diff --git a/.gitignore b/.gitignore
index e69de29..b694934 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
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diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md
new file mode 100644
index 0000000..a60530d
--- /dev/null
+++ b/.ipynb_checkpoints/README-checkpoint.md
@@ -0,0 +1 @@
+Горочный Григорий ИНБб-2301
\ No newline at end of file
diff --git a/.ipynb_checkpoints/data-checkpoint.csv b/.ipynb_checkpoints/data-checkpoint.csv
new file mode 100644
index 0000000..f82f608
--- /dev/null
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+Country Name,Country Code,Year,Value
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+Early-demographic dividend,EAR,1963,161801029341.01468
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+Early-demographic dividend,EAR,1984,1438397582849.1045
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+High income,HIC,1976,5316705508568.393
+High income,HIC,1977,6017649327457.811
+High income,HIC,1978,7224423199614.203
+High income,HIC,1979,8349033768623.11
+High income,HIC,1980,9318973466251.5
+High income,HIC,1981,9463529620224.12
+High income,HIC,1982,9370788287370.254
+High income,HIC,1983,9680375566671.547
+High income,HIC,1984,10091350207473.389
+High income,HIC,1985,10589659647967.176
+High income,HIC,1986,12863823517433.559
+High income,HIC,1987,14914230823362.945
+High income,HIC,1988,16785555755731.361
+High income,HIC,1989,17481687412866.576
+High income,HIC,1990,19577046428304.496
+High income,HIC,1991,20659797917552.074
+High income,HIC,1992,22119166125282.914
+High income,HIC,1993,22272082420753.633
+High income,HIC,1994,23818722465725.473
+High income,HIC,1995,26430046395918.6
+High income,HIC,1996,26643709351548.746
+High income,HIC,1997,26208151153812.51
+High income,HIC,1998,26174130826663.06
+High income,HIC,1999,27403905274743.547
+High income,HIC,2000,28036716903140.23
+High income,HIC,2001,27811072270318.094
+High income,HIC,2002,29022750091985.18
+High income,HIC,2003,32574954634084.754
+High income,HIC,2004,36387608557612.22
+High income,HIC,2005,38623901277744.24
+High income,HIC,2006,40986059390577.016
+High income,HIC,2007,45098647855102.445
+High income,HIC,2008,48338201243505.67
+High income,HIC,2009,44940316093124.67
+High income,HIC,2010,47340327876455.97
+High income,HIC,2011,51524951092552.39
+High income,HIC,2012,51710003724530.76
+High income,HIC,2013,52439877375602.58
+High income,HIC,2014,53310162826089.13
+High income,HIC,2015,49773105280474.15
+High income,HIC,2016,50737236574099.64
+High income,HIC,2017,53336788717601.586
+High income,HIC,2018,56684556889445.77
+High income,HIC,2019,57218295248360.38
+High income,HIC,2020,55791128824226.22
+High income,HIC,2021,62461025675219.586
+High income,HIC,2022,64444446480056.59
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+Honduras,HND,1960,335650000
+Honduras,HND,1961,356200000
+Honduras,HND,1962,387750000
+Honduras,HND,1963,410200000
+Honduras,HND,1964,457000000
+Honduras,HND,1965,508650000
+Honduras,HND,1966,549950000
+Honduras,HND,1967,598100000
+Honduras,HND,1968,646800000
+Honduras,HND,1969,668000050
+Honduras,HND,1970,723000000
+Honduras,HND,1971,731000000
+Honduras,HND,1972,802996336.5164857
+Honduras,HND,1973,912499493.7502531
+Honduras,HND,1974,1034500000
+Honduras,HND,1975,1124000000
+Honduras,HND,1976,1347999950
+Honduras,HND,1977,1669499950
+Honduras,HND,1978,3097242093.2
+Honduras,HND,1979,3544281976.3
+Honduras,HND,1980,3968160046
+Honduras,HND,1981,4043894878.6
+Honduras,HND,1982,4266503525.6
+Honduras,HND,1983,4476697184.849999
+Honduras,HND,1984,4915311846.5
+Honduras,HND,1985,5278120712.5
+Honduras,HND,1986,5677828959
+Honduras,HND,1987,6190521241.5
+Honduras,HND,1988,5902717091.416308
+Honduras,HND,1989,5432344901.724138
+Honduras,HND,1990,4923009551.55642
+Honduras,HND,1991,4648668478.567533
+Honduras,HND,1992,4943700431.073683
+Honduras,HND,1993,4926728932.950691
+Honduras,HND,1994,4642280682.142518
+Honduras,HND,1995,5347445005.213764
+Honduras,HND,1996,5215028986.486486
+Honduras,HND,1997,5737099650.190114
+Honduras,HND,1998,6366340265.878878
+Honduras,HND,1999,6414520529.616725
+Honduras,HND,2000,7186638029.058749
+Honduras,HND,2001,7651162302.345604
+Honduras,HND,2002,7858255412.860379
+Honduras,HND,2003,8230391346.672281
+Honduras,HND,2004,8869299234.337696
+Honduras,HND,2005,9757012696.562117
+Honduras,HND,2006,10917477066.142908
+Honduras,HND,2007,12361257680.562687
+Honduras,HND,2008,13881731875.746613
+Honduras,HND,2009,14587496229.18111
+Honduras,HND,2010,15839344591.984165
+Honduras,HND,2011,17710275685.407448
+Honduras,HND,2012,18528554397.56951
+Honduras,HND,2013,18499729214.904022
+Honduras,HND,2014,19756533971.869846
+Honduras,HND,2015,20979791685.416042
+Honduras,HND,2016,21717604952.183525
+Honduras,HND,2017,23136247990.597725
+Honduras,HND,2018,24067750760.49897
+Honduras,HND,2019,24882225741.811867
+Honduras,HND,2020,23352232483.75501
+Honduras,HND,2021,28144331506.614887
+Honduras,HND,2022,31426041806.798485
+Honduras,HND,2023,34400509852.04365
+"Hong Kong SAR, China",HKG,1960,1320796651.694569
+"Hong Kong SAR, China",HKG,1961,1383681651.1377556
+"Hong Kong SAR, China",HKG,1962,1612346412.2647462
+"Hong Kong SAR, China",HKG,1963,1935298266.45384
+"Hong Kong SAR, China",HKG,1964,2206466461.264338
+"Hong Kong SAR, China",HKG,1965,2435078534.0314136
+"Hong Kong SAR, China",HKG,1966,2489845016.6489425
+"Hong Kong SAR, China",HKG,1967,2692474989.125707
+"Hong Kong SAR, China",HKG,1968,2716964388.424184
+"Hong Kong SAR, China",HKG,1969,3189740055.1398187
+"Hong Kong SAR, China",HKG,1970,3800766535.6208773
+"Hong Kong SAR, China",HKG,1971,4476001946.014864
+"Hong Kong SAR, China",HKG,1972,5710090213.150349
+"Hong Kong SAR, China",HKG,1973,8030117555.620325
+"Hong Kong SAR, China",HKG,1974,9388695366.845781
+"Hong Kong SAR, China",HKG,1975,10048089558.063587
+"Hong Kong SAR, China",HKG,1976,12876366008.8077
+"Hong Kong SAR, China",HKG,1977,15719433719.43372
+"Hong Kong SAR, China",HKG,1978,18315136409.142666
+"Hong Kong SAR, China",HKG,1979,22526184533.17001
+"Hong Kong SAR, China",HKG,1980,28861857810.65147
+"Hong Kong SAR, China",HKG,1981,31055226088.694305
+"Hong Kong SAR, China",HKG,1982,32291215843.64333
+"Hong Kong SAR, China",HKG,1983,29907227184.178974
+"Hong Kong SAR, China",HKG,1984,33511383985.674088
+"Hong Kong SAR, China",HKG,1985,35699772165.709335
+"Hong Kong SAR, China",HKG,1986,41075396884.89521
+"Hong Kong SAR, China",HKG,1987,50622896162.60058
+"Hong Kong SAR, China",HKG,1988,59707404560.59441
+"Hong Kong SAR, China",HKG,1989,68790219177.97331
+"Hong Kong SAR, China",HKG,1990,76928784620.81581
+"Hong Kong SAR, China",HKG,1991,88959997899.92932
+"Hong Kong SAR, China",HKG,1992,104272507639.28247
+"Hong Kong SAR, China",HKG,1993,120354212475.00026
+"Hong Kong SAR, China",HKG,1994,135811771026.33049
+"Hong Kong SAR, China",HKG,1995,144652295363.66672
+"Hong Kong SAR, China",HKG,1996,159718183550.73416
+"Hong Kong SAR, China",HKG,1997,177353174849.7142
+"Hong Kong SAR, China",HKG,1998,168885443660.07248
+"Hong Kong SAR, China",HKG,1999,165768095391.55655
+"Hong Kong SAR, China",HKG,2000,171668891194.3487
+"Hong Kong SAR, China",HKG,2001,169404327616.60522
+"Hong Kong SAR, China",HKG,2002,166348866131.0282
+"Hong Kong SAR, China",HKG,2003,161385558801.8108
+"Hong Kong SAR, China",HKG,2004,169099768875.1926
+"Hong Kong SAR, China",HKG,2005,181569311742.21292
+"Hong Kong SAR, China",HKG,2006,193535442896.36505
+"Hong Kong SAR, China",HKG,2007,211596944503.79977
+"Hong Kong SAR, China",HKG,2008,219278749139.7337
+"Hong Kong SAR, China",HKG,2009,214047795659.0447
+"Hong Kong SAR, China",HKG,2010,228638668727.29083
+"Hong Kong SAR, China",HKG,2011,248513617677.28674
+"Hong Kong SAR, China",HKG,2012,262628865879.69678
+"Hong Kong SAR, China",HKG,2013,275696879834.9665
+"Hong Kong SAR, China",HKG,2014,291459995978.8927
+"Hong Kong SAR, China",HKG,2015,309385622601.3481
+"Hong Kong SAR, China",HKG,2016,320860317562.5624
+"Hong Kong SAR, China",HKG,2017,341273289534.46594
+"Hong Kong SAR, China",HKG,2018,361731070995.72626
+"Hong Kong SAR, China",HKG,2019,363074545072.3891
+"Hong Kong SAR, China",HKG,2020,344943149590.0583
+"Hong Kong SAR, China",HKG,2021,368954169748.81805
+"Hong Kong SAR, China",HKG,2022,358696261481.1598
+"Hong Kong SAR, China",HKG,2023,382054574298.52905
+Hungary,HUN,1968,4870283626.180433
+Hungary,HUN,1969,5412100260.1730995
+Hungary,HUN,1970,5762071728.1237335
+Hungary,HUN,1971,6271045034.468295
+Hungary,HUN,1972,7355242314.224068
+Hungary,HUN,1973,9108483160.503698
+Hungary,HUN,1974,9983664739.3203
+Hungary,HUN,1975,11383139029.43452
+Hungary,HUN,1976,13192437321.244184
+Hungary,HUN,1977,14735449496.728897
+Hungary,HUN,1978,17230354544.824234
+Hungary,HUN,1979,19894622393.194042
+Hungary,HUN,1980,23041569234.97921
+Hungary,HUN,1981,23628554956.605247
+Hungary,HUN,1982,24062916721.31206
+Hungary,HUN,1983,21838893330.60519
+Hungary,HUN,1984,21173432184.871105
+Hungary,HUN,1985,21440475719.858223
+Hungary,HUN,1986,24697337084.10124
+Hungary,HUN,1987,27143185296.5443
+Hungary,HUN,1988,29702631093.881485
+Hungary,HUN,1989,30323270274.405884
+Hungary,HUN,1990,34365891768.68776
+Hungary,HUN,1991,34753569692.91496
+Hungary,HUN,1992,38730585922.218864
+Hungary,HUN,1993,40124916940.59447
+Hungary,HUN,1994,43166678735.21832
+Hungary,HUN,1995,46425677734.14352
+Hungary,HUN,1996,46658755151.6017
+Hungary,HUN,1997,47296952928.75605
+Hungary,HUN,1998,48706787306.25736
+Hungary,HUN,1999,49073380173.71591
+Hungary,HUN,2000,47218405892.42581
+Hungary,HUN,2001,53749989092.01972
+Hungary,HUN,2002,67608919144.368355
+Hungary,HUN,2003,85285062818.00766
+Hungary,HUN,2004,104120820258.66873
+Hungary,HUN,2005,113211158292.9365
+Hungary,HUN,2006,115715618613.05196
+Hungary,HUN,2007,140186716681.42496
+Hungary,HUN,2008,158325614580.628
+Hungary,HUN,2009,131069255620.56659
+Hungary,HUN,2010,132175349953.71332
+Hungary,HUN,2011,141942264554.47513
+Hungary,HUN,2012,128814279315.1317
+Hungary,HUN,2013,135684315697.71341
+Hungary,HUN,2014,141033843265.66858
+Hungary,HUN,2015,125174166987.37169
+Hungary,HUN,2016,128609822750.03862
+Hungary,HUN,2017,143112196040.32568
+Hungary,HUN,2018,160565642983.58676
+Hungary,HUN,2019,164020460331.65897
+Hungary,HUN,2020,157288955508.17633
+Hungary,HUN,2021,182109999477.69073
+Hungary,HUN,2022,177006128624.62692
+Hungary,HUN,2023,212388906458.72394
+IBRD only,IBD,1960,326647514998.4808
+IBRD only,IBD,1961,322098726641.68274
+IBRD only,IBD,1962,327041835204.80804
+IBRD only,IBD,1963,361188678549.8145
+IBRD only,IBD,1964,409917161582.3877
+IBRD only,IBD,1965,449606624512.9089
+IBRD only,IBD,1966,455110460848.8413
+IBRD only,IBD,1967,474061106039.631
+IBRD only,IBD,1968,500737527389.4028
+IBRD only,IBD,1969,557158810956.071
+IBRD only,IBD,1970,606864433924.8542
+IBRD only,IBD,1971,658436487864.558
+IBRD only,IBD,1972,741446464309.9623
+IBRD only,IBD,1973,935257264659.15
+IBRD only,IBD,1974,1166117926870.0496
+IBRD only,IBD,1975,1283182211382.1401
+IBRD only,IBD,1976,1384184182987.9087
+IBRD only,IBD,1977,1572414965445.1057
+IBRD only,IBD,1978,1688541859448.7246
+IBRD only,IBD,1979,2054980853162.8042
+IBRD only,IBD,1980,2495337530229.9053
+IBRD only,IBD,1981,2679716239809.967
+IBRD only,IBD,1982,2598534706334.6484
+IBRD only,IBD,1983,2671753233490.0737
+IBRD only,IBD,1984,2773573376450.62
+IBRD only,IBD,1985,2856094151218.432
+IBRD only,IBD,1986,2906164344615.2295
+IBRD only,IBD,1987,2962762817058.3257
+IBRD only,IBD,1988,3156451762787.7754
+IBRD only,IBD,1989,3174083997481.793
+IBRD only,IBD,1990,3560303900038.582
+IBRD only,IBD,1991,3448774162440.4175
+IBRD only,IBD,1992,3624577197344.4175
+IBRD only,IBD,1993,3934211426064.5195
+IBRD only,IBD,1994,4339185750582.6406
+IBRD only,IBD,1995,4916948102663.379
+IBRD only,IBD,1996,5368909819962.314
+IBRD only,IBD,1997,5694529340364.156
+IBRD only,IBD,1998,5541398163455.25
+IBRD only,IBD,1999,5397562849862.584
+IBRD only,IBD,2000,5873248460100.093
+IBRD only,IBD,2001,5954917913637.387
+IBRD only,IBD,2002,6043651266693.341
+IBRD only,IBD,2003,6788178855352.027
+IBRD only,IBD,2004,8085419494002.896
+IBRD only,IBD,2005,9657998889452.797
+IBRD only,IBD,2006,11453342785003.834
+IBRD only,IBD,2007,14221824723609.965
+IBRD only,IBD,2008,17060926940839.877
+IBRD only,IBD,2009,16541826277696.307
+IBRD only,IBD,2010,20111983284711.48
+IBRD only,IBD,2011,23807285173177.438
+IBRD only,IBD,2012,25304734948065.89
+IBRD only,IBD,2013,26693510662628.867
+IBRD only,IBD,2014,27592783808400.59
+IBRD only,IBD,2015,25838353087075.664
+IBRD only,IBD,2016,26027770587219.1
+IBRD only,IBD,2017,28745223958869.04
+IBRD only,IBD,2018,30579316565085.14
+IBRD only,IBD,2019,31246593993598.37
+IBRD only,IBD,2020,30103859045527.812
+IBRD only,IBD,2021,35740404315658.11
+IBRD only,IBD,2022,37733201523901.375
+IBRD only,IBD,2023,38733043068564.85
+Iceland,ISL,1960,252169712.04212478
+Iceland,ISL,1961,257703244.55999702
+Iceland,ISL,1962,289200704.3825417
+Iceland,ISL,1963,345175036.09055525
+Iceland,ISL,1964,440797869.28226596
+Iceland,ISL,1965,531569563.88859105
+Iceland,ISL,1966,638349755.2219319
+Iceland,ISL,1967,630567115.7907317
+Iceland,ISL,1968,481532847.5905867
+Iceland,ISL,1969,420945147.6309565
+Iceland,ISL,1970,526704545.45454544
+Iceland,ISL,1971,670251136.3636364
+Iceland,ISL,1972,839652164.0607296
+Iceland,ISL,1973,1154440252.9679353
+Iceland,ISL,1974,1515190595.2976487
+Iceland,ISL,1975,1406875081.327261
+Iceland,ISL,1976,1669488389.965417
+Iceland,ISL,1977,2208509076.2809877
+Iceland,ISL,1978,2511826196.009
+Iceland,ISL,1979,2853435053.8854227
+Iceland,ISL,1980,3381419251.2923126
+Iceland,ISL,1981,3492997010.049556
+Iceland,ISL,1982,3206626644.537101
+Iceland,ISL,1983,2765950335.7109504
+Iceland,ISL,1984,2864441387.405068
+Iceland,ISL,1985,2984052356.550712
+Iceland,ISL,1986,3989622739.2821174
+Iceland,ISL,1987,5520318404.641495
+Iceland,ISL,1988,6106635816.2458725
+Iceland,ISL,1989,5672569449.070682
+Iceland,ISL,1990,6468736355.556776
+Iceland,ISL,1991,6909730288.170614
+Iceland,ISL,1992,7080981738.056056
+Iceland,ISL,1993,6218581531.643473
+Iceland,ISL,1994,6389460342.786556
+Iceland,ISL,1995,7123633418.197389
+Iceland,ISL,1996,7426082270.676692
+Iceland,ISL,1997,7569672925.337391
+Iceland,ISL,1998,8503693098.622713
+Iceland,ISL,1999,8982047589.489502
+Iceland,ISL,2000,9025660361.75842
+Iceland,ISL,2001,8234846804.605819
+Iceland,ISL,2002,9318395054.859337
+Iceland,ISL,2003,11429333037.84432
+Iceland,ISL,2004,13825302535.7699
+Iceland,ISL,2005,16852963067.049637
+Iceland,ISL,2006,17465318552.294098
+Iceland,ISL,2007,21652505596.75279
+Iceland,ISL,2008,18074622987.01845
+Iceland,ISL,2009,13154414219.206978
+Iceland,ISL,2010,13751161917.739773
+Iceland,ISL,2011,15221622925.931889
+Iceland,ISL,2012,14751508133.544277
+Iceland,ISL,2013,16125060515.31174
+Iceland,ISL,2014,17867662177.89113
+Iceland,ISL,2015,17517210519.091156
+Iceland,ISL,2016,20793168030.952427
+Iceland,ISL,2017,24728285177.460316
+Iceland,ISL,2018,26260850582.06868
+Iceland,ISL,2019,24681343649.29519
+Iceland,ISL,2020,21629953194.065945
+Iceland,ISL,2021,25797870984.2963
+Iceland,ISL,2022,28701830401.683697
+Iceland,ISL,2023,31020032583.197216
+IDA & IBRD total,IBT,1960,369468196078.4826
+IDA & IBRD total,IBT,1961,369197803239.24896
+IDA & IBRD total,IBT,1962,377767061032.32935
+IDA & IBRD total,IBT,1963,413669388135.15784
+IDA & IBRD total,IBT,1964,465199740731.88116
+IDA & IBRD total,IBT,1965,510173811321.20984
+IDA & IBRD total,IBT,1966,521460612081.27454
+IDA & IBRD total,IBT,1967,542785781834.8444
+IDA & IBRD total,IBT,1968,573059713397.6846
+IDA & IBRD total,IBT,1969,637528711300.9677
+IDA & IBRD total,IBT,1970,698056722723.8445
+IDA & IBRD total,IBT,1971,747346980067.8623
+IDA & IBRD total,IBT,1972,834442609498.4454
+IDA & IBRD total,IBT,1973,1038598297107.5975
+IDA & IBRD total,IBT,1974,1305873068194.0867
+IDA & IBRD total,IBT,1975,1454096413943.2312
+IDA & IBRD total,IBT,1976,1562102647645.7788
+IDA & IBRD total,IBT,1977,1759459705916.4446
+IDA & IBRD total,IBT,1978,1904189629796.481
+IDA & IBRD total,IBT,1979,2302082724432.5146
+IDA & IBRD total,IBT,1980,2791782941444.5557
+IDA & IBRD total,IBT,1981,3154989805210.8438
+IDA & IBRD total,IBT,1982,3046965333072.1987
+IDA & IBRD total,IBT,1983,3040184802522.8804
+IDA & IBRD total,IBT,1984,3114447962164.7944
+IDA & IBRD total,IBT,1985,3210382959422.716
+IDA & IBRD total,IBT,1986,3213192269731.733
+IDA & IBRD total,IBT,1987,3281409649813.81
+IDA & IBRD total,IBT,1988,3493680607335.671
+IDA & IBRD total,IBT,1989,3516679380808.702
+IDA & IBRD total,IBT,1990,3938241140458.3735
+IDA & IBRD total,IBT,1991,3855769843542.668
+IDA & IBRD total,IBT,1992,3988433395055.7295
+IDA & IBRD total,IBT,1993,4316120552669.1807
+IDA & IBRD total,IBT,1994,4734478573420.383
+IDA & IBRD total,IBT,1995,5396987083280.384
+IDA & IBRD total,IBT,1996,5921564677285.115
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+IDA & IBRD total,IBT,1998,6143311465263.654
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+IDA & IBRD total,IBT,2001,6465885572313.3955
+IDA & IBRD total,IBT,2002,6593953285638.37
+IDA & IBRD total,IBT,2003,7408766752494.036
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+IDA & IBRD total,IBT,2005,10499926993564.467
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+IDA & IBRD total,IBT,2007,15401421654907.701
+IDA & IBRD total,IBT,2008,18465834149693.492
+IDA & IBRD total,IBT,2009,17913122960002.152
+IDA & IBRD total,IBT,2010,21693456395830.336
+IDA & IBRD total,IBT,2011,25594615218755.266
+IDA & IBRD total,IBT,2012,27188545719649.605
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+IDA & IBRD total,IBT,2014,29800896836060.047
+IDA & IBRD total,IBT,2015,27987288576613.15
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+IDA & IBRD total,IBT,2018,32964096579686.016
+IDA & IBRD total,IBT,2019,33722356189565.855
+IDA & IBRD total,IBT,2020,32528587315534.145
+IDA & IBRD total,IBT,2021,38378968351134.22
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+IDA blend,IDB,1960,12304340519.437931
+IDA blend,IDB,1961,13173849239.235147
+IDA blend,IDB,1962,14087733811.174345
+IDA blend,IDB,1963,14916251701.126812
+IDA blend,IDB,1964,16283554669.880676
+IDA blend,IDB,1965,17681807373.14332
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+IDA blend,IDB,1967,19334017378.779907
+IDA blend,IDB,1968,20403706829.551464
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+IDA blend,IDB,1970,32052680442.7769
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+IDA blend,IDB,1972,33470488972.201614
+IDA blend,IDB,1973,35789060006.8562
+IDA blend,IDB,1974,51518189062.43885
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+IDA blend,IDB,1976,71358695957.48854
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+IDA blend,IDB,1978,81846895071.4822
+IDA blend,IDB,1979,100444794858.7986
+IDA blend,IDB,1980,128870008364.99786
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+IDA blend,IDB,1982,226070570770.67218
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+IDA blend,IDB,1985,148005975332.56854
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+IDA blend,IDB,1987,136898130940.9204
