Vanilla numpy

np.random.rand(3, 5)

array([[0.31024214, 0.22749145, 0.13044336, 0.25461994, 0.52697739],
       [0.92107579, 0.23948126, 0.39244633, 0.14060991, 0.65208926],
       [0.48535859, 0.36495544, 0.68908358, 0.6986305 , 0.10662724]])

Use pandas

import pandas as pd

pd.DataFrame(np.random.rand(20, 40))

	0	1	2	3	4	5	6	7	8	9	...	30	31	32	33	34	35	36	37	38	39
0	0.832204	0.617130	0.715202	0.048174	0.939108	0.694942	0.400874	0.768283	0.461823	0.752841	...	0.367615	0.838190	0.590583	0.835761	0.271560	0.465102	0.000261	0.592607	0.222789	0.555047
1	0.788321	0.860006	0.364577	0.863090	0.329000	0.645932	0.447750	0.104087	0.118309	0.875203	...	0.553917	0.856876	0.528867	0.029799	0.275072	0.084020	0.594230	0.080030	0.894168	0.263263
2	0.807275	0.177940	0.440055	0.946966	0.116133	0.239189	0.528964	0.697391	0.399538	0.036615	...	0.399281	0.794940	0.066883	0.784982	0.125367	0.127151	0.929687	0.290480	0.546324	0.208128
3	0.518876	0.145254	0.768098	0.355796	0.280516	0.090796	0.065341	0.632886	0.829269	0.066430	...	0.650922	0.758805	0.448107	0.664060	0.923742	0.563842	0.963763	0.634112	0.943547	0.277540
4	0.737489	0.088993	0.887788	0.783618	0.154229	0.646554	0.649964	0.458868	0.535735	0.419357	...	0.299188	0.180115	0.244154	0.800606	0.064398	0.665558	0.502236	0.777374	0.939236	0.907948
5	0.211469	0.518740	0.258009	0.669228	0.507891	0.561564	0.867016	0.188172	0.749954	0.467626	...	0.225084	0.441328	0.694882	0.817158	0.552874	0.537532	0.115093	0.543791	0.034233	0.778791
6	0.343946	0.880380	0.231436	0.192562	0.916107	0.021313	0.975490	0.219741	0.698656	0.506352	...	0.512907	0.957507	0.258529	0.734369	0.757271	0.713988	0.388670	0.086245	0.663918	0.679773
7	0.982413	0.776863	0.352873	0.272454	0.705776	0.105242	0.649339	0.127062	0.655501	0.776311	...	0.220118	0.272943	0.245174	0.436132	0.037625	0.187643	0.615592	0.058919	0.573035	0.765318
8	0.249716	0.687485	0.323589	0.605468	0.122717	0.607954	0.116775	0.545862	0.601487	0.510816	...	0.173497	0.736962	0.436200	0.635635	0.336970	0.225108	0.066986	0.437094	0.327171	0.651059
9	0.477024	0.804410	0.520621	0.251052	0.512090	0.155425	0.579456	0.943275	0.022175	0.635136	...	0.316939	0.631615	0.463521	0.415427	0.905898	0.898094	0.149950	0.693533	0.221796	0.966813
10	0.317880	0.306459	0.084306	0.713852	0.385076	0.857218	0.096751	0.397386	0.648722	0.323559	...	0.188888	0.366867	0.622417	0.802035	0.446026	0.552871	0.702380	0.288050	0.553756	0.574177
11	0.499070	0.752395	0.996066	0.378952	0.843003	0.093795	0.310451	0.045367	0.841575	0.690082	...	0.407933	0.934846	0.689559	0.071082	0.344150	0.831786	0.473617	0.914371	0.366394	0.283473
12	0.943410	0.720237	0.543442	0.385631	0.768054	0.591755	0.730742	0.529900	0.294391	0.495960	...	0.547509	0.818871	0.806232	0.442937	0.440860	0.169447	0.584755	0.436921	0.006258	0.619964
13	0.179326	0.760713	0.182186	0.670680	0.678732	0.761327	0.428410	0.779151	0.747744	0.599381	...	0.484679	0.529199	0.928070	0.294489	0.440652	0.106265	0.170487	0.639856	0.519882	0.987917
14	0.056025	0.629802	0.977545	0.658098	0.862397	0.123460	0.949861	0.059172	0.615887	0.450150	...	0.324923	0.156255	0.745297	0.299434	0.566796	0.086708	0.685972	0.187975	0.770196	0.805439
15	0.512351	0.653988	0.769904	0.990763	0.157606	0.412472	0.467803	0.710103	0.879640	0.745679	...	0.483111	0.023145	0.404432	0.331049	0.769805	0.401911	0.982737	0.708876	0.557452	0.350600
16	0.851925	0.700177	0.762888	0.314232	0.394194	0.009929	0.567694	0.887770	0.459956	0.384007	...	0.877070	0.578805	0.682054	0.817754	0.187930	0.815588	0.288723	0.759027	0.307923	0.385493
17	0.620958	0.787456	0.390768	0.055931	0.775903	0.779081	0.698823	0.160972	0.860040	0.983636	...	0.162996	0.808032	0.818766	0.665004	0.782833	0.460531	0.219354	0.643656	0.088890	0.051677
18	0.123906	0.231519	0.667075	0.008801	0.404813	0.241242	0.262940	0.560614	0.542335	0.521399	...	0.534192	0.329566	0.189557	0.818821	0.545051	0.410484	0.593889	0.542941	0.949775	0.887700
19	0.897231	0.290945	0.371099	0.648910	0.388507	0.892250	0.640442	0.429782	0.228143	0.748801	...	0.867375	0.589733	0.661740	0.532929	0.042675	0.818236	0.291838	0.270660	0.576204	0.596013

20 rows × 40 columns

Use seaborn

import seaborn as sns

Works quite well with small arrays.

sns.heatmap(np.random.rand(3, 5), annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x12097a110>

... but the solution doesn't scale well:

sns.heatmap(np.random.rand(20, 40), annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x11feed650>

This is why ndpretty was born.