Update README.md

README.md

@@ -30,10 +30,14 @@
 
 * Pandas better describe - colour the percentiles, not the counts/mean/std, include 5/95% in percentiles. Add dtype description and maybe memory, not how many columns were ignored too (stick on the end maybe?)
 
-* Pandas better cut - give it some ranges and ask for nice labels and it'll form e.g. in scientific form (1M-500k ...) with open/closed labels, maybe special handling of e.g. 0, with formatting for currency and others
+* Pandas better cut - give it some ranges and ask for nice labels and it'll form e.g. in scientific form (1M-500k ...) with open/closed labels, maybe special handling of e.g. 0, with formatting for currency and others - in progress in `simpler_pandas`
 
 * Matplotlib label formatter - take int/float labels and convert to eg currency (2dp), human readable (e.g. 1M), optional leading symbol (e.g. £, $) or trailing text (e.g. pp.), with commas (e.g. "2,000") `friendly_label(dp=2, leading_text="", following_text="", with_commas=False, ints_if_possible=False)` and `human_readable(...)`
 
+## NumPy
+
+`dts = np.arange("2021-01-01", "2021-01-30", dtype="datetime64[D]")` will make 29 * 1D datetime64 objectss in an array. `pd.to_datetime(dts)` will make a Pandas `DateTimeIndex` of dtype `datetime64[ns]`. NumPy doesn't store timezone info https://numpy.org/doc/stable/reference/arrays.datetime.html . 
+
 ## Pandas
 
 ### `groupby`