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+IDA blend,IDB,1999,183774429526.0243
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+IDA blend,IDB,2008,677188107675.7102
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+IDA only,IDX,1977,109704597324.3775
+IDA only,IDX,1978,127591069020.59163
+IDA only,IDX,1979,143005783535.95557
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+IDA only,IDX,1981,170754419012.08243
+IDA only,IDX,1982,171592411137.81265
+IDA only,IDX,1983,174451138973.80054
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+IDA only,IDX,1985,194539320065.9348
+IDA only,IDX,1986,174407396608.2547
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+IDA only,IDX,1989,201851599985.29355
+IDA only,IDX,1990,232816355943.8044
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+IDA only,IDX,1994,213964030903.19003
+IDA only,IDX,1995,224492421427.76117
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+IDA only,IDX,2004,386660282055.16534
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+IDA only,IDX,2008,727719101177.9065
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+IDA total,IDA,1961,44012577180.432236
+IDA total,IDA,1962,46765307423.11745
+IDA total,IDA,1963,49114642121.75161
+IDA total,IDA,1964,52679532945.22846
+IDA total,IDA,1965,57731457660.490746
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+IDA total,IDA,1967,64429850042.316795
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+IDA total,IDA,1970,84861825817.92558
+IDA total,IDA,1971,84960443693.54733
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+IDA total,IDA,1974,137918904112.60803
+IDA total,IDA,1975,163903050188.54425
+IDA total,IDA,1976,172256247640.56158
+IDA total,IDA,1977,185053978781.2845
+IDA total,IDA,1978,209143768718.97714
+IDA total,IDA,1979,243716595033
+IDA total,IDA,1980,293400954626.97705
+IDA total,IDA,1981,426223819976.08484
+IDA total,IDA,1982,404486564860.62445
+IDA total,IDA,1983,349645458108.88873
+IDA total,IDA,1984,331449107693.95746
+IDA total,IDA,1985,343779195534.5611
+IDA total,IDA,1986,309190328533.65027
+IDA total,IDA,1987,319495458117.7952
+IDA total,IDA,1988,338669681386.6566
+IDA total,IDA,1989,343828929532.5688
+IDA total,IDA,1990,378856660652.6743
+IDA total,IDA,1991,409092280613.03076
+IDA total,IDA,1992,364758759974.76874
+IDA total,IDA,1993,382908861851.4712
+IDA total,IDA,1994,395889357665.3764
+IDA total,IDA,1995,481341765046.5578
+IDA total,IDA,1996,554633517949.8015
+IDA total,IDA,1997,580664386042.9703
+IDA total,IDA,1998,604570449948.6671
+IDA total,IDA,1999,450872755018.96246
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+IDA total,IDA,2001,511239299618.94403
+IDA total,IDA,2002,550995965118.9045
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+IDA total,IDA,2004,726522702342.7588
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+IDA total,IDA,2008,1404907208853.6165
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+IDA total,IDA,2012,1883810771583.7258
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+"Iran, Islamic Rep.",IRN,1964,5379845647.7013855
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+"Iran, Islamic Rep.",IRN,1967,7555383690.16
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+"Iran, Islamic Rep.",IRN,1969,9743089607.495869
+"Iran, Islamic Rep.",IRN,1970,10976245153.589386
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+"Iran, Islamic Rep.",IRN,1972,17153463263.108646
+"Iran, Islamic Rep.",IRN,1973,27081698249.508286
+"Iran, Islamic Rep.",IRN,1974,46209092072.13827
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+"Iran, Islamic Rep.",IRN,1977,80600122701.9632
+"Iran, Islamic Rep.",IRN,1978,77994316621.48138
+"Iran, Islamic Rep.",IRN,1979,90391877325.9285
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+"Iran, Islamic Rep.",IRN,1982,125948756439.48515
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+Late-demographic dividend,LTE,1975,788838956828.1168
+Late-demographic dividend,LTE,1976,812873567659.8868
+Late-demographic dividend,LTE,1977,946458120173.975
+Late-demographic dividend,LTE,1978,939558602435.4497
+Late-demographic dividend,LTE,1979,1163005699434.633
+Late-demographic dividend,LTE,1980,1372119747166.9578
+Late-demographic dividend,LTE,1981,1415544964401.367
+Late-demographic dividend,LTE,1982,1400418314722.288
+Late-demographic dividend,LTE,1983,1461396318226.1274
+Late-demographic dividend,LTE,1984,1552335173187.2617
+Late-demographic dividend,LTE,1985,1660675505162.1038
+Late-demographic dividend,LTE,1986,1684896070685.4746
+Late-demographic dividend,LTE,1987,1746603588097.057
+Late-demographic dividend,LTE,1988,1907970208476.7786
+Late-demographic dividend,LTE,1989,1913194180510.283
+Late-demographic dividend,LTE,1990,1966143155409.277
+Late-demographic dividend,LTE,1991,1983419918605.2493
+Late-demographic dividend,LTE,1992,2022186054608.0432
+Late-demographic dividend,LTE,1993,2105333938710.2498
+Late-demographic dividend,LTE,1994,2442945412790.5786
+Late-demographic dividend,LTE,1995,3018292160017.546
+Late-demographic dividend,LTE,1996,3329594709957.7246
+Late-demographic dividend,LTE,1997,3486500370030.624
+Late-demographic dividend,LTE,1998,3361066467991.76
+Late-demographic dividend,LTE,1999,3106274396328.617
+Late-demographic dividend,LTE,2000,3410313487927.403
+Late-demographic dividend,LTE,2001,3525091029820.1104
+Late-demographic dividend,LTE,2002,3736796107267.6753
+Late-demographic dividend,LTE,2003,4257947205698.707
+Late-demographic dividend,LTE,2004,5117293242644.111
+Late-demographic dividend,LTE,2005,6172905218377.231
+Late-demographic dividend,LTE,2006,7448573743924.262
+Late-demographic dividend,LTE,2007,9382447414660.309
+Late-demographic dividend,LTE,2008,11663554908011.691
+Late-demographic dividend,LTE,2009,11269360521229.5
+Late-demographic dividend,LTE,2010,13564267760890.17
+Late-demographic dividend,LTE,2011,16530365963615.514
+Late-demographic dividend,LTE,2012,17680657970635.266
+Late-demographic dividend,LTE,2013,19042359863611.242
+Late-demographic dividend,LTE,2014,19776331354297.383
+Late-demographic dividend,LTE,2015,18550881244485.992
+Late-demographic dividend,LTE,2016,18593573738109.332
+Late-demographic dividend,LTE,2017,20631078375844.242
+Late-demographic dividend,LTE,2018,22623622462864.816
+Late-demographic dividend,LTE,2019,23050208314364.02
+Late-demographic dividend,LTE,2020,22531183291578.273
+Late-demographic dividend,LTE,2021,26872794398412.047
+Late-demographic dividend,LTE,2022,28062557325965.145
+Late-demographic dividend,LTE,2023,28341396914858.266
+Latin America & Caribbean,LCN,1989,1007476718921.5654
+Latin America & Caribbean,LCN,1990,1102119409745.3826
+Latin America & Caribbean,LCN,1991,1189975815419.2734
+Latin America & Caribbean,LCN,1992,1298654841940.8435
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+Latin America & Caribbean,LCN,1997,2303337106936.646
+Latin America & Caribbean,LCN,1998,2331650072097.7466
+Latin America & Caribbean,LCN,1999,2107238195958.9082
+Latin America & Caribbean,LCN,2000,2326171956618.827
+Latin America & Caribbean,LCN,2001,2281659001901.305
+Latin America & Caribbean,LCN,2002,2051651216847.5154
+Latin America & Caribbean,LCN,2003,2090649676533.063
+Latin America & Caribbean,LCN,2004,2404140329731.789
+Latin America & Caribbean,LCN,2005,2901840928620.712
+Latin America & Caribbean,LCN,2006,3395773013103.3794
+Latin America & Caribbean,LCN,2007,3997430459125.194
+Latin America & Caribbean,LCN,2008,4642685980825.886
+Latin America & Caribbean,LCN,2009,4355865396934.769
+Latin America & Caribbean,LCN,2010,5395521584867.198
+Latin America & Caribbean,LCN,2011,6137298312571.182
+Latin America & Caribbean,LCN,2012,6209024743500.111
+Latin America & Caribbean,LCN,2013,6362333301077.531
+Latin America & Caribbean,LCN,2014,6483202078515.132
+Latin America & Caribbean,LCN,2015,5418289197382.295
+Latin America & Caribbean,LCN,2016,5283310849435.272
+Latin America & Caribbean,LCN,2017,5866482345855.606
+Latin America & Caribbean,LCN,2018,5742638741431.275
+Latin America & Caribbean,LCN,2019,5659965575492.011
+Latin America & Caribbean,LCN,2020,4804924789054.883
+Latin America & Caribbean,LCN,2021,5578001135446.182
+Latin America & Caribbean,LCN,2022,6341680448313.757
+Latin America & Caribbean,LCN,2023,7094329636592.513
+Latin America & Caribbean (excluding high income),LAC,1989,876402463262.6858
+Latin America & Caribbean (excluding high income),LAC,1990,957593725702.9581
+Latin America & Caribbean (excluding high income),LAC,1991,1031036432080.7441
+Latin America & Caribbean (excluding high income),LAC,1992,1119017965058.758
+Latin America & Caribbean (excluding high income),LAC,1993,1348397983096.4402
+Latin America & Caribbean (excluding high income),LAC,1994,1592910332496.8948
+Latin America & Caribbean (excluding high income),LAC,1995,1697838269009.4563
+Latin America & Caribbean (excluding high income),LAC,1996,1853063931965.103
+Latin America & Caribbean (excluding high income),LAC,1997,2021893648691.252
+Latin America & Caribbean (excluding high income),LAC,1998,2038842311934.926
+Latin America & Caribbean (excluding high income),LAC,1999,1810327579938.6978
+Latin America & Caribbean (excluding high income),LAC,2000,2001496152611.1226
+Latin America & Caribbean (excluding high income),LAC,2001,1951435655114.4248
+Latin America & Caribbean (excluding high income),LAC,2002,1757081641119.6235
+Latin America & Caribbean (excluding high income),LAC,2003,1793945251035.7202
+Latin America & Caribbean (excluding high income),LAC,2004,2043684160277.3213
+Latin America & Caribbean (excluding high income),LAC,2005,2470502060472.195
+Latin America & Caribbean (excluding high income),LAC,2006,2881093926514.893
+Latin America & Caribbean (excluding high income),LAC,2007,3401597415158.732
+Latin America & Caribbean (excluding high income),LAC,2008,3931407891415.4766
+Latin America & Caribbean (excluding high income),LAC,2009,3643244830292.974
+Latin America & Caribbean (excluding high income),LAC,2010,4556436688834.758
+Latin America & Caribbean (excluding high income),LAC,2011,5320445093655.743
+Latin America & Caribbean (excluding high income),LAC,2012,5297857656983.352
+Latin America & Caribbean (excluding high income),LAC,2013,5436631031505.861
+Latin America & Caribbean (excluding high income),LAC,2014,5457289044548.768
+Latin America & Caribbean (excluding high income),LAC,2015,4634564315170.454
+Latin America & Caribbean (excluding high income),LAC,2016,4496975505130.537
+Latin America & Caribbean (excluding high income),LAC,2017,5012883307321.984
+Latin America & Caribbean (excluding high income),LAC,2018,4870399271215.6045
+Latin America & Caribbean (excluding high income),LAC,2019,4804547155275.126
+Latin America & Caribbean (excluding high income),LAC,2020,4046822646872.3726
+Latin America & Caribbean (excluding high income),LAC,2021,4690738841614.132
+Latin America & Caribbean (excluding high income),LAC,2022,5389510951677.686
+Latin America & Caribbean (excluding high income),LAC,2023,6050580948759.951
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1989,944566530515.018
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1990,1034598506513.3105
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1991,1124702232048.2854
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1992,1232756088301.5469
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1993,1467156610889.901
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1994,1715278789092.88
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1995,1856211576641.3835
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1996,2017401470167.4136
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1997,2212848167141.8213
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1998,2234145456857.453
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1999,2003326360339.101
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2000,2214793108979.011
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2001,2161567505918.137
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2002,1927469572333.205
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2003,1960572727991.551
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2004,2264753929049.2812
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2005,2751720692301.9966
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2006,3234376593372.225
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2007,3826508335402.8267
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2008,4465251653168.29
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2009,4176119188574.45
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2010,5210710546171.905
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2011,5940252657561.232
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2012,6005862490255.015
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2013,6154592516614.851
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2014,6270841236825.202
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+Latin America & the Caribbean (IDA & IBRD countries),TLA,2019,5413987316641.175
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+Latin America & the Caribbean (IDA & IBRD countries),TLA,2023,6772781697090.741
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+Latvia,LVA,1998,7166275467.651614
+Latvia,LVA,1999,7533788133.55753
+Latvia,LVA,2000,7958852838.933951
+Latvia,LVA,2001,8362398701.589434
+Latvia,LVA,2002,9557031605.275124
+Latvia,LVA,2003,11771975156.80728
+Latvia,LVA,2004,14435700533.368023
+Latvia,LVA,2005,17003459863.098942
+Latvia,LVA,2006,21570076498.620518
+Latvia,LVA,2007,31054350977.97839
+Latvia,LVA,2008,35854274228.9139
+Latvia,LVA,2009,26410909090.90909
+Latvia,LVA,2010,23956163076.55748
+Latvia,LVA,2011,27474380566.26504
+Latvia,LVA,2012,28169902669.378365
+Latvia,LVA,2013,30204783461.84881
+Latvia,LVA,2014,31386896487.040672
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+Latvia,LVA,2020,34390910338.9604
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+Least developed countries: UN classification,LDC,1960,25396340997.127872
+Least developed countries: UN classification,LDC,1961,27210618722.561977
+Least developed countries: UN classification,LDC,1962,28791232467.254963
+Least developed countries: UN classification,LDC,1963,30202790676.206306
+Least developed countries: UN classification,LDC,1964,31642936960.58793
+Least developed countries: UN classification,LDC,1965,34376613040.483406
+Least developed countries: UN classification,LDC,1966,37271686786.00302
+Least developed countries: UN classification,LDC,1967,39756391346.56397
+Least developed countries: UN classification,LDC,1968,41993452462.145515
+Least developed countries: UN classification,LDC,1969,45398307340.834015
+Least developed countries: UN classification,LDC,1970,43924106346.483284
+Least developed countries: UN classification,LDC,1971,45503456420.03081
+Least developed countries: UN classification,LDC,1972,46497667763.415955
+Least developed countries: UN classification,LDC,1973,56115642779.66818
+Least developed countries: UN classification,LDC,1974,70169680467.1637
+Least developed countries: UN classification,LDC,1975,86232150421.10028
+Least developed countries: UN classification,LDC,1976,79141108671.18828
+Least developed countries: UN classification,LDC,1977,84799217600.39177
+Least developed countries: UN classification,LDC,1978,98673333687.55481
+Least developed countries: UN classification,LDC,1979,110482187765.816
+Least developed countries: UN classification,LDC,1980,124275806288.40773
+Least developed countries: UN classification,LDC,1981,130944751635.4153
+Least developed countries: UN classification,LDC,1982,131526119023.53981
+Least developed countries: UN classification,LDC,1983,132942322285.83388
+Least developed countries: UN classification,LDC,1984,139143974827.19647
+Least developed countries: UN classification,LDC,1985,152679351682.86774
+Least developed countries: UN classification,LDC,1986,129919742514.94466
+Least developed countries: UN classification,LDC,1987,138575407039.5359
+Least developed countries: UN classification,LDC,1988,151408311246.3332
+Least developed countries: UN classification,LDC,1989,162003196598.6238
+Least developed countries: UN classification,LDC,1990,188377840875.20572
+Least developed countries: UN classification,LDC,1991,206285815475.59866
+Least developed countries: UN classification,LDC,1992,162423362071.82953
+Least developed countries: UN classification,LDC,1993,170645186609.5282
+Least developed countries: UN classification,LDC,1994,167418947426.8737
+Least developed countries: UN classification,LDC,1995,171656467729.8137
+Least developed countries: UN classification,LDC,1996,179828980354.19138
+Least developed countries: UN classification,LDC,1997,188878580088.1532
+Least developed countries: UN classification,LDC,1998,191440642053.54758
+Least developed countries: UN classification,LDC,1999,197386815443.76105
+Least developed countries: UN classification,LDC,2000,224220841301.6215
+Least developed countries: UN classification,LDC,2001,215386170078.76694
+Least developed countries: UN classification,LDC,2002,235506872334.1742
+Least developed countries: UN classification,LDC,2003,266569126690.00363
+Least developed countries: UN classification,LDC,2004,306201111423.0941
+Least developed countries: UN classification,LDC,2005,360878706648.537
+Least developed countries: UN classification,LDC,2006,417524553902.59216
+Least developed countries: UN classification,LDC,2007,501270268255.77356
+Least developed countries: UN classification,LDC,2008,614414977114.4393
+Least developed countries: UN classification,LDC,2009,608426703148.3109
+Least developed countries: UN classification,LDC,2010,687433128591.7863
+Least developed countries: UN classification,LDC,2011,783931296546.4978
+Least developed countries: UN classification,LDC,2012,819266433465.4807
+Least developed countries: UN classification,LDC,2013,892428919239.911
+Least developed countries: UN classification,LDC,2014,959618110350.5364
+Least developed countries: UN classification,LDC,2015,920864864683.3087
+Least developed countries: UN classification,LDC,2016,936561647193.3789
+Least developed countries: UN classification,LDC,2017,1025062467402.3052
+Least developed countries: UN classification,LDC,2018,1090417461003.2311
+Least developed countries: UN classification,LDC,2019,1150500716964.0056
+Least developed countries: UN classification,LDC,2020,1162019936698.8362
+Least developed countries: UN classification,LDC,2021,1266743958589.0854
+Least developed countries: UN classification,LDC,2022,1424615962242.8284
+Least developed countries: UN classification,LDC,2023,1504251159792.0146
+Lebanon,LBN,1988,3313540067.9324584
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+Lebanon,LBN,2001,17649751243.781094
+Lebanon,LBN,2002,19152238805.97015
+Lebanon,LBN,2003,20082918739.63516
+Lebanon,LBN,2004,21159827992.039803
+Lebanon,LBN,2005,21497336498.839138
+Lebanon,LBN,2006,22022709851.40962
+Lebanon,LBN,2007,24827355014.925373
+Lebanon,LBN,2008,29118916105.472637
+Lebanon,LBN,2009,35399582928.68988
