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dnanper
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dscoder_lib_usage

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train · 302 rows

usage_count int64 1 49.5k	rank int64 1 156	percentage_of_total float64 0 38.5	examples_count int64 1 44.5k	name stringlengths 2 22
49,454	1	20.360322	44,469	sklearn
47,443	2	19.532389	38,448	tensorflow
24,113	3	9.927376	22,930	torch
19,073	4	7.852397	16,200	numpy
17,693	5	7.284247	17,311	torchtext
16,361	6	6.73586	16,315	pandas_datareader
13,401	7	5.517222	12,988	matplotlib
12,650	8	5.208033	10,773	pandas
4,242	9	1.746441	3,792	nltk
3,725	10	1.533591	3,479	torchvision
3,599	11	1.481716	3,569	bokeh
2,548	12	1.049017	2,548	random
2,351	13	0.967912	2,167	scipy
1,538	14	0.633198	1,538	time
1,405	15	0.578442	1,394	gensim
1,342	16	0.552504	1,342	django
1,112	17	0.457813	1,083	joblib
1,105	18	0.454931	1,104	Utils
972	19	0.400175	951	transformers
967	20	0.398116	967	fabric
866	21	0.356534	866	evaluate
851	22	0.350359	814	math
819	23	0.337184	819	ggplot
792	24	0.326068	792	tkinter
760	25	0.312894	760	seaborn
719	26	0.296014	694	deap
705	27	0.29025	705	pylab
705	28	0.29025	705	read_binary
694	29	0.285721	694	re
689	30	0.283663	688	skimage
611	31	0.25155	611	string
517	32	0.21285	517	cv2
468	33	0.192677	468	rasa_nlu
458	34	0.18856	458	json
433	35	0.178267	429	itertools
423	36	0.17415	414	flask
414	37	0.170445	399	datetime
402	38	0.165504	402	os
395	39	0.162622	395	requests
382	40	0.15727	381	surprise
356	41	0.146566	355	plotly
315	42	0.129686	265	collections
287	43	0.118159	287	AdaptivePELE
252	44	0.103749	252	dash_html_components
230	45	0.094692	230	PIL
225	46	0.092633	225	IPython
223	47	0.09181	223	bert
201	48	0.082752	198	statsmodels
169	49	0.069578	168	selenium
160	50	0.065872	160	phoebe
156	51	0.064226	156	psycopg2
134	52	0.055168	134	google
132	53	0.054345	132	bs4
115	54	0.047346	112	wordcloud
114	55	0.046934	114	networkx
101	56	0.041582	101	gpt_2_simple
100	57	0.04117	100	librosa
93	58	0.038288	93	spacy
88	59	0.03623	81	xgboost
87	60	0.035818	87	skopt
86	61	0.035406	64	sys
86	62	0.035406	86	textblob
82	63	0.03376	82	boto3
81	64	0.033348	73	pymongo
79	65	0.032524	79	tflearn
78	66	0.032113	78	cassandra
74	67	0.030466	74	io
72	68	0.029643	72	imblearn
69	69	0.028407	69	gym
69	70	0.028407	69	Simulator
65	71	0.026761	63	sqlalchemy
61	72	0.025114	61	pickle
61	73	0.025114	61	streamlit
55	74	0.022644	55	gpflow
51	75	0.020997	51	sqlite3
48	76	0.019762	48	argparse
47	77	0.01935	47	pyodbc
44	78	0.018115	44	gspread
41	79	0.01688	41	multiprocessing
40	80	0.016468	40	tweepy
39	81	0.016056	39	evaluate_model
39	82	0.016056	39	simpleml
35	83	0.01441	35	wave
33	84	0.013586	33	tensorflow_hub
30	85	0.012351	30	__future__
28	86	0.011528	28	tensorflow_datasets
28	87	0.011528	28	http
28	88	0.011528	28	pymysql
26	89	0.010704	26	describe
25	90	0.010293	25	tqdm
25	91	0.010293	25	mysql
24	92	0.009881	24	dash
24	93	0.009881	24	dash_core_components
23	94	0.009469	23	warnings
22	95	0.009057	22	mido
21	96	0.008646	21	urllib
20	97	0.008234	20	mpl_toolkits
19	98	0.007822	19	read_data
19	99	0.007822	19	geopandas
17	100	0.006999	17	speech_recognition

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