Test_data added

examples/data_types_and_io/data_types_and_io/file.py

@@ -37,17 +37,17 @@ def normalize_columns(
         normalized_data[colname] = [(x - mean) / std for x in values]
 
     # write to local path
-    out_path = Path(flytekit.current_context().working_directory) / f"normalized-{Path(csv_url.path).stem}.csv"
-    with out_path.open(mode="w") as output_file:
+    out_path = str(Path(flytekit.current_context().working_directory) / f"normalized-{Path(csv_url.path).stem}.csv")
+    with open(out_path, mode="w") as output_file:
         writer = csv.DictWriter(output_file, fieldnames=columns_to_normalize)
         writer.writeheader()
         for row in zip(*normalized_data.values()):
             writer.writerow({k: row[i] for i, k in enumerate(columns_to_normalize)})
 
     if output_location:
-        return FlyteFile(path=out_path, remote_path=output_location)
+        return FlyteFile(path=str(out_path), remote_path=output_location)
     else:
-        return FlyteFile(path=out_path)
+        return FlyteFile(path=str(out_path))
 
 
 # Define a workflow. The `normalize_csv_files` workflow has an `output_location` argument which is passed

examples/data_types_and_io/test_data/biostats.csv

@@ -0,0 +1,20 @@
+"Name",     "Sex", "Age", "Height (in)", "Weight (lbs)"
+"Alex",       "M",   41,       74,      170
+"Bert",       "M",   42,       68,      166
+"Carl",       "M",   32,       70,      155
+"Dave",       "M",   39,       72,      167
+"Elly",       "F",   30,       66,      124
+"Fran",       "F",   33,       66,      115
+"Gwen",       "F",   26,       64,      121
+"Hank",       "M",   30,       71,      158
+"Ivan",       "M",   53,       72,      175
+"Jake",       "M",   32,       69,      143
+"Kate",       "F",   47,       69,      139
+"Luke",       "M",   34,       72,      163
+"Myra",       "F",   23,       62,       98
+"Neil",       "M",   36,       75,      160
+"Omar",       "M",   38,       70,      145
+"Page",       "F",   31,       67,      135
+"Quin",       "M",   29,       71,      176
+"Ruth",       "F",   28,       65,      131
+