+Lebanon,LBN,2010,38443907042.12272
+Lebanon,LBN,2011,39927125961.5257
+Lebanon,LBN,2012,44016799515.75456
+Lebanon,LBN,2013,46880103080.597015
+Lebanon,LBN,2014,48095213746.600334
+Lebanon,LBN,2015,49929337836.81592
+Lebanon,LBN,2016,51147308774.12936
+Lebanon,LBN,2017,53027680685.90382
+Lebanon,LBN,2018,54901519155.55556
+Lebanon,LBN,2019,51605959131.27413
+Lebanon,LBN,2020,31712128253.796097
+Lebanon,LBN,2021,23131941556.784348
+Lebanon,LBN,2022,20992421948.808086
+Lesotho,LSO,1960,34579986.16800553
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+Lesotho,LSO,1962,41859983.256006695
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+Lesotho,LSO,1966,56699977.320009075
+Lesotho,LSO,1967,59261976.29520948
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+Lesotho,LSO,1969,65967973.61281056
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+Lesotho,LSO,1971,76480285.00441125
+Lesotho,LSO,1972,80913200.42928225
+Lesotho,LSO,1973,121188715.75986478
+Lesotho,LSO,1974,150851316.52727023
+Lesotho,LSO,1975,149558895.91458103
+Lesotho,LSO,1976,147660036.91500923
+Lesotho,LSO,1977,193315048.32876208
+Lesotho,LSO,1978,266570066.64251664
+Lesotho,LSO,1979,290134592.5230071
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+Lesotho,LSO,1981,434188034.18803424
+Lesotho,LSO,1982,348741683.67384535
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+Middle East & North Africa (excluding high income),MNA,2007,1016252730742.8989
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+Middle East & North Africa (excluding high income),MNA,2009,1221252441475.6765
+Middle East & North Africa (excluding high income),MNA,2010,1413230709820.2507
+Middle East & North Africa (excluding high income),MNA,2011,1654719982162.4668
+Middle East & North Africa (excluding high income),MNA,2012,1782893176788.2053
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+Post-demographic dividend,PST,1997,24601931858245.918
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+Upper middle income,UMC,1999,3999785080973.845
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diff --git a/.ipynb_checkpoints/week2_analysis-checkpoint.ipynb b/.ipynb_checkpoints/week2_analysis-checkpoint.ipynb
new file mode 100644
index 0000000..ec384b2
--- /dev/null
+++ b/.ipynb_checkpoints/week2_analysis-checkpoint.ipynb
@@ -0,0 +1,1247 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d46393d7-f44f-40b2-9c4c-ce116425fe27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "print(\"Hello!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "629a0339-7fd9-454c-9b72-133d5dde76cd",
+   "metadata": {},
+   "source": [
+    "# Анализ данных за 2 неделю\n",
+    "Проверил работоспособность простого кода"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7fad19c2-6a4c-4e71-bd0f-4ba4cfb972a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     80\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  81.750000\n",
+      "std       0.50   7.228416\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  80.000000\n",
+      "75%      20.00  83.000000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создадим DataFrame\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 80]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce529a74-e979-406e-9ea0-fc699f1e520f",
+   "metadata": {},
+   "source": [
+    "Изучил работу с таблицами pandas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "da5bdc2c-ffa8-477f-9f0d-c2101542257d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  82.250000\n",
+      "std       0.50   7.135592\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  81.000000\n",
+      "75%      20.00  84.500000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Имя</th>\n",
+       "      <th>Возраст</th>\n",
+       "      <th>Баллы</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Гриша</td>\n",
+       "      <td>19</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Никита</td>\n",
+       "      <td>20</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Саня</td>\n",
+       "      <td>20</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Кирилл</td>\n",
+       "      <td>20</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Имя  Возраст  Баллы\n",
+       "0   Гриша       19     80\n",
+       "1  Никита       20     75\n",
+       "2    Саня       20     92\n",
+       "3  Кирилл       20     82"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "df  # отображаем датафрейм\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0588df5e-3b45-407a-ad8c-afbba02b09f4",
+   "metadata": {},
+   "source": [
+    "1 пункт"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e00445ca-d5da-4e20-95fa-efc8e52c29b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  82.250000\n",
+      "std       0.50   7.135592\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  81.000000\n",
+      "75%      20.00  84.500000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Обновленные данные:\n",
+      "      Имя  Возраст  Баллы  Обновленные баллы\n",
+      "0   Гриша       19     80              120.0\n",
+      "1  Никита       20     75              112.5\n",
+      "2    Саня       20     92              138.0\n",
+      "3  Кирилл       20     82              123.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "# Добавляем новый столбец с вычисляемыми значениями\n",
+    "df[\"Обновленные баллы\"] = df[\"Баллы\"] * 1.5\n",
+    "print(\"\\nОбновленные данные:\")\n",
+    "print(df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f28c486d-60c4-4515-8ac1-0d1130abbbfe",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "2d74b90e-f4b2-4c5a-a766-e50d66f9e9f4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       21     92\n",
+      "3  Кирилл       22     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "         Возраст      Баллы\n",
+      "count   4.000000   4.000000\n",
+      "mean   20.500000  82.250000\n",
+      "std     1.290994   7.135592\n",
+      "min    19.000000  75.000000\n",
+      "25%    19.750000  78.750000\n",
+      "50%    20.500000  81.000000\n",
+      "75%    21.250000  84.500000\n",
+      "max    22.000000  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Группированные данные:\n",
+      "         Баллы     Имя\n",
+      "Возраст               \n",
+      "19        80.0   Гриша\n",
+      "20        75.0  Никита\n",
+      "21        92.0    Саня\n",
+      "22        82.0  Кирилл\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 21, 22],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "# Применяем .groupby() и .agg()\n",
+    "grouped = df.groupby(\"Возраст\").agg({\n",
+    "    \"Баллы\": \"mean\",  # Среднее значение по столбцу \"Баллы\"\n",
+    "    \"Имя\": \"first\"   # Количество записей для каждой группы по возрасту\n",
+    "})\n",
+    "print(\"\\nГруппированные данные:\")\n",
+    "print(grouped)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "476b6585-5dda-4b54-bbe1-294caec29f09",
+   "metadata": {},
+   "source": [
+    "пункт 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "74d4e049-a72e-4ea3-909e-9b6399ad93bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  82.250000\n",
+      "std       0.50   7.135592\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  81.000000\n",
+      "75%      20.00  84.500000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Отфильтрованные данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "# Фильтруем записи по условиям\n",
+    "filtered_df = df[df[\"Возраст\"] > 19]\n",
+    "print(\"\\nОтфильтрованные данные:\")\n",
+    "print(filtered_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49536236-2050-4dfb-8d33-21c7dd68a557",
+   "metadata": {},
+   "source": [
+    "пункт 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "065fac3c-3b23-4fc2-94e6-122344c723fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Сумма элементов массива: 77\n",
+      "Среднее значение: 15.4\n",
+      "Медиана: 5.0\n",
+      "Стандартное отклонение: 18.789358690492875\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([5, 14, 52, 1, 5])\n",
+    "print(\"Сумма элементов массива:\", np.sum(arr))\n",
+    "print(\"Среднее значение:\", np.mean(arr))\n",
+    "print(\"Медиана:\", np.median(arr))\n",
+    "print(\"Стандартное отклонение:\", np.std(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68b77c5e-4dac-49f6-92fa-feef4d75b8c2",
+   "metadata": {},
+   "source": [
+    "изучил массивы и вычисления numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "02dc6842-fc0b-4944-93b5-a75f09a904f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Двумерный массив:\n",
+      "[[ 5  6]\n",
+      " [ 1 17]]\n",
+      "\n",
+      "Сумма всех элементов массива: 29\n",
+      "Среднее значение элементов массива: 7.25\n",
+      "Медиана элементов массива: 5.5\n",
+      "Стандартное отклонение элементов массива: 5.931905258852336\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Создаем двумерный массив\n",
+    "arr_2d = np.array([[5, 6], [1, 17]])\n",
+    "\n",
+    "print(\"Двумерный массив:\")\n",
+    "print(arr_2d)\n",
+    "\n",
+    "# Применяем операции\n",
+    "print(\"\\nСумма всех элементов массива:\", np.sum(arr_2d))\n",
+    "print(\"Среднее значение элементов массива:\", np.mean(arr_2d))\n",
+    "print(\"Медиана элементов массива:\", np.median(arr_2d))\n",
+    "print(\"Стандартное отклонение элементов массива:\", np.std(arr_2d))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34017aa9-5e74-4e97-b91a-a7707a10e6e9",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3523c6d3-6b5e-4b09-8d32-7eefd373f6b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Массив с np.linspace():\n",
+      "[ 0.   2.5  5.   7.5 10. ]\n",
+      "\n",
+      "Массив с np.random.randn():\n",
+      "[ 0.17990535  1.18232429 -1.35297202]\n",
+      "\n",
+      "Результат умножения матриц с np.dot():\n",
+      "[[19 22]\n",
+      " [43 50]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Использование np.linspace() для создания массива с 5 элементами от 0 до 10\n",
+    "arr_linspace = np.linspace(0, 10, 5)\n",
+    "print(\"Массив с np.linspace():\")\n",
+    "print(arr_linspace)\n",
+    "\n",
+    "# Использование np.random.randn() для создания массива с 3 случайными значениями из стандартного нормального распределения\n",
+    "arr_random = np.random.randn(3)\n",
+    "print(\"\\nМассив с np.random.randn():\")\n",
+    "print(arr_random)\n",
+    "\n",
+    "# Пример использования np.dot() для умножения матриц\n",
+    "matrix_a = np.array([[1, 2], [3, 4]])\n",
+    "matrix_b = np.array([[5, 6], [7, 8]])\n",
+    "dot_product = np.dot(matrix_a, matrix_b)\n",
+    "print(\"\\nРезультат умножения матриц с np.dot():\")\n",
+    "print(dot_product)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd97160-b97f-4c60-8bce-3f5b47c875e6",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "024362d0-4fc9-409a-b766-8c36f99b0f6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y, label='cos(x)')\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"График косинуса\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d795adf-9e77-46d4-9411-28bfe91597e1",
+   "metadata": {},
+   "source": [
+    "изучил построение графиков matplotlib\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "1ce3eff7-b795-4b61-9b7a-e7eb61fdd275",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y, color='purple', label='cos(x)')\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"График косинуса (фиол.)\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d5ee54-e40a-424c-9fa2-bd169e3d9e33",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "22c29c7e-4661-4b0f-b07b-44f743e358d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y_sin = np.sin(x)\n",
+    "y_cos = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y_sin, label='sin(x)', color='blue')\n",
+    "plt.plot(x, y_cos, label='cos(x)', color='green')\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"Графики синуса и косинуса\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efec143d-ed6d-472b-aa57-1e7608de0c1a",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3a0537d6-328a-441c-a9e2-e34e9e879298",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Данные для bar, scatter и hist\n",
+    "x = np.arange(1, 6)\n",
+    "y = np.array([5, 7, 3, 8, 4])\n",
+    "\n",
+    "# bar chart\n",
+    "plt.subplot(131)\n",
+    "plt.bar(x, y, color='purple', label='bar chart')\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Гистограмма')\n",
+    "plt.legend()\n",
+    "\n",
+    "# scatter plot\n",
+    "plt.subplot(132)\n",
+    "plt.scatter(x, y, color='orange', label='scatter plot')\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Точечный график')\n",
+    "plt.legend()\n",
+    "\n",
+    "# histogram\n",
+    "plt.subplot(133)\n",
+    "plt.hist(y, bins=5, color='cyan', label='histogram')\n",
+    "plt.xlabel('Значения')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.title('Гистограмма распределений')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8824583b-b920-423a-9cc7-7e64a9a264bf",
+   "metadata": {},
+   "source": [
+    "пункт 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "a11be903-b223-4890-a239-6c83baec9367",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# берем данные из предыдущего датафрейма\n",
+    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"] \n",
+    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5426eda2-7323-4349-9358-908b08452623",
+   "metadata": {},
+   "source": [
+    "изучил работу с графиками данных seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "56feb925-9279-4e76-9d5a-6928caf2eb90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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du/QHuGctKO2+4492NdRuN1p/RrtAaderxNoM2h1QaxZpVzrtoqSP0ZqG/upRape3wYMHm0COdqO6nn3guy1aKkHPeZ61aLQ0gLYHNJjh++Nen0d//NvBAd32hCRnmd27d5v2kl4Q08kffb/1gqcGBLX9pAEfvZhpd+VL6Ee5b3sjKdre0KCQdmfzF3zSgKF2CdPn14CiBqlSo25RYm0j7SKpF2/tz1b9+vXjPd4e/VLv99zXWhvM7pKowVHful7+jgdtH+kFXM/jITnHXXLe6xul3eb0+8GuH6vHjV401aCR1niyP5e6jA7ENGPGDBNA066OWtA8Od34AqGdatNuxTopDaDrsaGv2x6UCmmPwBSQgeiVm8S+ALWRqQ0NPYHrVSgt1qgNIL2ikhGi+/Y2aB9531oRNs+Tg17J0KtPmrWU1Ho1CKZFQfUkl9g6lX2lNLGhaHWdGpTSE60/vj8C9AT1yiuveN2m9ai0iKU/GnTSBrEWbPdXq0qfX4s86knPH7uBmFL6OM3C0vpM13vc6P4rXbp0ks+VnP2c0it8WtfArndw6NAh03DR2hJadNXzKiMABAOtU6PFllN6oUm/O/V7Xr9PfQuaK82oWbFihblfz9d6HtXzgRbCTqv2hJ4P9Qe91jrS59VzsJ7TtT6TZ9aGJ/0BrLVwtH6TBrX0B31S9FynP3C1TaXnKX2tGgzz/cGqWUv6A3zEiBEmoKTBE83O0HpE17sPfM91+ro061fPy3q+13O0/ujVDBrNHkntfW2vTwN5miHlj32hSM/zWn9L6xVpdponLbKd0MVTmwZVtCh2Qv7973+bAIRerPRXJ1JrWenxre0SDZKmJa1lqhdsNXP/emnQRl+zZhm9/vrrJrjn2y5MSlodd9dDg8kaaNKaUhqY0s+YDjaj3x2e9LOjn1tt7+pFWs1+s2uq6X7NLO1UzRLTGmEaoNPgmL4/WuPLvuiOtEdgCgggmnWjP9LtiL7niU6vdtn0RKcFpvVqZGoU8Lb5fjnrl7denbMbMHZRdT3hJufKmo62oduY1NUIXa8+l1658ZeO70vTr7VxqI2QxNa5aNEiUyA0OQEP3b++rymxAuV6VU4b+5oqndDz6+vXBmtqpWXb9EqTZ6Zdco8bpe+Hvqf64yQpdpq7fXXRH204aKNCjx3P5fSqoz6/b8NCryx67md9D/UKue5rLZgLAMFCuzZrF3nP4t/JoQNGaPaB/njU7ve+gSktzq2Do2gwQIsu227kB5g+p3bb98yasrtNaXFw+1ykQTbtnu953vPX1dAzW0IzNOxugXaX/sQ8/PDDJvClARfNyPAsQWDTYtS6X3RbPLtk2aMgJ5fuM89sLz1/6jnPfs1a6FzfRx0Rr1SpUu7lfLvr2+0n/bHum2Hsb5mE2li6f5W2/ZLTDtN9pcEIPRa0y6RmtGswMDkXTxN7H7Qrmmbj6bnb3wVFpW2vyZMnm2U1+0aPA20XaVAtLWi3UR1gx6btDw2M+bK70/m2T+zBdHREPXsEOO166XlxVI8HbW/Z9Pn0Aqw9yFByj7vkvNepQduoWoBe94NmTmnWWvv27eMtpxdSddLAsAa0te2so26mNDCXEdupNg2yee5rfV/186HHJ9IHNaaAAKInd6tGn/cVHLtegE2vYGmDQTN6fPk+PiU0Nd6uSWCfSPSEq2m/Susz6clUh6H1PPnbdCQ9323X1+Q5Soo/OpKGLqcNJ9/t13nPRrc2PDRNWxtQiaXuauNWT4x6ZcaXrsNO/70emq6vV5b0ilpCQSd9fn3ftFHm6+LFi6aBfz20UaQNDM+rnck9bpRutz6/XesqMdqI0UBSYid8uzGmo+t5sjPFtIGXGN0WpQ17AAgmM2fONN99Ka2zp+dK7b73xBNP+L3fDhT4nhd8v6dTQs+b2m3LMyNa5zX7WNsGCT2vXkTTc2ZiNItiy5YtputRcrrkaLaFZj/oY+wRAX352xbdZs20SgnNIvJkj9Jmt4v8PY9219JMJd9sYQ2gaTDRt36l/VjNatIgmL5Pvm0UexnNQtOaTLrvtX2WVDtMM3U0IKTtFd2/+sM8qZpPyaEBGw1eaFAxMXoRTwNjmk2nz20fK57s2ktJBSQTo0FSbRvVqlXLq32iI/55Hn/a9tKuoxpY1K57CdFt0QCkZ3dUpY/1vE27MepnI7Hjwd9xl5z3OjXo7wXdplmzZpl2obbHPYPLmiGm2+9JA1QaVLredllGa6cmxM7USiiwitRHxhQQQPSEoamlWnNBs0h0CGjtimZfIfMsUq5BJO0XriddvcqjJ1vNENIrI3ZRw5TSxp4WidTn14wXPWHqlT0d9lnpiUrrAekJWAuW6nKa6qwnFr06qJlUevVQt0UbczqssmZA6RUkmx3Q0galNha0BoEGu/SqjDZgtJ++Xj3WBpxeYdLi8FpsUq+w6evTVGB9rD5PYjSVWq/AaiNQr0hro1CvMOoJU0+GerXZ7vueUprqrN0TE7vKpcPaavq0/nDQfaMNOA2UaeNLb9cikUllkmljQRtz9glU941ewdJGkTYKU3LcaMNPG6faONJldH8kRLvV6X7WArP6fIllfGkjUNP4tbFmp7DrMaldMvR99LyyqLQYrP2a9LjR4KoeNxRABxAs9BypAQ793rZ/sNnfizY9B+v5Um/X841nHSk9B+l3vF1o3Jd+p2q2hg55r+cLPU/rY1KaLeTbLUi7AOl5SM/r+oNQz6363W9nbuu5SDNFdOh2vSihz6fnEA0A+LuYZdPMCA2oJCcoZdOu9NrO8M22sOl5TrOD9PykXZP0PKYZQyn90a+vQYMvuo3aZtH3QweAsQMgei7V90GzULTNoa9TL0hpAMkzcKTviXbt69Wrl6mtqOvQ7dMMIj0/6zlT21ga6NB1aUa2ntP1R7e2G7RukbYblL5ubatpAEHbZ3qu1+NFt0+7yOs6la5Xn0cDWZpdlpr0eNLaWonRNpu+Zt3viXXL0jaDthW0jZKcAugJtY30b8/sPG0naUBG26x6DGgbV/ezvqd6gdPO6NF2sx7Ddt3X5cuXm6CxHs+eNVPtIJO2V/TiowZftE2l74UdoEvucZfc9/pG6XGo+1YvFuqFZ98sf62Dpd3bNGipn2vdt7q9+r2UWDfOQGqn2jRAqsvbXfleffVVc1zefPPNdOdLL2kw0h+AFEpoKF5fOpzqv/71L1dUVJQZcrVRo0aulStXmqGadfKkw9H++9//NsOpZsmSxVW0aFHXgw8+6NqzZ4/XcMyewy3bdAhqz/XZQ/XqkMKDBg0ywz/r8+twwjoUsy8dXvb+++93FShQwAz1qsPdduzY0bV48WKv505q0qFmPX366aeuxo0bu3LkyGGmKlWqmOFdd+7cae5/5plnXLfffrtrwYIF8bbJdwhj2/vvv2+Gy9XXo0Ma6xDLAwcOdB0+fDjBoZNt+ty+69T5kJCQeMMS+3uPdEjhN954w+xv3U/58uUz26LD2sbExLhSOgyvbr++/q+//jrFx82hQ4dcJUuWNMMn+3tuz2F4dZtvvfVW14wZM5I1pK4O16uvyT4W9Xn0OPIdjlv3s+frKViwoKtVq1ZmWwEgWCT3HGlPeo72/P6tXbu215Dy9vr0fpt+5993332uvHnzuvLkyeN66KGHzHnPd8h1+9x54sSJBLdXzyN6Hlu3bp0Zgj5btmzm+3z8+PFey+k2vfbaa+Y+PefdfPPN5nyl5zO9zXd7/bVPEro/qe30d/8vv/ziuu2228x5sVixYubcv3DhQq99mhB7fdu2bTNtKz3/6jm8b9++rosXL3ot++WXX7pq1qxp9kuZMmXMOXTKlCl+h5/XZRs2bGi2KXfu3K569eqZtpen5cuXu1q2bGmeU9tCuu733nvPaxlt63Xt2tW0/fS8W7x4cdfdd9/t+uSTT9zL9OnTx7TT/vzzT6/H+rZ5Emqj6r5M6Hjp0KGD17J2O9LerydPnjT7vEuXLn6XmzdvXrzbPJ8nJW0j3Y/Nmzd3LVq0KN7yup/0/dPPgb4/ur9921ATJ040bUPd1zlz5nRVrVrVtGnOnTsXbx8tXbrU7Fc9FnTZhx9+2HXq1Cmv9aXkuEvOe21L7j7yNXnyZHcb0vfY/eOPP1w9e/Z0lS9f3uyf/Pnzu5o1a+Z3XwZyO9VzG7UNr58b/R2zffv2BB+D1Bei/6RbFAxAQNKMJr2ioplE15tF5EmvmGiKsl6RsOsw+NKrYrqcXvUEACBY2OdIzabVbJYbXS6t6XNr1ybPYsaZnbZRtMukZnIllJWF4KFtVc32Wbt2LaO4AdeJGlMAAAAAAABwBDWmAKQ7e6SLxOpFaFFErVkBAEAwniM960bdyHIAAGR0BKYApDtNe/ct5OpvJD4AAIJNcs6RKVkOAICMjhpTAAAAAAAAcAQ1pgAAAAAAAOAIAlMAAAAAAABwBDWm/IiLi5PDhw9Lrly5JCQkxOnNAQAA6UirHJw9e9YMwBAayjW860V7CgCA4OVKQXuKwJQf2ogqWbKk05sBAAAcdPDgQSlRooTTmxGwaE8BAICDyWhPEZjyQ6/s2Tswd+7cTm8OAABIR2fOnDEBFbs9gOtDewoAgOB1JgXtKQJTftjp5tqIoiEFAEBwovvZjaE9BQAAQpLRnqJwAgAAAAAAABxBYAoAAAAAAACOIDAFAAAQ4HTUm379+knp0qUlMjJSGjZsKGvXrjX3XblyRV544QWpUaOG5MiRw4yO07VrV1OcPDEvv/yySb/3nKpUqZJOrwgAAAQLakwBAAAEuF69esnWrVvlf//7nwk8ffzxx9KiRQvZtm2b5MyZUzZs2CBDhgyRWrVqyenTp+XZZ5+Ve+65R9atW5foeqtVqyaLFi1yz4eHp37T8dq1ayZ4htSTNWvWJIfmBgAgoyAwBQAAEMAuXrwon376qXzxxRdy++23u7OdvvrqK5k4caK88sor8sMPP3g9Zvz48VKvXj05cOCAlCpVKsF1ayCqaNGiabLdLpdLjh49KtHR0Wm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sBag0OHnxosjkySLVq1sj+n39tUhcnNNbCgBpQ7vv+ZsuXrwoFbQ/NAAAQCaXqsXPV65cKdmILsBDTIzI0qVWHSk7uFC8uEiTJiKVKlFTCBYdsU+78fXubdWiGjtWZP58kUWLrKliRSuA2b27tSwAZBZ58+b1W7fTLoZ+7do1R7YLAAAgQwem7tc0F5/G05EjR2TdunWmWCegXfZ+/llk/XoRu01drpxI48YiZcoQkIJ/elxo0FInHcVvwgQre2rXLpF//lNEv16eesrq5lekiNNbCwCp45NPPpH8+fM7vRkAAACBE5jKkyeP17wObVy5cmVTpLNVq1aptW0IQNoNS7vqaVFrHXFPlS2rIzmKlCzp9