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dnanper
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dscoder_lib_usage_2

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train · 146 rows

usage_count int64 1 15.9k	rank int64 1 146	percentage_of_total float64 0 38.5	examples_count int64 1 15.9k	name stringlengths 2 22
15,892	1	38.524193	15,892	tensorflow
12,410	2	30.08339	12,410	sklearn
4,458	3	10.806749	4,458	pandas
2,607	4	6.319694	2,607	matplotlib
2,542	5	6.162125	2,542	numpy
1,319	6	3.197421	1,319	torch
486	7	1.178125	486	nltk
95	8	0.230292	95	flask
62	9	0.150296	62	re
60	10	0.145447	60	os
58	11	0.140599	58	transformers
57	12	0.138175	57	requests
55	13	0.133327	55	tkinter
52	14	0.126054	52	torchvision
50	15	0.121206	50	random
49	16	0.118782	49	gym
49	17	0.118782	49	plotly
41	18	0.099389	41	tflearn
34	19	0.08242	34	json
31	20	0.075148	31	scipy
31	21	0.075148	31	collections
30	22	0.072724	30	seaborn
29	23	0.0703	29	streamlit
28	24	0.067875	28	xgboost
27	25	0.065451	27	tweepy
24	26	0.058179	24	torchtext
24	27	0.058179	24	bs4
23	28	0.055755	23	datetime
20	29	0.048482	20	librosa
20	30	0.048482	20	string
19	31	0.046058	19	http
18	32	0.043634	18	gensim
17	33	0.04121	17	deap
16	34	0.038786	16	argparse
15	35	0.036362	15	pickle
14	36	0.033938	14	socketserver
14	37	0.033938	14	tensorflow_hub
14	38	0.033938	14	mpl_toolkits
14	39	0.033938	14	skopt
13	40	0.031514	13	sqlite3
12	41	0.029089	12	spacy
12	42	0.029089	12	statsmodels
11	43	0.026665	11	sys
11	44	0.026665	11	cv2
10	45	0.024241	10	wordcloud
10	46	0.024241	10	phoebe
10	47	0.024241	10	networkx
10	48	0.024241	10	google
10	49	0.024241	10	math
10	50	0.024241	10	psycopg2
9	51	0.021817	9	time
9	52	0.021817	9	textblob
9	53	0.021817	9	pyautogui
9	54	0.021817	9	mido
9	55	0.021817	9	imblearn
8	56	0.019393	8	thriftpy
8	57	0.019393	8	Utils
8	58	0.019393	8	bokeh
8	59	0.019393	8	pyemma
7	60	0.016969	7	tensorflow_datasets
7	61	0.016969	7	io
7	62	0.016969	7	skimage
7	63	0.016969	7	qrcode
6	64	0.014545	6	pandas_datareader
6	65	0.014545	6	selenium
6	66	0.014545	6	tolt
6	67	0.014545	6	PIL
6	68	0.014545	6	simpleml
6	69	0.014545	6	dialogflow_v2
5	70	0.012121	5	graphviz
5	71	0.012121	5	inpututils
5	72	0.012121	5	autokeras
5	73	0.012121	5	describe
5	74	0.012121	5	joblib
5	75	0.012121	5	smtplib
5	76	0.012121	5	surprise
5	77	0.012121	5	tensorflow_addons
5	78	0.012121	5	mlxtend
4	79	0.009696	4	pymongo
4	80	0.009696	4	email
4	81	0.009696	4	gpt_2_simple
4	82	0.009696	4	stable_baselines
4	83	0.009696	4	rasa_nlu
4	84	0.009696	4	speech_recognition
4	85	0.009696	4	boto3
4	86	0.009696	4	django
4	87	0.009696	4	kerastuner
4	88	0.009696	4	PyPDF2
3	89	0.007272	3	yfinance
3	90	0.007272	3	pyodbc
3	91	0.007272	3	RForest
3	92	0.007272	3	mpi4py
3	93	0.007272	3	pycas
3	94	0.007272	3	dash
3	95	0.007272	3	bayes_opt
3	96	0.007272	3	sklearn_crfsuite
3	97	0.007272	3	flask_restful
3	98	0.007272	3	sqlalchemy
3	99	0.007272	3	evolutionary_search
2	100	0.004848	2	ga

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