examples/data_types_and_io/test_data/faithful.csv

@@ -0,0 +1,273 @@
+"Index", "Eruption length (mins)","Eruption wait (mins)"
+  1, 3.600, 79
+  2, 1.800, 54
+  3, 3.333, 74
+  4, 2.283, 62
+  5, 4.533, 85
+  6, 2.883, 55
+  7, 4.700, 88
+  8, 3.600, 85
+  9, 1.950, 51
+ 10, 4.350, 85
+ 11, 1.833, 54
+ 12, 3.917, 84
+ 13, 4.200, 78
+ 14, 1.750, 47
+ 15, 4.700, 83
+ 16, 2.167, 52
+ 17, 1.750, 62
+ 18, 4.800, 84
+ 19, 1.600, 52
+ 20, 4.250, 79
+ 21, 1.800, 51
+ 22, 1.750, 47
+ 23, 3.450, 78
+ 24, 3.067, 69
+ 25, 4.533, 74
+ 26, 3.600, 83
+ 27, 1.967, 55
+ 28, 4.083, 76
+ 29, 3.850, 78
+ 30, 4.433, 79
+ 31, 4.300, 73
+ 32, 4.467, 77
+ 33, 3.367, 66
+ 34, 4.033, 80
+ 35, 3.833, 74
+ 36, 2.017, 52
+ 37, 1.867, 48
+ 38, 4.833, 80
+ 39, 1.833, 59
+ 40, 4.783, 90
+ 41, 4.350, 80
+ 42, 1.883, 58
+ 43, 4.567, 84
+ 44, 1.750, 58
+ 45, 4.533, 73
+ 46, 3.317, 83
+ 47, 3.833, 64
+ 48, 2.100, 53
+ 49, 4.633, 82
+ 50, 2.000, 59
+ 51, 4.800, 75
+ 52, 4.716, 90
+ 53, 1.833, 54
+ 54, 4.833, 80
+ 55, 1.733, 54
+ 56, 4.883, 83
+ 57, 3.717, 71
+ 58, 1.667, 64
+ 59, 4.567, 77
+ 60, 4.317, 81
+ 61, 2.233, 59
+ 62, 4.500, 84
+ 63, 1.750, 48
+ 64, 4.800, 82
+ 65, 1.817, 60
+ 66, 4.400, 92
+ 67, 4.167, 78
+ 68, 4.700, 78
+ 69, 2.067, 65
+ 70, 4.700, 73
+ 71, 4.033, 82
+ 72, 1.967, 56
+ 73, 4.500, 79
+ 74, 4.000, 71
+ 75, 1.983, 62
+ 76, 5.067, 76
+ 77, 2.017, 60
+ 78, 4.567, 78
+ 79, 3.883, 76
+ 80, 3.600, 83
+ 81, 4.133, 75
+ 82, 4.333, 82
+ 83, 4.100, 70
+ 84, 2.633, 65
+ 85, 4.067, 73
+ 86, 4.933, 88
+ 87, 3.950, 76
+ 88, 4.517, 80
+ 89, 2.167, 48
+ 90, 4.000, 86
+ 91, 2.200, 60
+ 92, 4.333, 90
+ 93, 1.867, 50
+ 94, 4.817, 78
+ 95, 1.833, 63
+ 96, 4.300, 72
+ 97, 4.667, 84
+ 98, 3.750, 75
+ 99, 1.867, 51
+100, 4.900, 82
+101, 2.483, 62
+102, 4.367, 88
+103, 2.100, 49
+104, 4.500, 83
+105, 4.050, 81
+106, 1.867, 47
+107, 4.700, 84
+108, 1.783, 52
+109, 4.850, 86
+110, 3.683, 81
+111, 4.733, 75
+112, 2.300, 59
+113, 4.900, 89
+114, 4.417, 79
+115, 1.700, 59
+116, 4.633, 81
+117, 2.317, 50
+118, 4.600, 85
+119, 1.817, 59
+120, 4.417, 87
+121, 2.617, 53
+122, 4.067, 69
+123, 4.250, 77
+124, 1.967, 56
+125, 4.600, 88
+126, 3.767, 81
+127, 1.917, 45
+128, 4.500, 82
+129, 2.267, 55
+130, 4.650, 90
+131, 1.867, 45
+132, 4.167, 83
+133, 2.800, 56
+134, 4.333, 89
+135, 1.833, 46
+136, 4.383, 82
+137, 1.883, 51
+138, 4.933, 86
+139, 2.033, 53
+140, 3.733, 79
+141, 4.233, 81
+142, 2.233, 60
+143, 4.533, 82
+144, 4.817, 77
+145, 4.333, 76
+146, 1.983, 59
+147, 4.633, 80
+148, 2.017, 49
+149, 5.100, 96
+150, 1.800, 53
+151, 5.033, 77
+152, 4.000, 77
+153, 2.400, 65
+154, 4.600, 81
+155, 3.567, 71
+156, 4.000, 70
+157, 4.500, 81
+158, 4.083, 93
+159, 1.800, 53
+160, 3.967, 89
+161, 2.200, 45
+162, 4.150, 86
+163, 2.000, 58
+164, 3.833, 78
+165, 3.500, 66
+166, 4.583, 76
+167, 2.367, 63
+168, 5.000, 88
+169, 1.933, 52
+170, 4.617, 93
+171, 1.917, 49
+172, 2.083, 57
+173, 4.583, 77
+174, 3.333, 68
+175, 4.167, 81
+176, 4.333, 81
+177, 4.500, 73
+178, 2.417, 50
+179, 4.000, 85
+180, 4.167, 74
+181, 1.883, 55
+182, 4.583, 77
+183, 4.250, 83
+184, 3.767, 83
+185, 2.033, 51
+186, 4.433, 78
+187, 4.083, 84
+188, 1.833, 46
+189, 4.417, 83
+190, 2.183, 55
+191, 4.800, 81
+192, 1.833, 57
+193, 4.800, 76
+194, 4.100, 84
+195, 3.966, 77
+196, 4.233, 81
+197, 3.500, 87
+198, 4.366, 77
+199, 2.250, 51
+200, 4.667, 78
+201, 2.100, 60
+202, 4.350, 82
+203, 4.133, 91
+204, 1.867, 53
+205, 4.600, 78
+206, 1.783, 46
+207, 4.367, 77
+208, 3.850, 84
+209, 1.933, 49
+210, 4.500, 83
+211, 2.383, 71
+212, 4.700, 80
+213, 1.867, 49
+214, 3.833, 75
+215, 3.417, 64
+216, 4.233, 76
+217, 2.400, 53
+218, 4.800, 94
+219, 2.000, 55
+220, 4.150, 76
+221, 1.867, 50
+222, 4.267, 82
+223, 1.750, 54
+224, 4.483, 75
+225, 4.000, 78
+226, 4.117, 79
+227, 4.083, 78
+228, 4.267, 78
+229, 3.917, 70
+230, 4.550, 79
+231, 4.083, 70
+232, 2.417, 54
+233, 4.183, 86
+234, 2.217, 50
+235, 4.450, 90
+236, 1.883, 54
+237, 1.850, 54
+238, 4.283, 77
+239, 3.950, 79
+240, 2.333, 64
+241, 4.150, 75
+242, 2.350, 47
+243, 4.933, 86
+244, 2.900, 63
+245, 4.583, 85
+246, 3.833, 82
+247, 2.083, 57
+248, 4.367, 82
+249, 2.133, 67
+250, 4.350, 74
+251, 2.200, 54
+252, 4.450, 83
+253, 3.567, 73
+254, 4.500, 73
+255, 4.150, 88
+256, 3.817, 80
+257, 3.917, 71
+258, 4.450, 83
+259, 2.000, 56
+260, 4.283, 79
+261, 4.767, 78
+262, 4.533, 84
+263, 1.850, 58
+264, 4.250, 83
+265, 1.983, 43
+266, 2.250, 60
+267, 4.750, 75
+268, 4.117, 81
+269, 2.150, 46
+270, 4.417, 90
+271, 1.817, 46
+272, 4.467, 74