NYhkGgA8803rRH9dDQ/7Qb6++8io0aJjBljFUl//nkrmwoAAlmjRo2ksPZvBwAACELXFZj66KOPUn9LENC0ZpQWNV+58u+i5iVKiNx5pxWYAq5XzpxWltQTT4h8+aXIG29Yx9r771vZVJrA+cILIrfe6vSWAsD12bZtm5w6dUpy5MghRYsWlaxZszq9SQAAAIFRY2r9+vWyfft287eOHHOzVjJGUNG6UTqy2k8/iZw/b92mXaw0IKWZLHTZQ2oJDRW5916RDh2sOlSjR1uF0T/91Jo0K2/oUJGmTZ3eUgBImebNm7trSmkWepUqVaRnz57y3HPPOb1pAAAAGTMwdfz4cTOM8ZIlS0zRThUdHS3NmjWT2bNnS6FChVJ7O5EB7dkj8v33ejxY81oeQ4MDOro1ASmkRx2qrVtF3npLZMYMKziq0+23WwEqDY5yHALI6Pbu3WuCUleuXDGj8R0+fFjWrFljanZevXpVBgwY4PQmAgAApKnQ63nQM888I2fPnpXffvtN/vrrLzNt3brVNKj+qRWKkamdOCEyc6bIxx9bQSkdiLF1a6u7VfXqBAOQfvR4mzrVCpLq8ae9X5YtE2nRwiq0v3ChDs7g9FYCQMJKly4tZcqUkYoVK0qdOnWkffv2MnLkSJk4caK8r32WAQAAMrnrCkwtWLBA/vOf/8hNN93kvq1q1aoyYcIE+e6775K9nmXLlpkGWLFixUz6+nwdHz4JmqV1yy23SEREhFSoUEGm6q9SH7od2sjLli2b1K9f31x5xI27cEHk229FJk60RknTrlX161ujpd12m0hYmNNbiGBVqpQ1gt8ff1jHowZLV6wQadPGOja/+YYAFYDAopnpc+bMcXozAAAAMmZgKi4uTrJkyRLvdr1N70uu8+fPS61atUwgKbnp7u3atTNdBjdt2iT9+vWTXr16yUJNi/h/2ojr37+/DBs2TDZs2GDW37p1a9P9ENdH39J160Tee09k7VrrB37lylaGiv7wj4x0egsBS/Hi1uh9GqDq3986NjUufffdInXrinzxBQEqAIFB21R6IQ4AACCzu67A1J133inPPvusqYNg+/PPP02RTi3gmVxt27aVV155Re67775kLT9p0iQpW7asvP322yZbq2/fvvLggw/KO++8415mzJgx0rt3b+nRo4fJ4tLHZM+eXaZMmZLCVwn1558iH35oZZxcumQVNu/aVa/kihQo4PTWAf5FRYm8/bbIvn0iAweK5MghsmGDVTy9Xj26+AHIOK5duyZvvfWW1KtXz4zIlz9/fq8JAAAgs7uuwNT48eNNPSntLle+fHkzacBIb3tP02rSyMqVK6WFFo/xoNlQeru6fPmyGSnQcxkd3Ubn7WWQPBcvWiOeffCBiMYfIyKs7Kg+fUTKlnV664DkKVxY5I03rADVoEFWgEqz//RYvuMOkZ9/dnoLAQS74cOHm4tqnTp1kpiYGJP1ff/995v2y8svv+z05gEAAGTMUflKlixpusktWrRIduzYYW7TDCbfoFFqO3r0qBTRlB0POq8BsYsXL8rp06fNlUd/y9jb6U9sbKyZbLq+YKVZJBs3iixaZAWnVM2aVjHpXLmc3jpcr+3bt0uwe/BBzfYMl6lTi8i8eYXk559DzQh+DRrEyNNPH5abbvr/Ax43RL9LtQYgkNYKFiwopbTAXICbMWOGTJ482ZQq0EBUly5dzAW/mjVryqpVqxhUBgAAZHopCkz9+OOPpvucNpRy584tLVu2NJPSq3zVqlUzXeea6DjuAWTUqFHmimWw09H2vvpK5OBBa75QIZF27XTEIKe3DNfr3LkjIhIijzzyiNObksEUF5HBIvKYrFyZx0win4rIUBHZ5vTGBTgdlpN+kkh7kZHZZceO7QEfnNKLbjVq1DB/58yZ07Sn1N133y1DhgxxeOsAAAAyWGBq7Nixpn6TBqV85cmTRx5//HGTjp5WgSmtvXDs2DGv23RetycyMlLCwsLM5G8ZfWxCBg0aZFLnPTOmNCssWFy7JrJ8udWtSf/WuvZNm1oj7jHSXmC7dCnaBAmaNRsvFSs2cHpzMpwzZ3bKunVRsnu31nF5QETulwoV/pK6dY9I7tyXnd68gLNr17fy009DON6Q5k6c2C6ff/6InDx5MuADUyVKlJAjR46Y16GZUt9//70per527VqyDwEAQFBIUWBq8+bN8oYWbElAq1atTAHPtNKgQQP59ttvvW774YcfzO0qa9asUqdOHVm8eLHcq1WO/38EQZ3XTK+EaMMvWBt/hw5ZWVL2oIUVK1pZUnk0gQSZRr58FSQqitGd/BVJ1xEm9fhfskS7PIbI7t0F5I8/Csitt4rp6pc9u9NbGThOnrS6jHK8AcmnA8BoO6V+/fryzDPPmAzXDz/8UA4cOGAGlQEAAMjsUhSY0swjHb44wZWFh8sJ7Q+WTOfOnZPdu3e75/fu3SubNm0yo9DolUPNZNLR/qZPn27uf+KJJ0zh9YEDB0rPnj1N18K5c+fKNzpk3P/TzKdu3bpJ3bp1zQg3muV1/vx5M0of/nb5snbNFFm92prXH99aELp6dZEQ7YkDBFmR9I4drUL/+rnYs8f6bGzaJNKokchtt1mZhACQ2l5//XX331oAvXTp0rJixQqpWLGitG/f3tFtAwAAyHCBqeLFi8vWrVulQoUKfu/fsmWLRGkKQjKtW7dOmjVr5p63u9NpYGnq1KkmtV2vGNp05D8NQukVxHHjxpn09w8++MCMzOfZqNPg2NChQ03dhtq1a8uCBQviFUQPZvqjW7Ok/r+MhdSqpdluZIYAxYqJaDku/YzoAABHj1qBqrVrRfSrSj8rodc1likAJE2773311VdmIBe9wAYAABAMUhSYuuuuu0whzjZt2ki2bNm87tNR8YYNG2aKdSZX06ZNxaXDwCVAg1P+HrNRh41LhHbbS6zrXrDSgQd/+EFk/XprXrvr6duVQJwRCFrly4uUKyfy669WYEqDuF9+KbJypTVCpXZ5JbMQwI3QzHBtzxQoUMBdmkBH5tPSAqGhoaY0wqxZs+Shhx5yelMBAAAyTmBq8ODB8tlnn0mlSpVM4KeyFmcRkR07dsiECRPMFb5///vfabWtuAH79ol88YVItNbCFpF69USaN9e6XE5vGZAxaeCpZk2RqlWtjKlly6yRK2fNskaq1AFJi+vgfgBwHQ4dOmTaTbY333xTunTpIlOmTJGQkBBTb2r06NEEpgAAQKaXosCUdofTugdPPvmkqf9kZztpA0q702lwii5zGcuVKyKLF/9dS0qzpDp00G6RTm8ZEBjCw3XgBZHata3RK/WztH+/yAcfiFSrJnLnnSL5dVA/ALgB27dvl9dee82MLqw0MHWbFrgDAADI5FIUmFJalFNHxjt9+rQpXK7BKS3QmS9fvrTZQtzQiHvz54ucOmXN33KLVUsqSAcgBG5IZKSVJaXZhj/9pKOUivz2m/6YFDOC3x13WMsAwPWIjo42g7/YtF119uxZR7cJAAAgQwamPBtMt+qvMWQ42jNg6VIru0OT2nLmFLnnHqsuDoAbo1mH995rZVFpgXQdWFSzqLZssYJTWq/4/xMeACBBmm2uk+9tAAAAwYbxpTIZzY6aMkXk55+toFSNGiJPPUVQCkht2mv54YetUfwKF9YBIEQWLBCZOFFk507r8wcACdGM8+7du8v9999vpkuXLskTTzzhnu/Zs2ey16W1qnRwGh29ODIyUsqXLy8jR450l1y4cuWKvPDCC1KjRg3JkSOHFCtWTLp27SqHDx9OdL0vv/yyO4BmT1WqVLnh1w4AAJAqGVPIWLTtuWmTyHffWXWldNBEHXFPa+AASNsR/B5/XGTDBquLnwaHZ8+26ri1bm0FsADAV7du3bzmH9Eotw8NHiXHG2+8IRMnTpRp06ZJtWrVZN26ddKjRw/JkyeP/POf/5QLFy7Ihg0bTPCqVq1aphzDs88+K/fcc49ZNjG6vkWaHvr/wrXwHgAAQCqidZEJaKbG11+LbNtmzZcpY3U10i5HANJeaKjVhU8zFDVbcdUqkb17Rf77X6touhZI1y61AGD76KOPUm1dOjBNhw4dpF27dma+TJkyMmvWLFmzZo2Z1wDVDz/84PWY8ePHS7169eTAgQNSqlSpBNetgaiiRYum2rYCAAD4oitfgNMfv9p1SINS+uO4eXORRx8lKAU4QQcWaNFC5OmnrWxFzWTcuFHkvfesgJVmMwJAamvYsKEsXrxYfv/9dzO/efNmWb58ubRt2zbBx8TExJiueXnz5k103bt27TJd/8qVKycPP/ywCWQBAACkJjKmArjAuXYb+uUXa75AAZH77xcpVszpLQOgg5Q++KA1gt/ChSJaxuXHH0XWr7cCVxq0osYxgNTy4osvypkzZ0z9p7CwMFNz6tVXXzWBJH+0npXWnOrSpYvkzp07wfXWr19fpk6dKpUrV5YjR47I8OHDpUmTJrJ161bJlStXvOVjY2PNZNNtAgAASAqBqQAUEyPy6aciBw9a87fcYtWyyZrV6S0D4El7x/TqJfLrryKLF//92dVR/PQzW6KE01sIIDOYO3euzJgxQ2bOnGlqQm3atEn69etnMp18a1lpIfSOHTuawuhalyoxnhlXNWvWNIGq0qVLm+d77LHH4i0/atQoE7wCAABICQJTAUaz9OfPt+pKabehe+4RqVrV6a0CkBDNjKpZU+Smm7QOjJXleOiQyIcfWjWptPstXW8B3IgBAwaYrKnOnTubeR19b//+/SZQ5BmYsoNSet+PP/6YaLaUP9rtr1KlSrJ7926/9w8aNEj69+/vlTFVsmTJ635dAAAgOBCYCqCue5pxsXKlNR8VZXUVyp/f6S0DkBxZsojccYeV4ajd+nQUTc2k2r5d68OINGpE1iOA66Oj7oVqoUkP2qUvLi4uXlBKa0b99NNPUkBrAKTQuXPnZM+ePfKoFrP0IyIiwkwAAAApQWAqAERHW91/NMtCad2ali11pByntwxASmlZlg4d/q4/tX+/yLJlVpF0zZ7S7CrqTwFIifbt25uaUjq6nnbl27hxo4wZM0Z69uzpDko9+OCDsmHDBvn6669NDaqjR4+a+/Lnzy9Z/z8q3rx5c7nvvvukb9++Zv75558369bue4cPH5Zhw4aZgJfWpgIAAEgthDYyuJ07ra57ly5ZXff0B612CQIQ2DTrUXvYaMaUjuKuAWj9rOvo7m3aiND7BUByvffeezJkyBB56qmn5Pjx46a21OOPPy5Dhw419//555/y5Zdfmr9r167t9VjNnmratKn5W7OhTp486b7v0KFDJgh16tQpKVSokDRu3FhWrVpl/gYAAEgtBKYycNe9RYtEVq2y5nW0Pe26p6N9AcgcNDNKa8RVqmR91n/+2RrBb8oUkerVrRH8qD8FICk6Qt7YsWPN5E+ZMmVMsfOk7Nu3z2t+9uzZqbaNAAAACSEwlQHpyF3z5ukVTmu+fn2r615YmNNbBiAtaLfcxo01k8GqP6Xd+rZuFdmxQ6RBA+s+6k8BAAAAyIwITGUwf/xh1ZO6cEEkWzar616VKk5vFYD0kDOnNdKm1p9asMCqP6VZVFoonfpTAAAAADIjAlMZhGbY6zDymi2hfxctKtKxI133gGCkn3+tP6UZU99/711/qnVrkVKlnN5CAAAAAIHs8tU4OR97VbKGh0iOiCyObguBqQwgNlbkiy+sIshKu/PcdZc1vDyA4KSZUTrQQcWKIqtXWyP3af2pjz4SqVbNqj+VN6/TWwkAAAAgkFy5FieHT1+Q2Sv3y+YD0VI4d4R0bVJOKhTJKbkjnakfQmDKYSdOiMydK6KD4ISGirRtK1KnDt11APxdf6pRI5FatXT0LJENG0R++83KpmrYkPpTAAAAAJLv9yNn5MmP1pqMKfXH8XOyavcpeaJ5RelYv5Rkj0j/MFFouj8j3LZtE/ngAysolSuXSI8eInXrEpQC4L/+VPv2Io8/riNsWSN3av2p996zalAlY8AtAAAAAEHsr3Ox8toXv7mDUp7e/3GX/HX+siPbRcaUI8Jk1arismWLNac/Mh94wPrhCQBJ1Z/q2lVk506r/tTp01ZXYLv+VOnSTm8hAAAAgIzo7MUrsuf4Ob/3xblEdhyOkRL5s6f7dhGYSmd//aW7/HvZsqWImdeh4LVWjHbjA4Dk0KxKHa2zQgUrIKX1p44cEZk6VaRqVZGWLak/BQAAAMBHEt2zQhzqvkVgKh3FxYk88URFEakp4eHX5N57w0wRYwC43vpTWmdK60/piJ4bN1pdhDWbSoPeWn8qIsLprQQAAACQEeSOzCIViuSS3cfOxrsvNESkclRuR7aLPJ10pFlRffseFpHtct99OwlKAUgVOXJY9af69Pm7/tTy5SLjx1vBKupPAQAAAMiXI6v8u0M1icgSPxTUt1UlyZ+DUfmCwu23x4hIfcmXb7XTmwIgCOpPffml1d2vTRvqTwEAAADBrmLRXPLxkw3ls7UHZeP+01IkdzZ5pFEZKVMopyMj8ikCU4646vQGAAii+lNHj/5df0pr2uXL5/RWAgAAAHBCeFiolCyQQ55qWUkuxF6ViPBQyZbV2dAQgSkAyIQ860/99JPIhg1/15+67TaRJk2oPwUAAAAEqyxhoZInuzNd93xRYwoAMnn9qbvvFnn8cZGyZa36U7/8IvLee1awSgdlAAAAAACnEJgCgCBQpIjIo4+KdO4skj+/yPnzIl99JTJ5ssi+fU5vHQAAAIBgRVc+AAii+lOVK/9df2rpUqv+1LRpIjfdJNKyJfWnAAAAAKQvAlMAEGTCwkQaNBCpWVNkyRKR9etFtm8X+f136k8BAAAASF905QOAIK4/1a6dyBNPiJQrR/0pAAAAAOmPwBQABLnChUUeeUSkSxeRAgX+rj/1/vvUnwIAAACQtujKBwAw9acqVRIpX15k7Vqr/tSxY1b9qSpVrPpTWjQdAAAAAFITgSkAgFf9Ka0zZdefWrdOZMcOkV27ROrVE7n9dpFs2ZzeSgAAAACZBV35AADxZM8uctddVv0pzaLS+lMrV4qMGyeyYoXI1atObyEAAACAzIDAFAAg0fpTDz8s8o9/iBQqJHLpksgPP4iMHy+yZYuIy+X0FgIAAAAIZHTlAwAkWX+qYkUrc2rzZpGffhKJiRH5/HMri6pFC+s+AAAAAEgpAlMAgGQJDRW5+WaR6tVFVq0S+eUXkaNHRT7+WKRcOZEyZfI5vYkAAAAAAgyBKQBAimTJItKkiUidOiLLllmj+P3xh053ich0uXCB6ugAAAAAkofAFADgugukt2kjUr++yI8/imzdqrc+Kt9/HyfR0dYIfpGRTm8lAAAAgIyM4ucAgBuSL5/IAw+INGv2nYgslri4UNPVT0fw04yqy5ed3kIAAAAAGRWBKQBAqsiX7y8RaSENG66XIkVEYmOtQukaoNJA1dWrTm8hAAAAgIyGrnwAgFRVtOhJM1Kfdu1bskTkr79EFi60RvBr2lSkVi2rkDoAAAAAEJgCAKS6kBCRGjVEqlYV2bRJZOlSkTNnRL780hrNr1kz6z5dDgAAAEDwIjAFAEgzYWHW6H01a4qsWyfy888ip06JfPKJZlaJ3HmnSIUKBKgAAACAYEVgCgCQ5rJkEWnQQOSWW6x6UytWiBw9KjJzpkipUlaAqnRpp7cSAAAAQHojMAUASDcRESJ33CFy660iy5eLrF0rcuCAyNSpIuXKWTWoSpZ0eisBAAAApJcMUX52woQJUqZMGcmWLZvUr19f1qxZk+CyTZs2lZCQkHhTu3bt3Mt079493v1t2rRJp1cDAEhK9uwirVqJPPOM1dVPi6H/8YfIlCkiH38scvCg01sIAAAAICgypubMmSP9+/eXSZMmmaDU2LFjpXXr1rJz504pXLhwvOU/++wzuXz5snv+1KlTUqtWLXnooYe8ltNA1EcffeSej9DL9ACADCV3bpG77xZp1MiqP7V5s8iePdZUvryVQVWihNNbCQAAACDTBqbGjBkjvXv3lh49eph5DVB98803MmXKFHnxxRfjLZ8/f36v+dmzZ0v27NnjBaY0EFVUK+sCADK8fPlE7rlHpEkTK0ClI/nZASotjq7d/whQAQAAAJmPo135NPNp/fr10qJFi783KDTUzK9cuTJZ6/jwww+lc+fOkiNHDq/blyxZYjKuKleuLE8++aTJrAIABEaASrv41a5tjda3e7d+14vMmCHy559ObyEAAACATJMxdfLkSbl27ZoUKVLE63ad37FjR5KP11pUW7duNcEp3258999/v5QtW1b27NkjL730krRt29YEu8J07HIfsbGxZrKdOXPmhl4XAODGA1QdOojcfrvIsmVWFz8NUOlUsaKVQVW8uNNbCQAAACDgu/LdCA1I1ahRQ+rVq+d1u2ZQ2fT+mjVrSvny5U0WVfPmzeOtZ9SoUTJ8+PB02WYAQMoDVHYXPw1Q7dplTVqDqnFjkdKlrcwqAAAAAIHH0a58BQsWNBlMx44d87pd55OqD3X+/HlTX+qxxx5L8nnKlStnnmu3Xmr3Y9CgQRITE+OeDjIcFABkKFpeUANUffuK1KplBaK0/tS0aSI6zoUGqlwup7cSAAAAQEAFprJmzSp16tSRxYsXu2+Li4sz8w0aNEj0sfPmzTPd7x555JEkn+fQoUOmxlRUVJTf+7VQeu7cub0mAEDGDFDde69Vg6puXRHtna3XEmbOFPnvf0V++03PI05vJQAAAICACEyp/v37y+TJk2XatGmyfft2U6hcs6HsUfq6du1qMpr8deO79957pUCBAl63nzt3TgYMGCCrVq2Sffv2mSBXhw4dpEKFCtK6det0e10AgLTt4teuncizz4o0bKgXOjTbVuSTT0QmTBDZuFHk2jWntxIAAABAhq8x1alTJzlx4oQMHTpUjh49KrVr15YFCxa4C6IfOHDAjNTnaefOnbJ8+XL5/vvv461PuwZu2bLFBLqio6OlWLFi0qpVKxk5cqTJjAIAZB65com0bGnVmlq92pr++kvkyy91dFYraHXLLSJZsji9pQAAAAAyZGBK9e3b10z+aMFyX5UrVxZXAsVEIiMjZeHCham+jQCAjCsyUqRpUxHtBb5+vcjKlTrCqsiCBdaofrfdZnX90+UAAAAAZBwZIjAFAEBq0MRYzZLSwVo3bRL55ReR6GiRH3+0RvW7+WYrSKVdAQEAAAA4j8AUACDTCQ+3MqS0G9/WrSIrVlg1qNasEVm7VqRqVSu7qnhxp7cUAAAACG4EpgAAmZaWKKxZU6RGDZE//rC6+O3ZY43ep1Pp0laAqlIlkZAQp7cWAAAACD4EpgAAmZ4GncqXtybNnNIA1a+/iuzfb006wKsGqGrVsrKtAAAAAKQPmt8AgKCig77ee6/InXdao/hpsfRTp0S+/lrkp59Ebr3VmrJnd3pLAQAAgMyPwBQAICjlzi3SsqXI7beLbNggsmqVNZKfDga7fLnV/a9+fSuQBQAAACBtEJgCAEiwj+Sn3fh0JL9t26xufkeOiGzcaE1ah0oDVJUrWzWrAAAAAKQeAlMAAIhIWJiVJVW9usjBg9YIfhqosutQ5cljdfHTkf4iI53eWgAAACBzIDAFAIBPofRSpaxJu/atXWvVoYqJEVm0yOrqp0XSNcOqcGG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nzrieffZZ8wWQLVs2E5zUYcw9fxQl5z3X161fxp70h5SuVxuAOuT7qlWrXJlhn+iPQT0G/E16Akton3Tq1MkVFRVl9ocGNXReA8SZYZ8k93OQWY6T5Hxu1H//+19XZGSkGbbWn8x0jKg5c+aYfaHbr0P36rDJnq892L5LktonwfhdEsj0PfH3XukPbDVu3DgTZNTvQD1eBw8eHO874Y477nAvb9MfP5UqVTLvZ7Vq1VzffPNNur6uYNvn+nnyt07f751gllbHuicCU+mzz1esWGEuhug5V89Fr776quvq1avp+tqCaZ9fuXLF9fLLL5tglLYP9ffkU0895Tp9+nS6v76MSBJo83jGGjSYpPssX7585uLufffdZ4JXGSE+EfL/Tw4AAAAAAACkK2pMAQAAAAAAwBEEpgAAAAAAAOAIAlMAAAAAAABwBIEpAAAAAAAAOILAFAAAAAAAABxBYAoAAAAAAACOIDAFAAAAAAAARxCYAgAAAAAAgCMITAEAAAAAAMARBKYABLTu3btLSEhIvKlEiRJObxoAAECGbjcVKFBA2rRpI1u2bHF60wAEMQJTAAKeNqiOHDniNW3cuNHpzQIAAMjQ7abFixdLeHi43H333U5vFoAgRmAKQMCLiIiQokWLek2FChVy3//CCy9IpUqVJHv27FKuXDkZMmSIXLlyxWsd+/bt85t5FR0dnexlXn75Zaldu7bfbZw/f75ZFgAAIKO0m7Td8uKLL8rBgwflxIkT5v5ff/1V7rzzTomMjDQZVX369JFz5865H6/LFytWTLJmzSrFixc37ay4uDhz35IlS0x755tvvpGaNWtKtmzZ5LbbbpOtW7e6H3/q1Cnp0qWLeay2zWrUqCGzZs3y2kZd3+jRo6VChQpme0uVKiWvvvqquc9fW8ye9PkBBB4CUwAyvVy5csnUqVNl27ZtMm7cOJk8ebK88847Xsu4XC7z/6JFi8wVxE8//TTeepKzDAAAQKDQgNPHH39sAkAahDp//ry0bt1a8uXLJ2vXrpV58+aZdk/fvn3dj2nVqpV8/fXXsnv3bvnggw/k/fffN+vwNGDAAHn77bfNOvRiYfv27d0XBS9duiR16tQxwSsNWGng69FHH5U1a9a4Hz9o0CB5/fXXzcVEbb/NnDlTihQpYu7zzJBX2h6z5xs2bJhOew5AagpP1bUBgAO0cZQzZ07zd968eeWmm24yDSJtOKnBgwe7ly1Tpow8//zzMnv2bBk4cKD7druxZF9BzJ8/f7znSc4yAAAAgdJu0kBUVFSUuS00NNQEgDRwNH36dMmRI4dZZvz48Saw9MYbb5jgkGZT2a5du2Yyq/R/T8OGDZOWLVuav6dNm2Zqf37++efSsWNHkymlbTHbM888IwsXLpS5c+dKvXr15OzZs+ZCoj5vt27dzDLly5eXxo0bm7+1DeZJ22O+twEILGRMAQh4zZo1k02bNpnps88+Mw2sdu3ayerVq839c+bMkUaNGplGizbENFB14MABr3WcOXPG/G83wvxJzjKa/q7PkSdPHhMg06t9AAAAGbHdpFlKmiHVtm1b2b9/v2zfvv3/2rt/l6y3OA7g5/4PruEkOQQp5OAmuLkUtghGmCC62Y/NICrBRQhxcMvF/yAXQciWoqWhwMFCx9BJnESXe3kf+EpG9/JwCb9Wrxc8IM/3+5zv4zN9+Jxz3qdcv379XK2TGipb63Z2ds7eW1hYOItIuH37drl79+65ZwwODp5rHF29erWOHWlizc/P1y18uZa6KY2ppjbLfScnJ2V4ePgCfg3gMrBiCvjlpXjKEvTG6upqbVAl1ymF1Pj4eHn27FktvNIwymqpLC//1tevX+tM4X/NuHVyTwqvV69e1aLr/fv3ZWpqqn63BIsCAFy2uinb8VIfJeqgUzMzM2V0dLR8+PCh3L9/v/6dhlcnFhcX64qopaWl2pzK98kYp6en9XpWYAF/FiumgN9Omkd5pTn07t270t3dXR4/flxu3LhRenp66ozg95KB0NvbW0M6/00n9yQINMVeGlRZfp5Zx8xIAgBcRgkNT910fHxcV3t//PixbvFrvH37tl5PbdPISqfURJn8yxa773M3MznXODw8LJ8/f65jN+PdvHmz3Llzp9ZJWXWV643UamlO5cRA4M+gMQX88rLce39/v76y/DtZBQnzHBkZqcVNloZnldTu7m5ZXl6uGQeNzM6tra2VFy9elHv37v1w/E7u+TYgPdkMKehev35dAzuvXbv20/9nAICfWTclRyqNpkzAZXItweRbW1v1esLJm/DxlZWVsr29XU8rTuj55uZm6e/vP/eM58+f18ZSxpiYmChdXV3l1q1b9Vpqs3wmk4d5/vT0dDk4ODj7bJ6fk/6SBZqsq9RvaXS9fPnygn8p4KLYWwL88jY2NmquVHMCXwqehHcODQ3V9x48eFBPk0khluypnPDy9OnTs0yo/J33Hj58+MPxO7mn8enTpzrLl5nFhHs+evSojI2N1W2FAACXrW7KyqecvtfUTcl7mp2dLQMDAzVHKhlSmZxr5DS9hJsnpPzKlStlbm6uTE5OnntGMjYzxpcvX0pfX19ZX1+vq8ojWZ97e3s1YiHj51S+NK2Ojo7OPp+aKzEIT548qVEK+b7ZPgj8nv76uzn/HAAAAP6nN2/e1KypbN/LSckAnbCVDwAAAIBWaEwBAAAA0Apb+QAAAABohRVTAAAAALRCYwoAAACAVmhMAQAAANAKjSkAAAAAWqExBQAAAEArNKYAAAAAaIXGFAAAAACt0JgCAAAAoBUaUwAAAACUNvwDRXN18iCX0CoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# создаем данные для датафрейма\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82],\n",
+    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\"]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Построим histplot\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(df['Баллы'], kde=True, color='blue')\n",
+    "plt.title(\"Распределение Баллов\")\n",
+    "\n",
+    "# Построим scatterplot\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.scatterplot(x=\"Возраст\", y=\"Баллы\", data=df, hue=\"Категория\", palette=\"Set1\")\n",
+    "plt.title(\"Диаграмма рассеяния: Возраст vs Баллы\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e3db813-c976-4244-a25e-200999074720",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "56802999-1661-4d2d-af83-7ea5ea70ec13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 588.236x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# создаем данные для датафрейма\n",
+    "data = {\n",
+    "   \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82],\n",
+    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\"]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Используем pairplot для визуализации взаимосвязей между числовыми признаками\n",
+    "sns.pairplot(df, hue=\"Категория\", palette=\"Set1\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Используем heatmap для визуализации корреляции между числовыми признаками (выбираем только числовые столбцы)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(df[[\"Возраст\", \"Баллы\"]].corr(), annot=True, cmap='coolwarm', linewidths=0.5)\n",
+    "plt.title(\"Тепловая карта корреляции\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf8b848-9fa8-4d50-8c3e-7d889d37c62f",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "6b83e497-8686-4e10-814c-7805776d4372",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████| 100/100 [00:10<00:00,  9.91it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "for i in tqdm(range(100)):\n",
+    "    time.sleep(0.1)  # Симуляция долгого процесса"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee5065d-de15-4cfb-9162-e9dc80cd483b",
+   "metadata": {},
+   "source": [
+    "изучил progress-bar tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ca7ffb56-198d-4b82-b7c0-25028b163d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных:  25%|████            | 1/4 [00:00<00:01,  2.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Гриша\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных:  50%|████████        | 2/4 [00:01<00:01,  2.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Никита\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных:  75%|████████████    | 3/4 [00:01<00:00,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Саня\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных: 100%|████████████████| 4/4 [00:02<00:00,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Кирилл\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# создаем данные для датафрейма\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Используем tqdm для итерации по строкам датафрейма\n",
+    "\n",
+    "\n",
+    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Обработка данных\"):\n",
+    "    time.sleep(0.5)  # Симуляция долгой обработки данных\n",
+    "    # Дополнительная обработка строки \n",
+    "    print(f\"Обработаны данные: {row['Имя']}\")\n",
+    "    \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eb364bf-e4d8-4382-b715-ecdcac12714f",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3d3b5adf-33ae-4eb1-ba68-94b4ab2f3203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Загрузка данных: 100%|█████████████████████████████████████████████████████████████████████| 100/100\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# Используем tqdm с кастомными стилями\n",
+    "for i in tqdm(range(100), desc=\"Загрузка данных\", ncols=100, bar_format=\"{l_bar}{bar}| {n_fmt}/{total_fmt}\"):\n",
+    "    time.sleep(0.01)  # Симуляция долгого процесса\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50cab1a4-c5da-4fd7-b580-dbd7fb80fbfb",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "44b7d39f-fdc4-4c3d-891d-387f2a4060cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 13979 entries, 0 to 13978\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   Country Name  13979 non-null  object \n",
+      " 1   Country Code  13979 non-null  object \n",
+      " 2   Year          13979 non-null  int64  \n",
+      " 3   Value         13979 non-null  float64\n",
+      "dtypes: float64(1), int64(1), object(2)\n",
+      "memory usage: 437.0+ KB\n",
+      "None\n",
+      "               Year         Value\n",
+      "count  13979.000000  1.397900e+04\n",
+      "mean    1994.672866  1.207380e+12\n",
+      "std       17.731413  5.537517e+12\n",
+      "min     1960.000000  1.150263e+04\n",
+      "25%     1980.000000  2.233880e+09\n",
+      "50%     1996.000000  1.672591e+10\n",
+      "75%     2010.000000  2.058542e+11\n",
+      "max     2023.000000  1.054350e+14\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Загрузка данных: 100%|█████████████████████████████████████████████████████████████████████| 100/100\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# Загрузка данных\n",
+    "file_path = \"D:/Files/2 week/data.csv\"  # Укажите путь к вашему файлу\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "# Базовый анализ данных\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "\n",
+    "# Применяем tqdm для обработки\n",
+    "for i in tqdm(range(100), desc=\"Загрузка данных\", ncols=100, bar_format=\"{l_bar}{bar}| {n_fmt}/{total_fmt}\"):\n",
+    "    time.sleep(0.01)  # Симуляция долгого процесса\n",
+    "\n",
+    "# Увеличиваем размер графиков\n",
+    "plt.figure(figsize=(18, 6))\n",
+    "\n",
+    "# 1. Гистограмма распределения ВВП\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.histplot(df['Value'], bins=50, kde=True)\n",
+    "plt.xscale('log')  # Логарифмическая шкала для лучшей читаемости\n",
+    "plt.title(\"Распределение ВВП\")\n",
+    "plt.xlabel(\"Value (log scale)\")\n",
+    "\n",
+    "# 2. Точечный график (scatterplot) зависимости ВВП от года\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.scatterplot(x=df[\"Year\"], y=df[\"Value\"], hue=df[\"Country Name\"], alpha=0.5, s=10, legend=False)\n",
+    "plt.title(\"ВВП по годам\")\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Value\")\n",
+    "\n",
+    "# 3. Boxplot (ящик с усами) для распределения ВВП по странам\n",
+    "plt.subplot(1, 3, 3)\n",
+    "top_countries = df.groupby(\"Country Name\")[\"Value\"].median().nlargest(20).index  # ТОП-20 стран по медианному ВВП\n",
