pandas has a number of very handy utilities for manipulating datetime indices. Is there any similar functionality in Julia? I have not found any tutorials for working with such things, though it obviously must be possible.
Some examples of pandas utilities:
dti = pd.to_datetime(
["1/1/2018", np.datetime64("2018-01-01"),
datetime.datetime(2018, 1, 1)]
)
dti = pd.date_range("2018-01-01", periods=3, freq="H")
dti = dti.tz_localize("UTC")
dti.tz_convert("US/Pacific")
idx = pd.date_range("2018-01-01", periods=5, freq="H")
ts = pd.Series(range(len(idx)), index=idx)
ts.resample("2H").mean()
Julia libraries have "do only one thing but do it right" philosophy so the layout of its libraries matches perhaps more a Unix (battery of small tools that allow to accomplish a common goal) rather then Python's. Hence you have separate libraries for DataFrames and Dates:
julia> using Dates, DataFrames
Going through some of the examples of your tutorial:
dti = pd.to_datetime(
["1/1/2018", np.datetime64("2018-01-01"), datetime.datetime(2018, 1, 1)]
)
julia> DataFrame(dti=[Date("1/1/2018", "m/d/y"), Date("2018-01-01"), Date(2018,1,1)])
3×1 DataFrame
Row │ dti
│ Date
─────┼────────────
1 │ 2018-01-01
2 │ 2018-01-01
3 │ 2018-01-01
dti = pd.date_range("2018-01-01", periods=3, freq="H")
julia> DateTime("2018-01-01") .+ Hour.(0:2)
3-element Vector{DateTime}:
2018-01-01T00:00:00
2018-01-01T01:00:00
2018-01-01T02:00:00
dti = dti.tz_localize("UTC")
dti.tz_convert("US/Pacific")
Note that that there is a separate library in Julia for time zones. Additionally "US/Pacific" is a legacy name of a time zone.
julia> using TimeZones
julia> dti = ZonedDateTime.(dti, tz"UTC")
3-element Vector{ZonedDateTime}:
2018-01-01T00:00:00+00:00
2018-01-01T01:00:00+00:00
2018-01-01T02:00:00+00:00
julia> julia> astimezone.(dti, TimeZone("US/Pacific", TimeZones.Class(:LEGACY)))
3-element Vector{ZonedDateTime}:
2017-12-31T16:00:00-08:00
2017-12-31T17:00:00-08:00
2017-12-31T18:00:00-08:00
idx = pd.date_range("2018-01-01", periods=5, freq="H")
ts = pd.Series(range(len(idx)), index=idx)
ts.resample("2H").mean()
For resampling or other complex manipulations you will want to use the split-apply-combine pattern (see https://docs.juliahub.com/DataFrames/AR9oZ/1.3.1/man/split_apply_combine/)
julia> df = DataFrame(date=DateTime("2018-01-01") .+ Hour.(0:4), vals=1:5)
5×2 DataFrame
Row │ date vals
│ DateTime Int64
─────┼────────────────────────────
1 │ 2018-01-01T00:00:00 1
2 │ 2018-01-01T01:00:00 2
3 │ 2018-01-01T02:00:00 3
4 │ 2018-01-01T03:00:00 4
5 │ 2018-01-01T04:00:00 5
julia> df.date2 = floor.(df.date, Hour(2));
julia> using StatsBase
julia> combine(groupby(df, :date2), :date2, :vals => mean => :vals_mean)
5×2 DataFrame
Row │ date2 vals_mean
│ DateTime Float64
─────┼────────────────────────────────
1 │ 2018-01-01T00:00:00 1.5
2 │ 2018-01-01T00:00:00 1.5
3 │ 2018-01-01T02:00:00 3.5
4 │ 2018-01-01T02:00:00 3.5
5 │ 2018-01-01T04:00:00 5.0