, “ms”, “us”, “ns”, an alternative is to divide by another timedelta object. Note that division by the NumPy scalar is true division, while astyping is equivalent of floor division.

Dividing or multiplying a timedelta64[ns] Series by an integer or integer Series yields another timedelta64[ns] dtypes Series.

Rounded division (floor-division) of a timedelta64[ns] Series by a scalar Timedelta gives a series of integers.

The mod (%) and divmod operations are defined for Timedelta when operating with another timedelta-like or with a numeric argument.

### Attributes#

You can access various components of the Timedelta or TimedeltaIndex directly using the attributes days,seconds,microseconds,nanoseconds . These are identical to the values returned by datetime.timedelta , in that, for example, the .seconds attribute represents the number of seconds >= 0 and < 1 day. These are signed according to whether the Timedelta is signed.

These operations can also be directly accessed via the .dt property of the Series as well.

Note that the attributes are NOT the displayed values of the Timedelta . Use .components to retrieve the displayed values.

You can access the value of the fields for a scalar Timedelta directly.

You can use the .components property to access a reduced form of the timedelta. This returns a DataFrame indexed similarly to the Series . These are the *displayed* values of the Timedelta .

You can convert a Timedelta to an ISO 8601 Duration string with the .isoformat method

### TimedeltaIndex#

To generate an index with time delta, you can use either the TimedeltaIndex or the timedelta_range() constructor.

Using TimedeltaIndex you can pass string-like, Timedelta , timedelta , or np.timedelta64 objects. Passing np.nan/pd.NaT/nat will represent missing values.

The string ‘infer’ can be passed in order to set the frequency of the index as the inferred frequency upon creation:

#### Generating ranges of time deltas#

Similar to date_range() , you can construct regular ranges of a TimedeltaIndex using timedelta_range() . The default frequency for timedelta_range is calendar day:

Various combinations of start , end , and periods can be used with timedelta_range :

The freq parameter can passed a variety of frequency aliases :

Specifying start , end , and periods will generate a range of evenly spaced timedeltas from start to end inclusively, with periods number of elements in the resulting TimedeltaIndex :

#### Using the TimedeltaIndex#

Similarly to other of the datetime-like indices, DatetimeIndex and PeriodIndex , you can use TimedeltaIndex as the index of pandas objects.

Selections work similarly, with coercion on string-likes and slices:

Furthermore you can use partial string selection and the range will be inferred:

#### Operations#

Finally, the combination of TimedeltaIndex with DatetimeIndex allow certain combination operations that are NaT preserving:

#### Conversions#

Similarly to frequency conversion on a Series above, you can convert these indices to yield another Index.

Scalars type ops work as well. These can potentially return a *different* type of index.

### Resampling#

Similar to timeseries resampling , we can resample with a TimedeltaIndex .

## How to convert timedelta to integer in pandas?

I have a column ‘Time’ in pandas that includes both integer and time deltas in days:

I am wanting to change all of the Time deltas to integers, but I am getting many errors when trying to pick and choose which values to convert, as it throws errors when I try to convert an integer within the column rather than a TD.

I’ve tried a few variations of this to check if it’s an integer, as I’m not concerned with those:

## Как перевести timedelta в int python

First, PANDAS processing

Second, NUMPY processing

Method (1) Pandas is directly extracted DAYS value, such as 90 days 04:48:00 extraction value 90;

Method (2) NUMPY is converted throughout the time, such as 90 days 04:48:00 translates to 90.200000.

Different methods can be selected for time conversion according to actual needs.

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