+    "filtered_df = df[df[\"Country Name\"].isin(top_countries)]\n",
+    "sns.boxplot(x=\"Country Name\", y=\"Value\", data=filtered_df)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title(\"Распределение ВВП (ТОП-20 стран)\")\n",
+    "plt.xlabel(\"Country Name\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9659690a-77b9-49f2-b471-835fb6fb9765",
+   "metadata": {},
+   "source": [
+    "Выполнил самостоятельное задание используя таблицу данных по ВВП стран (источник: https://datahub.io/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index e69de29..a60530d 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1 @@
+Горочный Григорий ИНБб-2301
\ No newline at end of file
diff --git a/data.csv b/data.csv
new file mode 100644
index 0000000..f82f608
--- /dev/null
+++ b/data.csv
@@ -0,0 +1,13980 @@
+Country Name,Country Code,Year,Value
+Afghanistan,AFG,2000,3521418059.923445
+Afghanistan,AFG,2001,2813571753.8725324
+Afghanistan,AFG,2002,3825701438.9996333
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+Afghanistan,AFG,2004,5224896718.67782
+Afghanistan,AFG,2005,6203256538.709669
+Afghanistan,AFG,2006,6971758282.293506
+Afghanistan,AFG,2007,9747886187.393929
+Afghanistan,AFG,2008,10109297047.543194
+Afghanistan,AFG,2009,12416152732.056717
+Afghanistan,AFG,2010,15856668555.833595
+Afghanistan,AFG,2011,17805098206.314083
+Afghanistan,AFG,2012,19907329777.587166
+Afghanistan,AFG,2013,20146416757.598675
+Afghanistan,AFG,2014,20497128555.69723
+Afghanistan,AFG,2015,19134221644.732494
+Afghanistan,AFG,2016,18116572395.077213
+Afghanistan,AFG,2017,18753456497.815865
+Afghanistan,AFG,2018,18053222687.412624
+Afghanistan,AFG,2019,18799444490.11278
+Afghanistan,AFG,2020,19955929052.149597
+Afghanistan,AFG,2021,14266499429.874573
+Afghanistan,AFG,2022,14502158192.090395
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+Africa Eastern and Southern,AFE,1964,28049537324.857388
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+Heavily indebted poor countries (HIPC),HPC,1990,167698097617.1251
+Heavily indebted poor countries (HIPC),HPC,1991,182522872477.57516
+Heavily indebted poor countries (HIPC),HPC,1992,137478391074.75812
+Heavily indebted poor countries (HIPC),HPC,1993,142227536704.23697
+Heavily indebted poor countries (HIPC),HPC,1994,121486215558.28221
+Heavily indebted poor countries (HIPC),HPC,1995,141409774172.6868
+Heavily indebted poor countries (HIPC),HPC,1996,154144082762.93637
+Heavily indebted poor countries (HIPC),HPC,1997,161383997622.49945
+Heavily indebted poor countries (HIPC),HPC,1998,166850582010.55502
+Heavily indebted poor countries (HIPC),HPC,1999,167809406850.79425
+Heavily indebted poor countries (HIPC),HPC,2000,181381059271.1327
+Heavily indebted poor countries (HIPC),HPC,2001,174623068211.6252
+Heavily indebted poor countries (HIPC),HPC,2002,189453215972.27676
+Heavily indebted poor countries (HIPC),HPC,2003,214945631968.98584
+Heavily indebted poor countries (HIPC),HPC,2004,247743498935.08423
+Heavily indebted poor countries (HIPC),HPC,2005,283838004106.17816
+Heavily indebted poor countries (HIPC),HPC,2006,332982475731.4968
+Heavily indebted poor countries (HIPC),HPC,2007,397470489216.47504
+Heavily indebted poor countries (HIPC),HPC,2008,472367096833.1147
+Heavily indebted poor countries (HIPC),HPC,2009,468776321860.3896
+Heavily indebted poor countries (HIPC),HPC,2010,509061582442.26056
+Heavily indebted poor countries (HIPC),HPC,2011,564065134052.8373
+Heavily indebted poor countries (HIPC),HPC,2012,583888328733.1459
+Heavily indebted poor countries (HIPC),HPC,2013,655856029913.9088
+Heavily indebted poor countries (HIPC),HPC,2014,694344337474.5431
+Heavily indebted poor countries (HIPC),HPC,2015,667302120360.8035
+Heavily indebted poor countries (HIPC),HPC,2016,674512464051.6886
+Heavily indebted poor countries (HIPC),HPC,2017,724757996634.9285
+Heavily indebted poor countries (HIPC),HPC,2018,774607047567.8475
+Heavily indebted poor countries (HIPC),HPC,2019,803355942387.0563
+Heavily indebted poor countries (HIPC),HPC,2020,811360288084.9884
+Heavily indebted poor countries (HIPC),HPC,2021,902263213997.8197
+Heavily indebted poor countries (HIPC),HPC,2022,981675388899.4773
+Heavily indebted poor countries (HIPC),HPC,2023,1130472157492.994
+High income,HIC,1960,1092681910636.6074
+High income,HIC,1961,1165571615004.348
+High income,HIC,1962,1260544911051.2395
+High income,HIC,1963,1355913364088.0984
+High income,HIC,1964,1482006061458.1074
+High income,HIC,1965,1610880796545.3948
+High income,HIC,1966,1762770956963.8577
+High income,HIC,1967,1883245565212.1829
+High income,HIC,1968,2042819926658.0957
+High income,HIC,1969,2251670738554.677
+High income,HIC,1970,2471430871017.001
+High income,HIC,1971,2745745471453.573
+High income,HIC,1972,3189501756772.0186
+High income,HIC,1973,3869203380449.9243
+High income,HIC,1974,4374887819976.592
+High income,HIC,1975,4881968689614.128
+High income,HIC,1976,5316705508568.393
+High income,HIC,1977,6017649327457.811
+High income,HIC,1978,7224423199614.203
+High income,HIC,1979,8349033768623.11
+High income,HIC,1980,9318973466251.5
+High income,HIC,1981,9463529620224.12
+High income,HIC,1982,9370788287370.254
+High income,HIC,1983,9680375566671.547
+High income,HIC,1984,10091350207473.389
+High income,HIC,1985,10589659647967.176
+High income,HIC,1986,12863823517433.559
+High income,HIC,1987,14914230823362.945
+High income,HIC,1988,16785555755731.361
+High income,HIC,1989,17481687412866.576
+High income,HIC,1990,19577046428304.496
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+High income,HIC,1992,22119166125282.914
+High income,HIC,1993,22272082420753.633
+High income,HIC,1994,23818722465725.473
+High income,HIC,1995,26430046395918.6
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+High income,HIC,1997,26208151153812.51
+High income,HIC,1998,26174130826663.06
+High income,HIC,1999,27403905274743.547
+High income,HIC,2000,28036716903140.23
+High income,HIC,2001,27811072270318.094
+High income,HIC,2002,29022750091985.18
+High income,HIC,2003,32574954634084.754
+High income,HIC,2004,36387608557612.22
+High income,HIC,2005,38623901277744.24
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+High income,HIC,2007,45098647855102.445
+High income,HIC,2008,48338201243505.67
+High income,HIC,2009,44940316093124.67
+High income,HIC,2010,47340327876455.97
+High income,HIC,2011,51524951092552.39
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+High income,HIC,2013,52439877375602.58
+High income,HIC,2014,53310162826089.13
+High income,HIC,2015,49773105280474.15
+High income,HIC,2016,50737236574099.64
+High income,HIC,2017,53336788717601.586
+High income,HIC,2018,56684556889445.77
+High income,HIC,2019,57218295248360.38
+High income,HIC,2020,55791128824226.22
+High income,HIC,2021,62461025675219.586
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+Honduras,HND,1960,335650000
+Honduras,HND,1961,356200000
+Honduras,HND,1962,387750000
+Honduras,HND,1963,410200000
+Honduras,HND,1964,457000000
+Honduras,HND,1965,508650000
+Honduras,HND,1966,549950000
+Honduras,HND,1967,598100000
+Honduras,HND,1968,646800000
+Honduras,HND,1969,668000050
+Honduras,HND,1970,723000000
+Honduras,HND,1971,731000000
+Honduras,HND,1972,802996336.5164857
+Honduras,HND,1973,912499493.7502531
+Honduras,HND,1974,1034500000
+Honduras,HND,1975,1124000000
+Honduras,HND,1976,1347999950
+Honduras,HND,1977,1669499950
+Honduras,HND,1978,3097242093.2
+Honduras,HND,1979,3544281976.3
+Honduras,HND,1980,3968160046
+Honduras,HND,1981,4043894878.6
+Honduras,HND,1982,4266503525.6
+Honduras,HND,1983,4476697184.849999
+Honduras,HND,1984,4915311846.5
+Honduras,HND,1985,5278120712.5
+Honduras,HND,1986,5677828959
+Honduras,HND,1987,6190521241.5
+Honduras,HND,1988,5902717091.416308
+Honduras,HND,1989,5432344901.724138
+Honduras,HND,1990,4923009551.55642
+Honduras,HND,1991,4648668478.567533
+Honduras,HND,1992,4943700431.073683
+Honduras,HND,1993,4926728932.950691
+Honduras,HND,1994,4642280682.142518
+Honduras,HND,1995,5347445005.213764
+Honduras,HND,1996,5215028986.486486
+Honduras,HND,1997,5737099650.190114
+Honduras,HND,1998,6366340265.878878
+Honduras,HND,1999,6414520529.616725
+Honduras,HND,2000,7186638029.058749
+Honduras,HND,2001,7651162302.345604
+Honduras,HND,2002,7858255412.860379
+Honduras,HND,2003,8230391346.672281
+Honduras,HND,2004,8869299234.337696
+Honduras,HND,2005,9757012696.562117
+Honduras,HND,2006,10917477066.142908
+Honduras,HND,2007,12361257680.562687
+Honduras,HND,2008,13881731875.746613
+Honduras,HND,2009,14587496229.18111
+Honduras,HND,2010,15839344591.984165
+Honduras,HND,2011,17710275685.407448
+Honduras,HND,2012,18528554397.56951
+Honduras,HND,2013,18499729214.904022
+Honduras,HND,2014,19756533971.869846
+Honduras,HND,2015,20979791685.416042
+Honduras,HND,2016,21717604952.183525
+Honduras,HND,2017,23136247990.597725
+Honduras,HND,2018,24067750760.49897
+Honduras,HND,2019,24882225741.811867
+Honduras,HND,2020,23352232483.75501
+Honduras,HND,2021,28144331506.614887
+Honduras,HND,2022,31426041806.798485
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+"Hong Kong SAR, China",HKG,1960,1320796651.694569
+"Hong Kong SAR, China",HKG,1961,1383681651.1377556
+"Hong Kong SAR, China",HKG,1962,1612346412.2647462
+"Hong Kong SAR, China",HKG,1963,1935298266.45384
+"Hong Kong SAR, China",HKG,1964,2206466461.264338
+"Hong Kong SAR, China",HKG,1965,2435078534.0314136
+"Hong Kong SAR, China",HKG,1966,2489845016.6489425
+"Hong Kong SAR, China",HKG,1967,2692474989.125707
+"Hong Kong SAR, China",HKG,1968,2716964388.424184
+"Hong Kong SAR, China",HKG,1969,3189740055.1398187
+"Hong Kong SAR, China",HKG,1970,3800766535.6208773
+"Hong Kong SAR, China",HKG,1971,4476001946.014864
+"Hong Kong SAR, China",HKG,1972,5710090213.150349
+"Hong Kong SAR, China",HKG,1973,8030117555.620325
+"Hong Kong SAR, China",HKG,1974,9388695366.845781
+"Hong Kong SAR, China",HKG,1975,10048089558.063587
+"Hong Kong SAR, China",HKG,1976,12876366008.8077
+"Hong Kong SAR, China",HKG,1977,15719433719.43372
+"Hong Kong SAR, China",HKG,1978,18315136409.142666
+"Hong Kong SAR, China",HKG,1979,22526184533.17001
+"Hong Kong SAR, China",HKG,1980,28861857810.65147
+"Hong Kong SAR, China",HKG,1981,31055226088.694305
+"Hong Kong SAR, China",HKG,1982,32291215843.64333
+"Hong Kong SAR, China",HKG,1983,29907227184.178974
+"Hong Kong SAR, China",HKG,1984,33511383985.674088
+"Hong Kong SAR, China",HKG,1985,35699772165.709335
+"Hong Kong SAR, China",HKG,1986,41075396884.89521
+"Hong Kong SAR, China",HKG,1987,50622896162.60058
+"Hong Kong SAR, China",HKG,1988,59707404560.59441
+"Hong Kong SAR, China",HKG,1989,68790219177.97331
+"Hong Kong SAR, China",HKG,1990,76928784620.81581
+"Hong Kong SAR, China",HKG,1991,88959997899.92932
+"Hong Kong SAR, China",HKG,1992,104272507639.28247
+"Hong Kong SAR, China",HKG,1993,120354212475.00026
+"Hong Kong SAR, China",HKG,1994,135811771026.33049
+"Hong Kong SAR, China",HKG,1995,144652295363.66672
+"Hong Kong SAR, China",HKG,1996,159718183550.73416
+"Hong Kong SAR, China",HKG,1997,177353174849.7142
+"Hong Kong SAR, China",HKG,1998,168885443660.07248
+"Hong Kong SAR, China",HKG,1999,165768095391.55655
+"Hong Kong SAR, China",HKG,2000,171668891194.3487
+"Hong Kong SAR, China",HKG,2001,169404327616.60522
+"Hong Kong SAR, China",HKG,2002,166348866131.0282
+"Hong Kong SAR, China",HKG,2003,161385558801.8108
+"Hong Kong SAR, China",HKG,2004,169099768875.1926
+"Hong Kong SAR, China",HKG,2005,181569311742.21292
+"Hong Kong SAR, China",HKG,2006,193535442896.36505
+"Hong Kong SAR, China",HKG,2007,211596944503.79977
+"Hong Kong SAR, China",HKG,2008,219278749139.7337
+"Hong Kong SAR, China",HKG,2009,214047795659.0447
+"Hong Kong SAR, China",HKG,2010,228638668727.29083
+"Hong Kong SAR, China",HKG,2011,248513617677.28674
+"Hong Kong SAR, China",HKG,2012,262628865879.69678
+"Hong Kong SAR, China",HKG,2013,275696879834.9665
+"Hong Kong SAR, China",HKG,2014,291459995978.8927
+"Hong Kong SAR, China",HKG,2015,309385622601.3481
+"Hong Kong SAR, China",HKG,2016,320860317562.5624
+"Hong Kong SAR, China",HKG,2017,341273289534.46594
+"Hong Kong SAR, China",HKG,2018,361731070995.72626
+"Hong Kong SAR, China",HKG,2019,363074545072.3891
+"Hong Kong SAR, China",HKG,2020,344943149590.0583
+"Hong Kong SAR, China",HKG,2021,368954169748.81805
+"Hong Kong SAR, China",HKG,2022,358696261481.1598
+"Hong Kong SAR, China",HKG,2023,382054574298.52905
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+Hungary,HUN,1971,6271045034.468295
+Hungary,HUN,1972,7355242314.224068
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+Hungary,HUN,1975,11383139029.43452
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+Hungary,HUN,1978,17230354544.824234
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+Hungary,HUN,1982,24062916721.31206
+Hungary,HUN,1983,21838893330.60519
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+Hungary,HUN,1998,48706787306.25736
+Hungary,HUN,1999,49073380173.71591
+Hungary,HUN,2000,47218405892.42581
+Hungary,HUN,2001,53749989092.01972
+Hungary,HUN,2002,67608919144.368355
+Hungary,HUN,2003,85285062818.00766
+Hungary,HUN,2004,104120820258.66873
+Hungary,HUN,2005,113211158292.9365
+Hungary,HUN,2006,115715618613.05196
+Hungary,HUN,2007,140186716681.42496
+Hungary,HUN,2008,158325614580.628
+Hungary,HUN,2009,131069255620.56659
+Hungary,HUN,2010,132175349953.71332
+Hungary,HUN,2011,141942264554.47513
+Hungary,HUN,2012,128814279315.1317
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+Hungary,HUN,2015,125174166987.37169
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+Hungary,HUN,2020,157288955508.17633
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+IBRD only,IBD,1963,361188678549.8145
+IBRD only,IBD,1964,409917161582.3877
+IBRD only,IBD,1965,449606624512.9089
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+IBRD only,IBD,1968,500737527389.4028
+IBRD only,IBD,1969,557158810956.071
+IBRD only,IBD,1970,606864433924.8542
+IBRD only,IBD,1971,658436487864.558
+IBRD only,IBD,1972,741446464309.9623
+IBRD only,IBD,1973,935257264659.15
+IBRD only,IBD,1974,1166117926870.0496
+IBRD only,IBD,1975,1283182211382.1401
+IBRD only,IBD,1976,1384184182987.9087
+IBRD only,IBD,1977,1572414965445.1057
+IBRD only,IBD,1978,1688541859448.7246
+IBRD only,IBD,1979,2054980853162.8042
+IBRD only,IBD,1980,2495337530229.9053
+IBRD only,IBD,1981,2679716239809.967
+IBRD only,IBD,1982,2598534706334.6484
+IBRD only,IBD,1983,2671753233490.0737
+IBRD only,IBD,1984,2773573376450.62
+IBRD only,IBD,1985,2856094151218.432
+IBRD only,IBD,1986,2906164344615.2295
+IBRD only,IBD,1987,2962762817058.3257
+IBRD only,IBD,1988,3156451762787.7754
+IBRD only,IBD,1989,3174083997481.793
+IBRD only,IBD,1990,3560303900038.582
+IBRD only,IBD,1991,3448774162440.4175
+IBRD only,IBD,1992,3624577197344.4175
+IBRD only,IBD,1993,3934211426064.5195
+IBRD only,IBD,1994,4339185750582.6406
+IBRD only,IBD,1995,4916948102663.379
+IBRD only,IBD,1996,5368909819962.314
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+IBRD only,IBD,1998,5541398163455.25
+IBRD only,IBD,1999,5397562849862.584
+IBRD only,IBD,2000,5873248460100.093
+IBRD only,IBD,2001,5954917913637.387
+IBRD only,IBD,2002,6043651266693.341
+IBRD only,IBD,2003,6788178855352.027
+IBRD only,IBD,2004,8085419494002.896
+IBRD only,IBD,2005,9657998889452.797
+IBRD only,IBD,2006,11453342785003.834
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+IBRD only,IBD,2009,16541826277696.307
+IBRD only,IBD,2010,20111983284711.48
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+IBRD only,IBD,2014,27592783808400.59
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+Iceland,ISL,1962,289200704.3825417
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+Iceland,ISL,1965,531569563.88859105
+Iceland,ISL,1966,638349755.2219319
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+Iceland,ISL,1975,1406875081.327261
+Iceland,ISL,1976,1669488389.965417
+Iceland,ISL,1977,2208509076.2809877
+Iceland,ISL,1978,2511826196.009
+Iceland,ISL,1979,2853435053.8854227
+Iceland,ISL,1980,3381419251.2923126
+Iceland,ISL,1981,3492997010.049556
+Iceland,ISL,1982,3206626644.537101
+Iceland,ISL,1983,2765950335.7109504
+Iceland,ISL,1984,2864441387.405068
+Iceland,ISL,1985,2984052356.550712
+Iceland,ISL,1986,3989622739.2821174
+Iceland,ISL,1987,5520318404.641495
+Iceland,ISL,1988,6106635816.2458725
+Iceland,ISL,1989,5672569449.070682
+Iceland,ISL,1990,6468736355.556776
+Iceland,ISL,1991,6909730288.170614
+Iceland,ISL,1992,7080981738.056056
+Iceland,ISL,1993,6218581531.643473
+Iceland,ISL,1994,6389460342.786556
+Iceland,ISL,1995,7123633418.197389
+Iceland,ISL,1996,7426082270.676692
+Iceland,ISL,1997,7569672925.337391
+Iceland,ISL,1998,8503693098.622713
+Iceland,ISL,1999,8982047589.489502
+Iceland,ISL,2000,9025660361.75842
+Iceland,ISL,2001,8234846804.605819
+Iceland,ISL,2002,9318395054.859337
+Iceland,ISL,2003,11429333037.84432
+Iceland,ISL,2004,13825302535.7699
+Iceland,ISL,2005,16852963067.049637
+Iceland,ISL,2006,17465318552.294098
+Iceland,ISL,2007,21652505596.75279
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+IDA & IBRD total,IBT,1974,1305873068194.0867
+IDA & IBRD total,IBT,1975,1454096413943.2312
+IDA & IBRD total,IBT,1976,1562102647645.7788
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+IDA & IBRD total,IBT,1982,3046965333072.1987
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+IDA & IBRD total,IBT,1986,3213192269731.733
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+IDA & IBRD total,IBT,2018,32964096579686.016
+IDA & IBRD total,IBT,2019,33722356189565.855
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+IDA blend,IDB,1960,12304340519.437931
+IDA blend,IDB,1961,13173849239.235147
+IDA blend,IDB,1962,14087733811.174345
+IDA blend,IDB,1963,14916251701.126812
+IDA blend,IDB,1964,16283554669.880676
+IDA blend,IDB,1965,17681807373.14332
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+IDA blend,IDB,1967,19334017378.779907
+IDA blend,IDB,1968,20403706829.551464
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+IDA blend,IDB,1970,32052680442.7769
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+IDA blend,IDB,1973,35789060006.8562
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+IDA blend,IDB,1976,71358695957.48854
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+IDA blend,IDB,1979,100444794858.7986
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+IDA blend,IDB,1982,226070570770.67218
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+IDA blend,IDB,1987,136898130940.9204
+IDA blend,IDB,1988,144303058843.68182
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+IDA blend,IDB,1992,153779820642.0538
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+IDA blend,IDB,1995,255612158933.52695
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+IDA blend,IDB,1999,183774429526.0243
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+IDA blend,IDB,2008,677188107675.7102
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+IDA only,IDX,1962,33656896744.69395
+IDA only,IDX,1963,35196758489.04887
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+IDA only,IDX,1965,41186080422.793625
