Convert Pandas DateSeries to native Julia.

Project.toml

@@ -5,6 +5,7 @@ version = "1.5.0"
 [deps]
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 DataValues = "e7dc6d0d-1eca-5fa6-8ad6-5aecde8b7ea5"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 IteratorInterfaceExtensions = "82899510-4779-5014-852e-03e436cf321d"
 Lazy = "50d2b5c4-7a5e-59d5-8109-a42b560f39c0"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
@@ -22,5 +23,5 @@ Lazy = "0.15"
 PyCall = "1.90"
 TableTraits = "^0.4, ^1"
 TableTraitsUtils = "^0.3, ^0.4, ^1"
-julia = "0.7, 1"
 Tables = "1"
+julia = "0.7, 1"

src/Pandas.jl

@@ -1,6 +1,7 @@
 __precompile__(true)
 module Pandas
 
+using Dates
 using PyCall
 using Lazy
 using Compat
@@ -76,9 +77,27 @@ end
 
 quot(x) = Expr(:quote, x)
 
+
+function convert_datetime_series_to_julia_vector(series)
+    N = length(series)
+    out = Array{Dates.DateTime}(undef, N)
+    for i in 1:N
+        # PyCall.jl overloads the getindex method on `series` to automatically convert 
+        # to a Julia date type.
+        out[i] = series[i]
+    end
+    return out
+end
+
+
 function Base.Array(x::PandasWrapped)
+    if typeof(x) <: Series && x.pyo.dtype == np.dtype("<M8[ns]")
+        return convert_datetime_series_to_julia_vector(x)
+    end
     c = np.asarray(x.pyo)
-    if typeof(c).parameters[1] == PyObject
+    # PyCall will automatically try to convert the result of np.asarray to a native Julia array containing native Julia objects.
+    # If it can't, it will return a PyObject or a Julia vector of PyObjects.
+    if typeof(c) == PyObject || typeof(c).parameters[1] == PyObject
         out = Array{Any}(undef, size(x))
         for idx in eachindex(out)
             out[idx] = convert(PyAny, c[idx])

test/Project.toml

@@ -2,14 +2,16 @@
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataValues = "e7dc6d0d-1eca-5fa6-8ad6-5aecde8b7ea5"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 IteratorInterfaceExtensions = "82899510-4779-5014-852e-03e436cf321d"
+PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 TableTraits = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
+DataFrames = "1"
 DataValues = "0.4.4"
 IteratorInterfaceExtensions = "^0.1.1, ^1"
 TableTraits = "^0.4, ^1"
 Tables = "1"
-DataFrames = "1"

test/runtests.jl

@@ -1,6 +1,8 @@
 using Pandas
 using Test
 import DataFrames
+using PyCall
+using Dates
 
 df = DataFrame(Dict(:name=>["a", "b"], :age=>[27, 30]))
 age = values(df.age)
@@ -50,3 +52,19 @@ julia_df = DataFrames.DataFrame(x=[1,2], y=[missing, missing])
 py_df = Pandas.DataFrame(julia_df)
 expected_df = Pandas.DataFrame(:x=>[1,2], :y=>[NaN, NaN])
 @test Pandas.equals(py_df, expected_df)
+
+# Issue #68
+py"""
+import pandas as pd
+
+def get_df():
+    df = pd.DataFrame({
+        "a":pd.to_datetime(["2021.01.15","2021.01.15","2020.04.06"])
+    })
+    return df
+"""
+
+py_df = py"get_df"()|>Pandas.DataFrame
+julia_df = DataFrames.DataFrame(py_df)
+
+@test julia_df.a == [DateTime(2021, 1, 15), DateTime(2021, 1, 15), DateTime(2020, 4, 6)]