demo + utils venv

2019-02-03 13:40:10 +01:00
parent 5fa112490b
commit cfa9c8ea23
5994 changed files with 1353819 additions and 0 deletions
@@ -0,0 +1,64 @@
+"""This module is designed for community supported date conversion functions"""
+import numpy as np
+
+from pandas._libs.tslibs import parsing
+from pandas.compat import map, range
+
+
+def parse_date_time(date_col, time_col):
+    date_col = _maybe_cast(date_col)
+    time_col = _maybe_cast(time_col)
+    return parsing.try_parse_date_and_time(date_col, time_col)
+
+
+def parse_date_fields(year_col, month_col, day_col):
+    year_col = _maybe_cast(year_col)
+    month_col = _maybe_cast(month_col)
+    day_col = _maybe_cast(day_col)
+    return parsing.try_parse_year_month_day(year_col, month_col, day_col)
+
+
+def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col,
+                     second_col):
+    year_col = _maybe_cast(year_col)
+    month_col = _maybe_cast(month_col)
+    day_col = _maybe_cast(day_col)
+    hour_col = _maybe_cast(hour_col)
+    minute_col = _maybe_cast(minute_col)
+    second_col = _maybe_cast(second_col)
+    return parsing.try_parse_datetime_components(year_col, month_col, day_col,
+                                                 hour_col, minute_col,
+                                                 second_col)
+
+
+def generic_parser(parse_func, *cols):
+    N = _check_columns(cols)
+    results = np.empty(N, dtype=object)
+
+    for i in range(N):
+        args = [c[i] for c in cols]
+        results[i] = parse_func(*args)
+
+    return results
+
+
+def _maybe_cast(arr):
+    if not arr.dtype.type == np.object_:
+        arr = np.array(arr, dtype=object)
+    return arr
+
+
+def _check_columns(cols):
+    if not len(cols):
+        raise AssertionError("There must be at least 1 column")
+
+    head, tail = cols[0], cols[1:]
+
+    N = len(head)
+
+    for i, n in enumerate(map(len, tail)):
+        if n != N:
+            raise AssertionError('All columns must have the same length: {0}; '
+                                 'column {1} has length {2}'.format(N, i, n))
+
+    return N