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+IDA only,IDX,1974,86863214691.36337
+IDA only,IDX,1975,105464227616.34944
+IDA only,IDX,1976,100668175789.60442
+IDA only,IDX,1977,109704597324.3775
+IDA only,IDX,1978,127591069020.59163
+IDA only,IDX,1979,143005783535.95557
+IDA only,IDX,1980,163265145364.36432
+IDA only,IDX,1981,170754419012.08243
+IDA only,IDX,1982,171592411137.81265
+IDA only,IDX,1983,174451138973.80054
+IDA only,IDX,1984,182330874019.79776
+IDA only,IDX,1985,194539320065.9348
+IDA only,IDX,1986,174407396608.2547
+IDA only,IDX,1987,181510608388.10736
+IDA only,IDX,1988,193390531239.40918
+IDA only,IDX,1989,201851599985.29355
+IDA only,IDX,1990,232816355943.8044
+IDA only,IDX,1991,252129992740.6574
+IDA only,IDX,1992,211672027107.27893
+IDA only,IDX,1993,219777744615.82022
+IDA only,IDX,1994,213964030903.19003
+IDA only,IDX,1995,224492421427.76117
+IDA only,IDX,1996,243038325367.7629
+IDA only,IDX,1997,253429945077.79953
+IDA only,IDX,1998,262082551568.0948
+IDA only,IDX,1999,268212792940.09537
+IDA only,IDX,2000,291557552376.7759
+IDA only,IDX,2001,285482351627.00745
+IDA only,IDX,2002,303011338636.68097
+IDA only,IDX,2003,341509684072.07446
+IDA only,IDX,2004,386660282055.16534
+IDA only,IDX,2005,440100803249.34784
+IDA only,IDX,2006,506608850466.3347
+IDA only,IDX,2007,601253783985.3998
+IDA only,IDX,2008,727719101177.9065
+IDA only,IDX,2009,739884145330.9576
+IDA only,IDX,2010,840161104102.0992
+IDA only,IDX,2011,939785129456.0404
+IDA only,IDX,2012,942114904365.1609
+IDA only,IDX,2013,1026527917428.5073
+IDA only,IDX,2014,1097548704598.0332
+IDA only,IDX,2015,1093640032209.0707
+IDA only,IDX,2016,1156608800300.8652
+IDA only,IDX,2017,1247733925328.4648
+IDA only,IDX,2018,1327890357838.6838
+IDA only,IDX,2019,1397670771530.9763
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+IDA only,IDX,2022,1651361994353.6184
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+IDA total,IDA,1960,41098321652.643524
+IDA total,IDA,1961,44012577180.432236
+IDA total,IDA,1962,46765307423.11745
+IDA total,IDA,1963,49114642121.75161
+IDA total,IDA,1964,52679532945.22846
+IDA total,IDA,1965,57731457660.490746
+IDA total,IDA,1966,62126772802.83831
+IDA total,IDA,1967,64429850042.316795
+IDA total,IDA,1968,67855625543.42733
+IDA total,IDA,1969,75426324755.10237
+IDA total,IDA,1970,84861825817.92558
+IDA total,IDA,1971,84960443693.54733
+IDA total,IDA,1972,90623495176.74626
+IDA total,IDA,1973,104416522876.93674
+IDA total,IDA,1974,137918904112.60803
+IDA total,IDA,1975,163903050188.54425
+IDA total,IDA,1976,172256247640.56158
+IDA total,IDA,1977,185053978781.2845
+IDA total,IDA,1978,209143768718.97714
+IDA total,IDA,1979,243716595033
+IDA total,IDA,1980,293400954626.97705
+IDA total,IDA,1981,426223819976.08484
+IDA total,IDA,1982,404486564860.62445
+IDA total,IDA,1983,349645458108.88873
+IDA total,IDA,1984,331449107693.95746
+IDA total,IDA,1985,343779195534.5611
+IDA total,IDA,1986,309190328533.65027
+IDA total,IDA,1987,319495458117.7952
+IDA total,IDA,1988,338669681386.6566
+IDA total,IDA,1989,343828929532.5688
+IDA total,IDA,1990,378856660652.6743
+IDA total,IDA,1991,409092280613.03076
+IDA total,IDA,1992,364758759974.76874
+IDA total,IDA,1993,382908861851.4712
+IDA total,IDA,1994,395889357665.3764
+IDA total,IDA,1995,481341765046.5578
+IDA total,IDA,1996,554633517949.8015
+IDA total,IDA,1997,580664386042.9703
+IDA total,IDA,1998,604570449948.6671
+IDA total,IDA,1999,450872755018.96246
+IDA total,IDA,2000,517721477276.15155
+IDA total,IDA,2001,511239299618.94403
+IDA total,IDA,2002,550995965118.9045
+IDA total,IDA,2003,621400206928.4497
+IDA total,IDA,2004,726522702342.7588
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+IDA total,IDA,2006,1005777469306.554
+IDA total,IDA,2007,1179708743319.3362
+IDA total,IDA,2008,1404907208853.6165
+IDA total,IDA,2009,1371296682305.8477
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+IDA total,IDA,2012,1883810771583.7258
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+India,IND,1960,37029883876.183876
+India,IND,1961,39232435784.03579
+India,IND,1962,42161481858.08186
+India,IND,1963,48421923459.12346
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+India,IND,1965,59556105229.00523
+India,IND,1966,45581230504.07104
+India,IND,1967,50134942204
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+India,IND,1969,58447995017.333336
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+India,IND,1972,71464700666.98688
+India,IND,1973,85517673172.55133
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+India,IND,1975,98473832017.32423
+India,IND,1976,102716451979.68039
+India,IND,1977,121486641441.3093
+India,IND,1978,137302319828.99506
+India,IND,1979,152995442497.70932
+India,IND,1980,186328579302.06827
+India,IND,1981,193491368445.57254
+India,IND,1982,200715624830.9017
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+India,IND,1992,288208070278.0129
+India,IND,1993,279295648982.52924
+India,IND,1994,327274843459.429
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+India,IND,1997,415867563592.82904
+India,IND,1998,421351317224.9413
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+India,IND,2003,607700687237.3179
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+India,IND,2008,1198895139005.919
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+India,IND,2013,1856721507621.4607
+India,IND,2014,2039126479155.269
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+"Iran, Islamic Rep.",IRN,1960,4199134389.9036956
+"Iran, Islamic Rep.",IRN,1961,4426949094.840303
+"Iran, Islamic Rep.",IRN,1962,4693566416.4834585
+"Iran, Islamic Rep.",IRN,1963,4928628018.387591
+"Iran, Islamic Rep.",IRN,1964,5379845647.7013855
+"Iran, Islamic Rep.",IRN,1965,6197319929.040383
+"Iran, Islamic Rep.",IRN,1966,6789938671.750944
+"Iran, Islamic Rep.",IRN,1967,7555383690.16
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+"Iran, Islamic Rep.",IRN,1969,9743089607.495869
+"Iran, Islamic Rep.",IRN,1970,10976245153.589386
+"Iran, Islamic Rep.",IRN,1971,13731802833.0363
+"Iran, Islamic Rep.",IRN,1972,17153463263.108646
+"Iran, Islamic Rep.",IRN,1973,27081698249.508286
+"Iran, Islamic Rep.",IRN,1974,46209092072.13827
+"Iran, Islamic Rep.",IRN,1975,51776222349.8869
+"Iran, Islamic Rep.",IRN,1976,68055295080.75381
+"Iran, Islamic Rep.",IRN,1977,80600122701.9632
+"Iran, Islamic Rep.",IRN,1978,77994316621.48138
+"Iran, Islamic Rep.",IRN,1979,90391877325.9285
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+"Iran, Islamic Rep.",IRN,1982,125948756439.48515
+"Iran, Islamic Rep.",IRN,1983,156365156618.24072
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+Kyrgyz Republic,KGZ,2022,12134931017.946526
+Kyrgyz Republic,KGZ,2023,13987627908.838139
+Lao PDR,LAO,1984,1757142855.9183671
+Lao PDR,LAO,1985,2366666615.5555553
+Lao PDR,LAO,1986,1776842097.1634347
+Lao PDR,LAO,1987,1087273103.6963859
+Lao PDR,LAO,1988,598961269.2978786
+Lao PDR,LAO,1989,714046821.093797
+Lao PDR,LAO,1990,865559879.4004065
+Lao PDR,LAO,1991,1028087972.3108478
+Lao PDR,LAO,1992,1127806944.6151268
+Lao PDR,LAO,1993,1327748689.8809426
+Lao PDR,LAO,1994,1543606345.1168365
+Lao PDR,LAO,1995,1763536304.5396364
+Lao PDR,LAO,1996,1873671550.3463552
+Lao PDR,LAO,1997,1747011857.3310688
+Lao PDR,LAO,1998,1280177838.7190535
+Lao PDR,LAO,1999,1454430642.449592
+Lao PDR,LAO,2000,1731198022.4676313
+Lao PDR,LAO,2001,1768619058.3464744
+Lao PDR,LAO,2002,1758176653.0774584
+Lao PDR,LAO,2003,2023324407.2841384
+Lao PDR,LAO,2004,2366398119.863208
+Lao PDR,LAO,2005,2735558734.737903
+Lao PDR,LAO,2006,3455030060.978498
+Lao PDR,LAO,2007,4223152739.0405164
+Lao PDR,LAO,2008,5446433157.401138
+Lao PDR,LAO,2009,5836137329.633054
+Lao PDR,LAO,2010,7131771014.704564
+Lao PDR,LAO,2011,8750104617.173029
+Lao PDR,LAO,2012,10192846339.40287
+Lao PDR,LAO,2013,11983252626.545183
+Lao PDR,LAO,2014,13279245886.385435
+Lao PDR,LAO,2015,14426380125.648829
+Lao PDR,LAO,2016,15912501722.614256
+Lao PDR,LAO,2017,17071155481.49936
+Lao PDR,LAO,2018,18141641089.796326
+Lao PDR,LAO,2019,18740561512.555496
+Lao PDR,LAO,2020,18981805250.242397
+Lao PDR,LAO,2021,18827148530.934723
+Lao PDR,LAO,2022,15468785203.753174
+Lao PDR,LAO,2023,15843155731.25518
+Late-demographic dividend,LTE,1960,236359697329.26685
+Late-demographic dividend,LTE,1961,215773982350.5694
+Late-demographic dividend,LTE,1962,215218301251.24612
+Late-demographic dividend,LTE,1963,228482029485.83868
+Late-demographic dividend,LTE,1964,263093007398.16443
+Late-demographic dividend,LTE,1965,297806778465.67114
+Late-demographic dividend,LTE,1966,322756407405.05963
+Late-demographic dividend,LTE,1967,317115282370.30304
+Late-demographic dividend,LTE,1968,316338717706.1487
+Late-demographic dividend,LTE,1969,354454622911.11707
+Late-demographic dividend,LTE,1970,402926758998.3742
+Late-demographic dividend,LTE,1971,443431301620.6312
+Late-demographic dividend,LTE,1972,504217949362.97546
+Late-demographic dividend,LTE,1973,633970164011.254
+Late-demographic dividend,LTE,1974,733418812229.9064
+Late-demographic dividend,LTE,1975,788838956828.1168
+Late-demographic dividend,LTE,1976,812873567659.8868
+Late-demographic dividend,LTE,1977,946458120173.975
+Late-demographic dividend,LTE,1978,939558602435.4497
+Late-demographic dividend,LTE,1979,1163005699434.633
+Late-demographic dividend,LTE,1980,1372119747166.9578
+Late-demographic dividend,LTE,1981,1415544964401.367
+Late-demographic dividend,LTE,1982,1400418314722.288
+Late-demographic dividend,LTE,1983,1461396318226.1274
+Late-demographic dividend,LTE,1984,1552335173187.2617
+Late-demographic dividend,LTE,1985,1660675505162.1038
+Late-demographic dividend,LTE,1986,1684896070685.4746
+Late-demographic dividend,LTE,1987,1746603588097.057
+Late-demographic dividend,LTE,1988,1907970208476.7786
+Late-demographic dividend,LTE,1989,1913194180510.283
+Late-demographic dividend,LTE,1990,1966143155409.277
+Late-demographic dividend,LTE,1991,1983419918605.2493
+Late-demographic dividend,LTE,1992,2022186054608.0432
+Late-demographic dividend,LTE,1993,2105333938710.2498
+Late-demographic dividend,LTE,1994,2442945412790.5786
+Late-demographic dividend,LTE,1995,3018292160017.546
+Late-demographic dividend,LTE,1996,3329594709957.7246
+Late-demographic dividend,LTE,1997,3486500370030.624
+Late-demographic dividend,LTE,1998,3361066467991.76
+Late-demographic dividend,LTE,1999,3106274396328.617
+Late-demographic dividend,LTE,2000,3410313487927.403
+Late-demographic dividend,LTE,2001,3525091029820.1104
+Late-demographic dividend,LTE,2002,3736796107267.6753
+Late-demographic dividend,LTE,2003,4257947205698.707
+Late-demographic dividend,LTE,2004,5117293242644.111
+Late-demographic dividend,LTE,2005,6172905218377.231
+Late-demographic dividend,LTE,2006,7448573743924.262
+Late-demographic dividend,LTE,2007,9382447414660.309
+Late-demographic dividend,LTE,2008,11663554908011.691
+Late-demographic dividend,LTE,2009,11269360521229.5
+Late-demographic dividend,LTE,2010,13564267760890.17
+Late-demographic dividend,LTE,2011,16530365963615.514
+Late-demographic dividend,LTE,2012,17680657970635.266
+Late-demographic dividend,LTE,2013,19042359863611.242
+Late-demographic dividend,LTE,2014,19776331354297.383
+Late-demographic dividend,LTE,2015,18550881244485.992
+Late-demographic dividend,LTE,2016,18593573738109.332
+Late-demographic dividend,LTE,2017,20631078375844.242
+Late-demographic dividend,LTE,2018,22623622462864.816
+Late-demographic dividend,LTE,2019,23050208314364.02
+Late-demographic dividend,LTE,2020,22531183291578.273
+Late-demographic dividend,LTE,2021,26872794398412.047
+Late-demographic dividend,LTE,2022,28062557325965.145
+Late-demographic dividend,LTE,2023,28341396914858.266
+Latin America & Caribbean,LCN,1989,1007476718921.5654
+Latin America & Caribbean,LCN,1990,1102119409745.3826
+Latin America & Caribbean,LCN,1991,1189975815419.2734
+Latin America & Caribbean,LCN,1992,1298654841940.8435
+Latin America & Caribbean,LCN,1993,1536673815797.37
+Latin America & Caribbean,LCN,1994,1794992207468.049
+Latin America & Caribbean,LCN,1995,1941685792038.1758
+Latin America & Caribbean,LCN,1996,2101023530809.9414
+Latin America & Caribbean,LCN,1997,2303337106936.646
+Latin America & Caribbean,LCN,1998,2331650072097.7466
+Latin America & Caribbean,LCN,1999,2107238195958.9082
+Latin America & Caribbean,LCN,2000,2326171956618.827
+Latin America & Caribbean,LCN,2001,2281659001901.305
+Latin America & Caribbean,LCN,2002,2051651216847.5154
+Latin America & Caribbean,LCN,2003,2090649676533.063
+Latin America & Caribbean,LCN,2004,2404140329731.789
+Latin America & Caribbean,LCN,2005,2901840928620.712
+Latin America & Caribbean,LCN,2006,3395773013103.3794
+Latin America & Caribbean,LCN,2007,3997430459125.194
+Latin America & Caribbean,LCN,2008,4642685980825.886
+Latin America & Caribbean,LCN,2009,4355865396934.769
+Latin America & Caribbean,LCN,2010,5395521584867.198
+Latin America & Caribbean,LCN,2011,6137298312571.182
+Latin America & Caribbean,LCN,2012,6209024743500.111
+Latin America & Caribbean,LCN,2013,6362333301077.531
+Latin America & Caribbean,LCN,2014,6483202078515.132
+Latin America & Caribbean,LCN,2015,5418289197382.295
+Latin America & Caribbean,LCN,2016,5283310849435.272
+Latin America & Caribbean,LCN,2017,5866482345855.606
+Latin America & Caribbean,LCN,2018,5742638741431.275
+Latin America & Caribbean,LCN,2019,5659965575492.011
+Latin America & Caribbean,LCN,2020,4804924789054.883
+Latin America & Caribbean,LCN,2021,5578001135446.182
+Latin America & Caribbean,LCN,2022,6341680448313.757
+Latin America & Caribbean,LCN,2023,7094329636592.513
+Latin America & Caribbean (excluding high income),LAC,1989,876402463262.6858
+Latin America & Caribbean (excluding high income),LAC,1990,957593725702.9581
+Latin America & Caribbean (excluding high income),LAC,1991,1031036432080.7441
+Latin America & Caribbean (excluding high income),LAC,1992,1119017965058.758
+Latin America & Caribbean (excluding high income),LAC,1993,1348397983096.4402
+Latin America & Caribbean (excluding high income),LAC,1994,1592910332496.8948
+Latin America & Caribbean (excluding high income),LAC,1995,1697838269009.4563
+Latin America & Caribbean (excluding high income),LAC,1996,1853063931965.103
+Latin America & Caribbean (excluding high income),LAC,1997,2021893648691.252
+Latin America & Caribbean (excluding high income),LAC,1998,2038842311934.926
+Latin America & Caribbean (excluding high income),LAC,1999,1810327579938.6978
+Latin America & Caribbean (excluding high income),LAC,2000,2001496152611.1226
+Latin America & Caribbean (excluding high income),LAC,2001,1951435655114.4248
+Latin America & Caribbean (excluding high income),LAC,2002,1757081641119.6235
+Latin America & Caribbean (excluding high income),LAC,2003,1793945251035.7202
+Latin America & Caribbean (excluding high income),LAC,2004,2043684160277.3213
+Latin America & Caribbean (excluding high income),LAC,2005,2470502060472.195
+Latin America & Caribbean (excluding high income),LAC,2006,2881093926514.893
+Latin America & Caribbean (excluding high income),LAC,2007,3401597415158.732
+Latin America & Caribbean (excluding high income),LAC,2008,3931407891415.4766
+Latin America & Caribbean (excluding high income),LAC,2009,3643244830292.974
+Latin America & Caribbean (excluding high income),LAC,2010,4556436688834.758
+Latin America & Caribbean (excluding high income),LAC,2011,5320445093655.743
+Latin America & Caribbean (excluding high income),LAC,2012,5297857656983.352
+Latin America & Caribbean (excluding high income),LAC,2013,5436631031505.861
+Latin America & Caribbean (excluding high income),LAC,2014,5457289044548.768
+Latin America & Caribbean (excluding high income),LAC,2015,4634564315170.454
+Latin America & Caribbean (excluding high income),LAC,2016,4496975505130.537
+Latin America & Caribbean (excluding high income),LAC,2017,5012883307321.984
+Latin America & Caribbean (excluding high income),LAC,2018,4870399271215.6045
+Latin America & Caribbean (excluding high income),LAC,2019,4804547155275.126
+Latin America & Caribbean (excluding high income),LAC,2020,4046822646872.3726
+Latin America & Caribbean (excluding high income),LAC,2021,4690738841614.132
+Latin America & Caribbean (excluding high income),LAC,2022,5389510951677.686
+Latin America & Caribbean (excluding high income),LAC,2023,6050580948759.951
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1989,944566530515.018
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1990,1034598506513.3105
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1991,1124702232048.2854
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1992,1232756088301.5469
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1993,1467156610889.901
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1994,1715278789092.88
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1995,1856211576641.3835
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1996,2017401470167.4136
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1997,2212848167141.8213
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1998,2234145456857.453
+Latin America & the Caribbean (IDA & IBRD countries),TLA,1999,2003326360339.101
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2000,2214793108979.011
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2001,2161567505918.137
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2002,1927469572333.205
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2003,1960572727991.551
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2004,2264753929049.2812
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2005,2751720692301.9966
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2006,3234376593372.225
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2007,3826508335402.8267
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2008,4465251653168.29
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2009,4176119188574.45
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2010,5210710546171.905
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2011,5940252657561.232
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2012,6005862490255.015
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2013,6154592516614.851
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2014,6270841236825.202
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2015,5194835549016.913
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2016,5053700630074.006
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2017,5631949735822.711
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2018,5506072829643.664
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2019,5413987316641.175
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2020,4561296585538.663
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2021,5310337203631.617
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2022,6045250902748.987
+Latin America & the Caribbean (IDA & IBRD countries),TLA,2023,6772781697090.741
+Latvia,LVA,1995,5789128636.6229315
+Latvia,LVA,1996,5975248851.454824
+Latvia,LVA,1997,6527926445.681104
+Latvia,LVA,1998,7166275467.651614
+Latvia,LVA,1999,7533788133.55753
+Latvia,LVA,2000,7958852838.933951
+Latvia,LVA,2001,8362398701.589434
+Latvia,LVA,2002,9557031605.275124
+Latvia,LVA,2003,11771975156.80728
+Latvia,LVA,2004,14435700533.368023
+Latvia,LVA,2005,17003459863.098942
+Latvia,LVA,2006,21570076498.620518
+Latvia,LVA,2007,31054350977.97839
+Latvia,LVA,2008,35854274228.9139
+Latvia,LVA,2009,26410909090.90909
+Latvia,LVA,2010,23956163076.55748
+Latvia,LVA,2011,27474380566.26504
+Latvia,LVA,2012,28169902669.378365
+Latvia,LVA,2013,30204783461.84881
+Latvia,LVA,2014,31386896487.040672
+Latvia,LVA,2015,27263090547.061707
+Latvia,LVA,2016,28083597512.484116
+Latvia,LVA,2017,30483806017.831818
+Latvia,LVA,2018,34429023435.02115
+Latvia,LVA,2019,34225547537.074455
+Latvia,LVA,2020,34390910338.9604
+Latvia,LVA,2021,39443126174.43347
+Latvia,LVA,2022,40422521943.44179
+Latvia,LVA,2023,43627078481.28705
+Least developed countries: UN classification,LDC,1960,25396340997.127872
+Least developed countries: UN classification,LDC,1961,27210618722.561977
+Least developed countries: UN classification,LDC,1962,28791232467.254963
+Least developed countries: UN classification,LDC,1963,30202790676.206306
+Least developed countries: UN classification,LDC,1964,31642936960.58793
+Least developed countries: UN classification,LDC,1965,34376613040.483406
+Least developed countries: UN classification,LDC,1966,37271686786.00302
+Least developed countries: UN classification,LDC,1967,39756391346.56397
+Least developed countries: UN classification,LDC,1968,41993452462.145515
+Least developed countries: UN classification,LDC,1969,45398307340.834015
+Least developed countries: UN classification,LDC,1970,43924106346.483284
+Least developed countries: UN classification,LDC,1971,45503456420.03081
+Least developed countries: UN classification,LDC,1972,46497667763.415955
+Least developed countries: UN classification,LDC,1973,56115642779.66818
+Least developed countries: UN classification,LDC,1974,70169680467.1637
+Least developed countries: UN classification,LDC,1975,86232150421.10028
+Least developed countries: UN classification,LDC,1976,79141108671.18828
+Least developed countries: UN classification,LDC,1977,84799217600.39177
+Least developed countries: UN classification,LDC,1978,98673333687.55481
+Least developed countries: UN classification,LDC,1979,110482187765.816
+Least developed countries: UN classification,LDC,1980,124275806288.40773
+Least developed countries: UN classification,LDC,1981,130944751635.4153
+Least developed countries: UN classification,LDC,1982,131526119023.53981
+Least developed countries: UN classification,LDC,1983,132942322285.83388
+Least developed countries: UN classification,LDC,1984,139143974827.19647
+Least developed countries: UN classification,LDC,1985,152679351682.86774
+Least developed countries: UN classification,LDC,1986,129919742514.94466
+Least developed countries: UN classification,LDC,1987,138575407039.5359
+Least developed countries: UN classification,LDC,1988,151408311246.3332
+Least developed countries: UN classification,LDC,1989,162003196598.6238
+Least developed countries: UN classification,LDC,1990,188377840875.20572
+Least developed countries: UN classification,LDC,1991,206285815475.59866
+Least developed countries: UN classification,LDC,1992,162423362071.82953
+Least developed countries: UN classification,LDC,1993,170645186609.5282
+Least developed countries: UN classification,LDC,1994,167418947426.8737
+Least developed countries: UN classification,LDC,1995,171656467729.8137
+Least developed countries: UN classification,LDC,1996,179828980354.19138
+Least developed countries: UN classification,LDC,1997,188878580088.1532
+Least developed countries: UN classification,LDC,1998,191440642053.54758
+Least developed countries: UN classification,LDC,1999,197386815443.76105
+Least developed countries: UN classification,LDC,2000,224220841301.6215
+Least developed countries: UN classification,LDC,2001,215386170078.76694
+Least developed countries: UN classification,LDC,2002,235506872334.1742
+Least developed countries: UN classification,LDC,2003,266569126690.00363
+Least developed countries: UN classification,LDC,2004,306201111423.0941
+Least developed countries: UN classification,LDC,2005,360878706648.537
+Least developed countries: UN classification,LDC,2006,417524553902.59216
+Least developed countries: UN classification,LDC,2007,501270268255.77356
+Least developed countries: UN classification,LDC,2008,614414977114.4393
+Least developed countries: UN classification,LDC,2009,608426703148.3109
+Least developed countries: UN classification,LDC,2010,687433128591.7863
+Least developed countries: UN classification,LDC,2011,783931296546.4978
+Least developed countries: UN classification,LDC,2012,819266433465.4807
+Least developed countries: UN classification,LDC,2013,892428919239.911
+Least developed countries: UN classification,LDC,2014,959618110350.5364
+Least developed countries: UN classification,LDC,2015,920864864683.3087
+Least developed countries: UN classification,LDC,2016,936561647193.3789
+Least developed countries: UN classification,LDC,2017,1025062467402.3052
+Least developed countries: UN classification,LDC,2018,1090417461003.2311
+Least developed countries: UN classification,LDC,2019,1150500716964.0056
+Least developed countries: UN classification,LDC,2020,1162019936698.8362
+Least developed countries: UN classification,LDC,2021,1266743958589.0854
+Least developed countries: UN classification,LDC,2022,1424615962242.8284
+Least developed countries: UN classification,LDC,2023,1504251159792.0146
+Lebanon,LBN,1988,3313540067.9324584
+Lebanon,LBN,1989,2717998687.710016
+Lebanon,LBN,1990,2838485353.9618664
+Lebanon,LBN,1991,4690415092.536633
+Lebanon,LBN,1992,5843579160.901219
+Lebanon,LBN,1993,7941744492.235954
+Lebanon,LBN,1994,9599127050.175585
+Lebanon,LBN,1995,11718795528.555569
+Lebanon,LBN,1996,13690217333.587883
+Lebanon,LBN,1997,15751867489.444624
+Lebanon,LBN,1998,17247179005.521946
+Lebanon,LBN,1999,17391056369.226524
+Lebanon,LBN,2000,17260364842.454395
+Lebanon,LBN,2001,17649751243.781094
+Lebanon,LBN,2002,19152238805.97015
+Lebanon,LBN,2003,20082918739.63516
+Lebanon,LBN,2004,21159827992.039803
+Lebanon,LBN,2005,21497336498.839138
+Lebanon,LBN,2006,22022709851.40962
+Lebanon,LBN,2007,24827355014.925373
+Lebanon,LBN,2008,29118916105.472637
+Lebanon,LBN,2009,35399582928.68988
+Lebanon,LBN,2010,38443907042.12272
+Lebanon,LBN,2011,39927125961.5257
+Lebanon,LBN,2012,44016799515.75456
+Lebanon,LBN,2013,46880103080.597015
+Lebanon,LBN,2014,48095213746.600334
+Lebanon,LBN,2015,49929337836.81592
+Lebanon,LBN,2016,51147308774.12936
+Lebanon,LBN,2017,53027680685.90382
+Lebanon,LBN,2018,54901519155.55556
+Lebanon,LBN,2019,51605959131.27413
+Lebanon,LBN,2020,31712128253.796097
+Lebanon,LBN,2021,23131941556.784348
+Lebanon,LBN,2022,20992421948.808086
+Lesotho,LSO,1960,34579986.16800553
+Lesotho,LSO,1961,35699985.72000571
+Lesotho,LSO,1962,41859983.256006695
+Lesotho,LSO,1963,47039981.184007525
+Lesotho,LSO,1964,51939979.224008314
+Lesotho,LSO,1965,54879978.048008785
+Lesotho,LSO,1966,56699977.320009075
+Lesotho,LSO,1967,59261976.29520948
+Lesotho,LSO,1968,61445975.421609834
+Lesotho,LSO,1969,65967973.61281056
+Lesotho,LSO,1970,68739972.504011
+Lesotho,LSO,1971,76480285.00441125
+Lesotho,LSO,1972,80913200.42928225
+Lesotho,LSO,1973,121188715.75986478
+Lesotho,LSO,1974,150851316.52727023
+Lesotho,LSO,1975,149558895.91458103
+Lesotho,LSO,1976,147660036.91500923
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+Middle East & North Africa,MEA,2023,4287684126920.0137
+Middle East & North Africa (excluding high income),MNA,1961,16859956000.577162
+Middle East & North Africa (excluding high income),MNA,1962,17721013430.697613
+Middle East & North Africa (excluding high income),MNA,1963,20017082638.059296
+Middle East & North Africa (excluding high income),MNA,1964,22275305018.214695
+Middle East & North Africa (excluding high income),MNA,1965,24847602751.215687
+Middle East & North Africa (excluding high income),MNA,1966,26305840617.03649
+Middle East & North Africa (excluding high income),MNA,1967,28630544838.979855
+Middle East & North Africa (excluding high income),MNA,1968,32496383972.019363
+Middle East & North Africa (excluding high income),MNA,1969,36404628647.12123
+Middle East & North Africa (excluding high income),MNA,1970,40616151582.591156
+Middle East & North Africa (excluding high income),MNA,1971,46629419112.300446
+Middle East & North Africa (excluding high income),MNA,1972,55719939191.23262
+Middle East & North Africa (excluding high income),MNA,1973,74036325634.51567
+Middle East & North Africa (excluding high income),MNA,1974,114764959393.67685
+Middle East & North Africa (excluding high income),MNA,1975,130815997907.46863
+Middle East & North Africa (excluding high income),MNA,1976,162082404645.748
+Middle East & North Africa (excluding high income),MNA,1977,187307614841.1593
+Middle East & North Africa (excluding high income),MNA,1978,199470627871.18823
+Middle East & North Africa (excluding high income),MNA,1979,250478169534.3365
+Middle East & North Africa (excluding high income),MNA,1980,304410546301.89996
+Middle East & North Africa (excluding high income),MNA,1981,292160808820.65656
+Middle East & North Africa (excluding high income),MNA,1982,327203301153.22546
+Middle East & North Africa (excluding high income),MNA,1983,363200737973.84546
+Middle East & North Africa (excluding high income),MNA,1984,378202585948.20166
+Middle East & North Africa (excluding high income),MNA,1985,408729430738.2906
+Middle East & North Africa (excluding high income),MNA,1986,440997031586.9405
+Middle East & North Africa (excluding high income),MNA,1987,380589506756.0965
+Middle East & North Africa (excluding high income),MNA,1988,362853528586.2793
+Middle East & North Africa (excluding high income),MNA,1989,362861531928.54474
+Middle East & North Africa (excluding high income),MNA,1990,504675545300.17865
+Middle East & North Africa (excluding high income),MNA,1991,256748084020.3331
+Middle East & North Africa (excluding high income),MNA,1992,280535150234.9394
+Middle East & North Africa (excluding high income),MNA,1993,290355959192.11444
+Middle East & North Africa (excluding high income),MNA,1994,307433748179.3977
+Middle East & North Africa (excluding high income),MNA,1995,340259302406.65204
+Middle East & North Africa (excluding high income),MNA,1996,380849300431.5171
+Middle East & North Africa (excluding high income),MNA,1997,400496877681.08453
+Middle East & North Africa (excluding high income),MNA,1998,410727251056.2147
+Middle East & North Africa (excluding high income),MNA,1999,449128387154.8823
+Middle East & North Africa (excluding high income),MNA,2000,474549193839.26263
+Middle East & North Africa (excluding high income),MNA,2001,480454322805.49664
+Middle East & North Africa (excluding high income),MNA,2002,463162700952.3677
+Middle East & North Africa (excluding high income),MNA,2003,509460205617.9616
+Middle East & North Africa (excluding high income),MNA,2004,604588881725.0559
+Middle East & North Africa (excluding high income),MNA,2005,707135263713.1755
+Middle East & North Africa (excluding high income),MNA,2006,828322826165.2681
+Middle East & North Africa (excluding high income),MNA,2007,1016252730742.8989
+Middle East & North Africa (excluding high income),MNA,2008,1260101657447.262
+Middle East & North Africa (excluding high income),MNA,2009,1221252441475.6765
+Middle East & North Africa (excluding high income),MNA,2010,1413230709820.2507
+Middle East & North Africa (excluding high income),MNA,2011,1654719982162.4668
+Middle East & North Africa (excluding high income),MNA,2012,1782893176788.2053
+Middle East & North Africa (excluding high income),MNA,2013,1643993969590.373
+Middle East & North Africa (excluding high income),MNA,2014,1625989413800.0613
+Middle East & North Africa (excluding high income),MNA,2015,1460582775148.2905
+Middle East & North Africa (excluding high income),MNA,2016,1496714718329.8464
+Middle East & North Africa (excluding high income),MNA,2017,1496691802249.524
+Middle East & North Africa (excluding high income),MNA,2018,1421482666869.7126
+Middle East & North Africa (excluding high income),MNA,2019,1433133665481.5989
+Middle East & North Africa (excluding high income),MNA,2020,1324364502715.092
+Middle East & North Africa (excluding high income),MNA,2021,1535302141067.382
+Middle East & North Africa (excluding high income),MNA,2022,1760103359907.5435
+Middle East & North Africa (excluding high income),MNA,2023,1646554468701.6702
+Middle East & North Africa (IDA & IBRD countries),TMN,1961,16704026753.445625
+Middle East & North Africa (IDA & IBRD countries),TMN,1962,17557120696.780518
+Middle East & North Africa (IDA & IBRD countries),TMN,1963,19831954715.696064
+Middle East & North Africa (IDA & IBRD countries),TMN,1964,22069291933.66117
+Middle East & North Africa (IDA & IBRD countries),TMN,1965,24617799779.61315
+Middle East & North Africa (IDA & IBRD countries),TMN,1966,26062551137.370216
+Middle East & North Africa (IDA & IBRD countries),TMN,1967,28365755340.030094
+Middle East & North Africa (IDA & IBRD countries),TMN,1968,32195841272.67419
+Middle East & North Africa (IDA & IBRD countries),TMN,1969,36067940559.86558
+Middle East & North Africa (IDA & IBRD countries),TMN,1970,40240513239.44026
+Middle East & North Africa (IDA & IBRD countries),TMN,1971,46198167084.35251
+Middle East & North Africa (IDA & IBRD countries),TMN,1972,55204613518.49619
+Middle East & North Africa (IDA & IBRD countries),TMN,1973,73351600922.53064
+Middle East & North Africa (IDA & IBRD countries),TMN,1974,113703556047.5041
+Middle East & North Africa (IDA & IBRD countries),TMN,1975,129606146584.85696
+Middle East & North Africa (IDA & IBRD countries),TMN,1976,160583386064.15643
+Middle East & North Africa (IDA & IBRD countries),TMN,1977,185575300986.77042
+Middle East & North Africa (IDA & IBRD countries),TMN,1978,197625824431.1469
+Middle East & North Africa (IDA & IBRD countries),TMN,1979,248161623014.4619
+Middle East & North Africa (IDA & IBRD countries),TMN,1980,301595206374.4333
+Middle East & North Africa (IDA & IBRD countries),TMN,1981,289458760549.64166
+Middle East & North Africa (IDA & IBRD countries),TMN,1982,324177162508.14056
+Middle East & North Africa (IDA & IBRD countries),TMN,1983,359841677153.7612
+Middle East & North Africa (IDA & IBRD countries),TMN,1984,374704780586.90137
+Middle East & North Africa (IDA & IBRD countries),TMN,1985,404949297954.23444
+Middle East & North Africa (IDA & IBRD countries),TMN,1986,436918472003.4965
+Middle East & North Africa (IDA & IBRD countries),TMN,1987,377069625965.62396
+Middle East & North Africa (IDA & IBRD countries),TMN,1988,359497678931.06396
+Middle East & North Africa (IDA & IBRD countries),TMN,1989,359505608254.445
+Middle East & North Africa (IDA & IBRD countries),TMN,1990,500008055193.9373
+Middle East & North Africa (IDA & IBRD countries),TMN,1991,254373550217.13773
+Middle East & North Africa (IDA & IBRD countries),TMN,1992,277940621828.78156
+Middle East & North Africa (IDA & IBRD countries),TMN,1993,287670603066.9007
+Middle East & North Africa (IDA & IBRD countries),TMN,1994,304590448179.39764
+Middle East & North Africa (IDA & IBRD countries),TMN,1995,336976502406.652
+Middle East & North Africa (IDA & IBRD countries),TMN,1996,377439700431.51697
+Middle East & North Africa (IDA & IBRD countries),TMN,1997,396737077681.08453
+Middle East & North Africa (IDA & IBRD countries),TMN,1998,406659451056.21466
+Middle East & North Africa (IDA & IBRD countries),TMN,1999,444857187154.88226
+Middle East & North Africa (IDA & IBRD countries),TMN,2000,470235593839.2626
+Middle East & North Africa (IDA & IBRD countries),TMN,2001,476450622805.49646
+Middle East & North Africa (IDA & IBRD countries),TMN,2002,459606900952.3675
+Middle East & North Africa (IDA & IBRD countries),TMN,2003,505492205617.9615
+Middle East & North Africa (IDA & IBRD countries),TMN,2004,599985781725.0558
+Middle East & North Africa (IDA & IBRD countries),TMN,2005,702009563713.1754
+Middle East & North Africa (IDA & IBRD countries),TMN,2006,822974526165.268
+Middle East & North Africa (IDA & IBRD countries),TMN,2007,1010437030742.8987
+Middle East & North Africa (IDA & IBRD countries),TMN,2008,1252791257447.2617
+Middle East & North Africa (IDA & IBRD countries),TMN,2009,1213166741475.6763
+Middle East & North Africa (IDA & IBRD countries),TMN,2010,1403549209820.2505
+Middle East & North Africa (IDA & IBRD countries),TMN,2011,1643533882162.4666
+Middle East & North Africa (IDA & IBRD countries),TMN,2012,1770684776788.2053
+Middle East & North Africa (IDA & IBRD countries),TMN,2013,1630478469590.373
+Middle East & North Africa (IDA & IBRD countries),TMN,2014,1611999713800.0613
+Middle East & North Africa (IDA & IBRD countries),TMN,2015,1446610375148.2905
+Middle East & North Africa (IDA & IBRD countries),TMN,2016,1481309318329.8464
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+Middle East & North Africa (IDA & IBRD countries),TMN,2018,1405206066869.7126
+Middle East & North Africa (IDA & IBRD countries),TMN,2019,1416000165481.5989
+Middle East & North Africa (IDA & IBRD countries),TMN,2020,1308832802715.092
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+Middle East & North Africa (IDA & IBRD countries),TMN,2022,1740947356984.7458
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+"Venezuela, RB",VEN,1960,7663938303.071715
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+"Venezuela, RB",VEN,1964,8192413793.103449
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diff --git a/week2_analysis.ipynb b/week2_analysis.ipynb
new file mode 100644
index 0000000..ec384b2
--- /dev/null
+++ b/week2_analysis.ipynb
@@ -0,0 +1,1247 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d46393d7-f44f-40b2-9c4c-ce116425fe27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "print(\"Hello!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "629a0339-7fd9-454c-9b72-133d5dde76cd",
+   "metadata": {},
+   "source": [
+    "# Анализ данных за 2 неделю\n",
+    "Проверил работоспособность простого кода"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7fad19c2-6a4c-4e71-bd0f-4ba4cfb972a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     80\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  81.750000\n",
+      "std       0.50   7.228416\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  80.000000\n",
+      "75%      20.00  83.000000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создадим DataFrame\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 80]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce529a74-e979-406e-9ea0-fc699f1e520f",
+   "metadata": {},
+   "source": [
+    "Изучил работу с таблицами pandas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "da5bdc2c-ffa8-477f-9f0d-c2101542257d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  82.250000\n",
+      "std       0.50   7.135592\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  81.000000\n",
+      "75%      20.00  84.500000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Имя</th>\n",
+       "      <th>Возраст</th>\n",
+       "      <th>Баллы</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Гриша</td>\n",
+       "      <td>19</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Никита</td>\n",
+       "      <td>20</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Саня</td>\n",
+       "      <td>20</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Кирилл</td>\n",
+       "      <td>20</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Имя  Возраст  Баллы\n",
+       "0   Гриша       19     80\n",
+       "1  Никита       20     75\n",
+       "2    Саня       20     92\n",
+       "3  Кирилл       20     82"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "df  # отображаем датафрейм\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0588df5e-3b45-407a-ad8c-afbba02b09f4",
+   "metadata": {},
+   "source": [
+    "1 пункт"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e00445ca-d5da-4e20-95fa-efc8e52c29b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  82.250000\n",
+      "std       0.50   7.135592\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  81.000000\n",
+      "75%      20.00  84.500000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Обновленные данные:\n",
+      "      Имя  Возраст  Баллы  Обновленные баллы\n",
+      "0   Гриша       19     80              120.0\n",
+      "1  Никита       20     75              112.5\n",
+      "2    Саня       20     92              138.0\n",
+      "3  Кирилл       20     82              123.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "# Добавляем новый столбец с вычисляемыми значениями\n",
+    "df[\"Обновленные баллы\"] = df[\"Баллы\"] * 1.5\n",
+    "print(\"\\nОбновленные данные:\")\n",
+    "print(df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f28c486d-60c4-4515-8ac1-0d1130abbbfe",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "2d74b90e-f4b2-4c5a-a766-e50d66f9e9f4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       21     92\n",
+      "3  Кирилл       22     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "         Возраст      Баллы\n",
+      "count   4.000000   4.000000\n",
+      "mean   20.500000  82.250000\n",
+      "std     1.290994   7.135592\n",
+      "min    19.000000  75.000000\n",
+      "25%    19.750000  78.750000\n",
+      "50%    20.500000  81.000000\n",
+      "75%    21.250000  84.500000\n",
+      "max    22.000000  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Группированные данные:\n",
+      "         Баллы     Имя\n",
+      "Возраст               \n",
+      "19        80.0   Гриша\n",
+      "20        75.0  Никита\n",
+      "21        92.0    Саня\n",
+      "22        82.0  Кирилл\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 21, 22],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "# Применяем .groupby() и .agg()\n",
+    "grouped = df.groupby(\"Возраст\").agg({\n",
+    "    \"Баллы\": \"mean\",  # Среднее значение по столбцу \"Баллы\"\n",
+    "    \"Имя\": \"first\"   # Количество записей для каждой группы по возрасту\n",
+    "})\n",
+    "print(\"\\nГруппированные данные:\")\n",
+    "print(grouped)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "476b6585-5dda-4b54-bbe1-294caec29f09",
+   "metadata": {},
+   "source": [
+    "пункт 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "74d4e049-a72e-4ea3-909e-9b6399ad93bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0   Гриша       19     80\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   Имя      4 non-null      object\n",
+      " 1   Возраст  4 non-null      int64 \n",
+      " 2   Баллы    4 non-null      int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 228.0+ bytes\n",
+      "None\n",
+      "       Возраст      Баллы\n",
+      "count     4.00   4.000000\n",
+      "mean     19.75  82.250000\n",
+      "std       0.50   7.135592\n",
+      "min      19.00  75.000000\n",
+      "25%      19.75  78.750000\n",
+      "50%      20.00  81.000000\n",
+      "75%      20.00  84.500000\n",
+      "max      20.00  92.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Отфильтрованные данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "1  Никита       20     75\n",
+      "2    Саня       20     92\n",
+      "3  Кирилл       20     82\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создали датафрейм\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "print(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "# Фильтруем записи по условиям\n",
+    "filtered_df = df[df[\"Возраст\"] > 19]\n",
+    "print(\"\\nОтфильтрованные данные:\")\n",
+    "print(filtered_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49536236-2050-4dfb-8d33-21c7dd68a557",
+   "metadata": {},
+   "source": [
+    "пункт 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "065fac3c-3b23-4fc2-94e6-122344c723fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Сумма элементов массива: 77\n",
+      "Среднее значение: 15.4\n",
+      "Медиана: 5.0\n",
+      "Стандартное отклонение: 18.789358690492875\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([5, 14, 52, 1, 5])\n",
+    "print(\"Сумма элементов массива:\", np.sum(arr))\n",
+    "print(\"Среднее значение:\", np.mean(arr))\n",
+    "print(\"Медиана:\", np.median(arr))\n",
+    "print(\"Стандартное отклонение:\", np.std(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68b77c5e-4dac-49f6-92fa-feef4d75b8c2",
+   "metadata": {},
+   "source": [
+    "изучил массивы и вычисления numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "02dc6842-fc0b-4944-93b5-a75f09a904f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Двумерный массив:\n",
+      "[[ 5  6]\n",
+      " [ 1 17]]\n",
+      "\n",
+      "Сумма всех элементов массива: 29\n",
+      "Среднее значение элементов массива: 7.25\n",
+      "Медиана элементов массива: 5.5\n",
+      "Стандартное отклонение элементов массива: 5.931905258852336\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Создаем двумерный массив\n",
+    "arr_2d = np.array([[5, 6], [1, 17]])\n",
+    "\n",
+    "print(\"Двумерный массив:\")\n",
+    "print(arr_2d)\n",
+    "\n",
+    "# Применяем операции\n",
+    "print(\"\\nСумма всех элементов массива:\", np.sum(arr_2d))\n",
+    "print(\"Среднее значение элементов массива:\", np.mean(arr_2d))\n",
+    "print(\"Медиана элементов массива:\", np.median(arr_2d))\n",
+    "print(\"Стандартное отклонение элементов массива:\", np.std(arr_2d))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34017aa9-5e74-4e97-b91a-a7707a10e6e9",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3523c6d3-6b5e-4b09-8d32-7eefd373f6b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Массив с np.linspace():\n",
+      "[ 0.   2.5  5.   7.5 10. ]\n",
+      "\n",
+      "Массив с np.random.randn():\n",
+      "[ 0.17990535  1.18232429 -1.35297202]\n",
+      "\n",
+      "Результат умножения матриц с np.dot():\n",
+      "[[19 22]\n",
+      " [43 50]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Использование np.linspace() для создания массива с 5 элементами от 0 до 10\n",
+    "arr_linspace = np.linspace(0, 10, 5)\n",
+    "print(\"Массив с np.linspace():\")\n",
+    "print(arr_linspace)\n",
+    "\n",
+    "# Использование np.random.randn() для создания массива с 3 случайными значениями из стандартного нормального распределения\n",
+    "arr_random = np.random.randn(3)\n",
+    "print(\"\\nМассив с np.random.randn():\")\n",
+    "print(arr_random)\n",
+    "\n",
+    "# Пример использования np.dot() для умножения матриц\n",
+    "matrix_a = np.array([[1, 2], [3, 4]])\n",
+    "matrix_b = np.array([[5, 6], [7, 8]])\n",
+    "dot_product = np.dot(matrix_a, matrix_b)\n",
+    "print(\"\\nРезультат умножения матриц с np.dot():\")\n",
+    "print(dot_product)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd97160-b97f-4c60-8bce-3f5b47c875e6",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "024362d0-4fc9-409a-b766-8c36f99b0f6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y, label='cos(x)')\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"График косинуса\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d795adf-9e77-46d4-9411-28bfe91597e1",
+   "metadata": {},
+   "source": [
+    "изучил построение графиков matplotlib\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "1ce3eff7-b795-4b61-9b7a-e7eb61fdd275",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y, color='purple', label='cos(x)')\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"График косинуса (фиол.)\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d5ee54-e40a-424c-9fa2-bd169e3d9e33",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "22c29c7e-4661-4b0f-b07b-44f743e358d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y_sin = np.sin(x)\n",
+    "y_cos = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y_sin, label='sin(x)', color='blue')\n",
+    "plt.plot(x, y_cos, label='cos(x)', color='green')\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"Графики синуса и косинуса\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efec143d-ed6d-472b-aa57-1e7608de0c1a",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3a0537d6-328a-441c-a9e2-e34e9e879298",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Данные для bar, scatter и hist\n",
+    "x = np.arange(1, 6)\n",
+    "y = np.array([5, 7, 3, 8, 4])\n",
+    "\n",
+    "# bar chart\n",
+    "plt.subplot(131)\n",
+    "plt.bar(x, y, color='purple', label='bar chart')\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Гистограмма')\n",
+    "plt.legend()\n",
+    "\n",
+    "# scatter plot\n",
+    "plt.subplot(132)\n",
+    "plt.scatter(x, y, color='orange', label='scatter plot')\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Точечный график')\n",
+    "plt.legend()\n",
+    "\n",
+    "# histogram\n",
+    "plt.subplot(133)\n",
+    "plt.hist(y, bins=5, color='cyan', label='histogram')\n",
+    "plt.xlabel('Значения')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.title('Гистограмма распределений')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8824583b-b920-423a-9cc7-7e64a9a264bf",
+   "metadata": {},
+   "source": [
+    "пункт 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "a11be903-b223-4890-a239-6c83baec9367",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# берем данные из предыдущего датафрейма\n",
+    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"] \n",
+    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5426eda2-7323-4349-9358-908b08452623",
+   "metadata": {},
+   "source": [
+    "изучил работу с графиками данных seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "56feb925-9279-4e76-9d5a-6928caf2eb90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sBag0OHnxosjkySLVq1sj+n39tUhcnNNbCgBpQ7vv+ZsuXrwoFbQ/NAAAQCaXqsXPV65cKdmILsBDTIzI0qVWHSk7uFC8uEiTJiKVKlFTCBYdsU+78fXubdWiGjtWZP58kUWLrKliRSuA2b27tSwAZBZ58+b1W7fTLoZ+7do1R7YLAAAgQwem7tc0F5/G05EjR2TdunWmWCegXfZ+/llk/XoRu01drpxI48YiZcoQkIJ/elxo0FInHcVvwgQre2rXLpF//lNEv16eesrq5lekiNNbCwCp45NPPpH8+fM7vRkAAACBE5jKkyeP17wObVy5cmVTpLNVq1aptW0IQNoNS7vqaVFrHXFPlS2rIzmKlCzp9NYhkGgA8803rRH9dDQ/7Qb6++8io0aJjBljFUl//nkrmwoAAlmjRo2ksPZvBwAACELXFZj66KOPUn9LENC0ZpQWNV+58u+i5iVKiNx5pxWYAq5XzpxWltQTT4h8+aXIG29Yx9r771vZVJrA+cILIrfe6vSWAsD12bZtm5w6dUpy5MghRYsWlaxZszq9SQAAAIFRY2r9+vWyfft287eOHHOzVjJGUNG6UTqy2k8/iZw/b92mXaw0IKWZLHTZQ2oJDRW5916RDh2sOlSjR1uF0T/91Jo0K2/oUJGmTZ3eUgBImebNm7trSmkWepUqVaRnz57y3HPPOb1pAAAAGTMwdfz4cTOM8ZIlS0zRThUdHS3NmjWT2bNnS6FChVJ7O5EB7dkj8v33ejxY81oeQ4MDOro1ASmkRx2qrVtF3npLZMYMKziq0+23WwEqDY5yHALI6Pbu3WuCUleuXDGj8R0+fFjWrFljanZevXpVBgwY4PQmAgAApKnQ63nQM888I2fPnpXffvtN/vrrLzNt3brVNKj+qRWKkamdOCEyc6bIxx9bQSkdiLF1a6u7VfXqBAOQfvR4mzrVCpLq8ae9X5YtE2nRwiq0v3ChDs7g9FYCQMJKly4tZcqUkYoVK0qdOnWkffv2MnLkSJk4caK8r32WAQAAMrnrCkwtWLBA/vOf/8hNN93kvq1q1aoyYcIE+e6775K9nmXLlpkGWLFixUz6+nwdHz4JmqV1yy23SEREhFSoUEGm6q9SH7od2sjLli2b1K9f31x5xI27cEHk229FJk60RknTrlX161ujpd12m0hYmNNbiGBVqpQ1gt8ff1jHowZLV6wQadPGOja/+YYAFYDAopnpc+bMcXozAAAAMmZgKi4uTrJkyRLvdr1N70uu8+fPS61atUwgKbnp7u3atTNdBjdt2iT9+vWTXr16yUJNi/h/2ojr37+/DBs2TDZs2GDW37p1a9P9ENdH39J160Tee09k7VrrB37lylaGiv7wj4x0egsBS/Hi1uh9GqDq3986NjUufffdInXrinzxBQEqAIFB21R6IQ4AACCzu67A1J133inPPvusqYNg+/PPP02RTi3gmVxt27aVV155Re67775kLT9p0iQpW7asvP322yZbq2/fvvLggw/KO++8415mzJgx0rt3b+nRo4fJ4tLHZM+eXaZMmZLCVwn1558iH35oZZxcumQVNu/aVa/kihQo4PTWAf5FRYm8/bbIvn0iAweK5MghsmGDVTy9Xj26+AHIOK5duyZvvfWW1KtXz4zIlz9/fq8JAAAgs7uuwNT48eNNPSntLle+fHkzacBIb3tP02rSyMqVK6WFFo/xoNlQeru6fPmyGSnQcxkd3Ubn7WWQPBcvWiOeffCBiMYfIyKs7Kg+fUTKlnV664DkKVxY5I03rADVoEFWgEqz//RYvuMOkZ9/dnoLAQS74cOHm4tqnTp1kpiYGJP1ff/995v2y8svv+z05gEAAGTMUflKlixpusktWrRIduzYYW7TDCbfoFFqO3r0qBTRlB0POq8BsYsXL8rp06fNlUd/y9jb6U9sbKyZbLq+YKVZJBs3iixaZAWnVM2aVjHpXLmc3jpcr+3bt0uwe/BBzfYMl6lTi8i8eYXk559DzQh+DRrEyNNPH5abbvr/Ax43RL9LtQYgkNYKFiwopbTAXICbMWOGTJ482ZQq0EBUly5dzAW/mjVryqpVqxhUBgAAZHopCkz9+OOPpvucNpRy584tLVu2NJPSq3zVqlUzXeea6DjuAWTUqFHmimWw09H2vvpK5OBBa75QIZF27XTEIKe3DNfr3LkjIhIijzzyiNObksEUF5HBIvKYrFyZx0win4rIUBHZ5vTGBTgdlpN+kkh7kZHZZceO7QEfnNKLbjVq1DB/58yZ07Sn1N133y1DhgxxeOsAAAAyWGBq7Nixpn6TBqV85cmTRx5//HGTjp5WgSmtvXDs2DGv23RetycyMlLCwsLM5G8ZfWxCBg0aZFLnPTOmNCssWFy7JrJ8udWtSf/WuvZNm1oj7jHSXmC7dCnaBAmaNRsvFSs2cHpzMpwzZ3bKunVRsnu31nF5QETulwoV/pK6dY9I7tyXnd68gLNr17fy009DON6Q5k6c2C6ff/6InDx5MuADUyVKlJAjR46Y16GZUt9//70per527VqyDwEAQFBIUWBq8+bN8oYWbElAq1atTAHPtNKgQQP59ttvvW774YcfzO0qa9asUqdOHVm8eLHcq1WO/38EQZ3XTK+EaMMvWBt/hw5ZWVL2oIUVK1pZUnk0gQSZRr58FSQqitGd/BVJ1xEm9fhfskS7PIbI7t0F5I8/Csitt4rp6pc9u9NbGThOnrS6jHK8AcmnA8BoO6V+/fryzDPPmAzXDz/8UA4cOGAGlQEAAMjsUhSY0swjHb44wZWFh8sJ7Q+WTOfOnZPdu3e75/fu3SubNm0yo9DolUPNZNLR/qZPn27uf+KJJ0zh9YEDB0rPnj1N18K5c+fKNzpk3P/TzKdu3bpJ3bp1zQg3muV1/vx5M0of/nb5snbNFFm92prXH99aELp6dZEQ7YkDBFmR9I4drUL/+rnYs8f6bGzaJNKokchtt1mZhACQ2l5//XX331oAvXTp0rJixQqpWLGitG/f3tFtAwAAyHCBqeLFi8vWrVulQoUKfu/fsmWLRGkKQjKtW7dOmjVr5p63u9NpYGnq1KkmtV2vGNp05D8NQukVxHHjxpn09w8++MCMzOfZqNPg2NChQ03dhtq1a8uCBQviFUQPZvqjW7Ok/r+MhdSqpdluZIYAxYqJaDku/YzoAABHj1qBqrVrRfSrSj8rodc1likAJE2773311VdmIBe9wAYAABAMUhSYuuuuu0whzjZt2ki2bNm87tNR8YYNG2aKdSZX06ZNxaXDwCVAg1P+HrNRh41LhHbbS6zrXrDSgQd/+EFk/XprXrvr6duVQJwRCFrly4uUKyfy669WYEqDuF9+KbJypTVCpXZ5JbMQwI3QzHBtzxQoUMBdmkBH5tPSAqGhoaY0wqxZs+Shhx5yelMBAAAyTmBq8ODB8tlnn0mlSpVM4KeyFmcRkR07dsiECRPMFb5///vfabWtuAH79ol88YVItNbCFpF69USaN9e6XE5vGZAxaeCpZk2RqlWtjKlly6yRK2fNskaq1AFJi+vgfgBwHQ4dOmTaTbY333xTunTpIlOmTJGQkBBTb2r06NEEpgAAQKaXosCUdofTugdPPvmkqf9kZztpA0q702lwii5zGcuVKyKLF/9dS0qzpDp00G6RTm8ZEBjCw3XgBZHata3RK/WztH+/yAcfiFSrJnLnnSL5dVA/ALgB27dvl9dee82MLqw0MHWbFrgDAADI5FIUmFJalFNHxjt9+rQpXK7BKS3QmS9fvrTZQtzQiHvz54ucOmXN33KLVUsqSAcgBG5IZKSVJaXZhj/9pKOUivz2m/6YFDOC3x13WMsAwPWIjo42g7/YtF119uxZR7cJAAAgQwamPBtMt+qvMWQ42jNg6VIru0OT2nLmFLnnHqsuDoAbo1mH995rZVFpgXQdWFSzqLZssYJTWq/4/xMeACBBmm2uk+9tAAAAwYbxpTIZzY6aMkXk55+toFSNGiJPPUVQCkht2mv54YetUfwKF9YBIEQWLBCZOFFk507r8wcACdGM8+7du8v9999vpkuXLskTTzzhnu/Zs2ey16W1qnRwGh29ODIyUsqXLy8jR450l1y4cuWKvPDCC1KjRg3JkSOHFCtWTLp27SqHDx9OdL0vv/yyO4BmT1WqVLnh1w4AAJAqGVPIWLTtuWmTyHffWXWldNBEHXFPa+AASNsR/B5/XGTDBquLnwaHZ8+26ri1bm0FsADAV7du3bzmH9Eotw8NHiXHG2+8IRMnTpRp06ZJtWrVZN26ddKjRw/JkyeP/POf/5QLFy7Ihg0bTPCqVq1aphzDs88+K/fcc49ZNjG6vkWaHvr/wrXwHgAAQCqidZEJaKbG11+LbNtmzZcpY3U10i5HANJeaKjVhU8zFDVbcdUqkb17Rf77X6touhZI1y61AGD76KOPUm1dOjBNhw4dpF27dma+TJkyMmvWLFmzZo2Z1wDVDz/84PWY8ePHS7169eTAgQNSqlSpBNetgaiiRYum2rYCAAD4oitfgNMfv9p1SINS+uO4eXORRx8lKAU4QQcWaNFC5OmnrWxFzWTcuFHkvfesgJVmMwJAamvYsKEsXrxYfv/9dzO/efNmWb58ubRt2zbBx8TExJiueXnz5k103bt27TJd/8qVKycPP/ywCWQBAACkJjKmArjAuXYb+uUXa75AAZH77xcpVszpLQOgg5Q++KA1gt/ChSJaxuXHH0XWr7cCVxq0osYxgNTy4osvypkzZ0z9p7CwMFNz6tVXXzWBJH+0npXWnOrSpYvkzp07wfXWr19fpk6dKpUrV5YjR47I8OHDpUmTJrJ161bJlStXvOVjY2PNZNNtAgAASAqBqQAUEyPy6aciBw9a87fcYtWyyZrV6S0D4El7x/TqJfLrryKLF//92dVR/PQzW6KE01sIIDOYO3euzJgxQ2bOnGlqQm3atEn69etnMp18a1lpIfSOHTuawuhalyoxnhlXNWvWNIGq0qVLm+d77LHH4i0/atQoE7wCAABICQJTAUaz9OfPt+pKabehe+4RqVrV6a0CkBDNjKpZU+Smm7QOjJXleOiQyIcfWjWptPstXW8B3IgBAwaYrKnOnTubeR19b//+/SZQ5BmYsoNSet+PP/6YaLaUP9rtr1KlSrJ7926/9w8aNEj69+/vlTFVsmTJ635dAAAgOBCYCqCue5pxsXKlNR8VZXUVyp/f6S0DkBxZsojccYeV4ajd+nQUTc2k2r5d68OINGpE1iOA66Oj7oVqoUkP2qUvLi4uXlBKa0b99NNPUkBrAKTQuXPnZM+ePfKoFrP0IyIiwkwAAAApQWAqAERHW91/NMtCad2ali11pByntwxASmlZlg4d/q4/tX+/yLJlVpF0zZ7S7CrqTwFIifbt25uaUjq6nnbl27hxo4wZM0Z69uzpDko9+OCDsmHDBvn6669NDaqjR4+a+/Lnzy9Z/z8q3rx5c7nvvvukb9++Zv75558369bue4cPH5Zhw4aZgJfWpgIAAEgthDYyuJ07ra57ly5ZXff0B612CQIQ2DTrUXvYaMaUjuKuAWj9rOvo7m3aiND7BUByvffeezJkyBB56qmn5Pjx46a21OOPPy5Dhw419//555/y5Zdfmr9r167t9VjNnmratKn5W7OhTp486b7v0KFDJgh16tQpKVSokDRu3FhWrVpl/gYAAEgtBKYycNe9RYtEVq2y5nW0Pe26p6N9AcgcNDNKa8RVqmR91n/+2RrBb8oUkerVrRH8qD8FICk6Qt7YsWPN5E+ZMmVMsfOk7Nu3z2t+9uzZqbaNAAAACSEwlQHpyF3z5ukVTmu+fn2r615YmNNbBiAtaLfcxo01k8GqP6Xd+rZuFdmxQ6RBA+s+6k8BAAAAyIwITGUwf/xh1ZO6cEEkWzar616VKk5vFYD0kDOnNdKm1p9asMCqP6VZVFoonfpTAAAAADIjAlMZhGbY6zDymi2hfxctKtKxI133gGCkn3+tP6UZU99/711/qnVrkVKlnN5CAAAAAIHs8tU4OR97VbKGh0iOiCyObguBqQwgNlbkiy+sIshKu/PcdZc1vDyA4KSZUTrQQcWKIqtXWyP3af2pjz4SqVbNqj+VN6/TWwkAAAAgkFy5FieHT1+Q2Sv3y+YD0VI4d4R0bVJOKhTJKbkjnakfQmDKYSdOiMydK6KD4ISGirRtK1KnDt11APxdf6pRI5FatXT0LJENG0R++83KpmrYkPpTAAAAAJLv9yNn5MmP1pqMKfXH8XOyavcpeaJ5RelYv5Rkj0j/MFFouj8j3LZtE/ngAysolSuXSI8eInXrEpQC4L/+VPv2Io8/riNsWSN3av2p996zalAlY8AtAAAAAEHsr3Ox8toXv7mDUp7e/3GX/HX+siPbRcaUI8Jk1arismWLNac/Mh94wPrhCQBJ1Z/q2lVk506r/tTp01ZXYLv+VOnSTm8hAAAAgIzo7MUrsuf4Ob/3xblEdhyOkRL5s6f7dhGYSmd//aW7/HvZsqWImdeh4LVWjHbjA4Dk0KxKHa2zQgUrIKX1p44cEZk6VaRqVZGWLak/BQAAAMBHEt2zQhzqvkVgKh3FxYk88URFEakp4eHX5N57w0wRYwC43vpTWmdK60/piJ4bN1pdhDWbSoPeWn8qIsLprQQAAACQEeSOzCIViuSS3cfOxrsvNESkclRuR7aLPJ10pFlRffseFpHtct99OwlKAUgVOXJY9af69Pm7/tTy5SLjx1vBKupPAQAAAMiXI6v8u0M1icgSPxTUt1UlyZ+DUfmCwu23x4hIfcmXb7XTmwIgCOpPffml1d2vTRvqTwEAAADBrmLRXPLxkw3ls7UHZeP+01IkdzZ5pFEZKVMopyMj8ikCU4646vQGAAii+lNHj/5df0pr2uXL5/RWAgAAAHBCeFiolCyQQ55qWUkuxF6ViPBQyZbV2dAQgSkAyIQ860/99JPIhg1/15+67TaRJk2oPwUAAAAEqyxhoZInuzNd93xRYwoAMnn9qbvvFnn8cZGyZa36U7/8IvLee1awSgdlAAAAAACnEJgCgCBQpIjIo4+KdO4skj+/yPnzIl99JTJ5ssi+fU5vHQAAAIBgRVc+AAii+lOVK/9df2rpUqv+1LRpIjfdJNKyJfWnAAAAAKQvAlMAEGTCwkQaNBCpWVNkyRKR9etFtm8X+f136k8BAAAASF905QOAIK4/1a6dyBNPiJQrR/0pAAAAAOmPwBQABLnChUUeeUSkSxeRAgX+rj/1/vvUnwIAAACQtujKBwAw9acqVRIpX15k7Vqr/tSxY1b9qSpVrPpTWjQdAAAAAFITgSkAgFf9Ka0zZdefWrdOZMcOkV27ROrVE7n9dpFs2ZzeSgAAAACZBV35AADxZM8uctddVv0pzaLS+lMrV4qMGyeyYoXI1atObyEAAACAzIDAFAAg0fpTDz8s8o9/iBQqJHLpksgPP4iMHy+yZYuIy+X0FgIAAAAIZHTlAwAkWX+qYkUrc2rzZpGffhKJiRH5/HMri6pFC+s+AAAAAEgpAlMAgGQJDRW5+WaR6tVFVq0S+eUXkaNHRT7+WKRcOZEyZfI5vYkAAAAAAgyBKQBAimTJItKkiUidOiLLllmj+P3xh053ich0uXCB6ugAAAAAkofAFADgugukt2kjUr++yI8/imzdqrc+Kt9/HyfR0dYIfpGRTm8lAAAAgIyM4ucAgBuSL5/IAw+INGv2nYgslri4UNPVT0fw04yqy5ed3kIAAAAAGRWBKQBAqsiX7y8RaSENG66XIkVEYmOtQukaoNJA1dWrTm8hAAAAgIyGrnwAgFRVtOhJM1Kfdu1bskTkr79EFi60RvBr2lSkVi2rkDoAAAAAEJgCAKS6kBCRGjVEqlYV2bRJZOlSkTNnRL780hrNr1kz6z5dDgAAAEDwIjAFAEgzYWHW6H01a4qsWyfy888ip06JfPKJZlaJ3HmnSIUKBKgAAACAYEVgCgCQ5rJkEWnQQOSWW6x6UytWiBw9KjJzpkipUlaAqnRpp7cSAAAAQHojMAUASDcRESJ33CFy660iy5eLrF0rcuCAyNSpIuXKWTWoSpZ0eisBAAAApJcMUX52woQJUqZMGcmWLZvUr19f1qxZk+CyTZs2lZCQkHhTu3bt3Mt079493v1t2rRJp1cDAEhK9uwirVqJPPOM1dVPi6H/8YfIlCkiH38scvCg01sIAAAAICgypubMmSP9+/eXSZMmmaDU2LFjpXXr1rJz504pXLhwvOU/++wzuXz5snv+1KlTUqtWLXnooYe8ltNA1EcffeSej9DL9ACADCV3bpG77xZp1MiqP7V5s8iePdZUvryVQVWihNNbCQAAACDTBqbGjBkjvXv3lh49eph5DVB98803MmXKFHnxxRfjLZ8/f36v+dmzZ0v27NnjBaY0EFVUK+sCADK8fPlE7rlHpEkTK0ClI/nZASotjq7d/whQAQAAAJmPo135NPNp/fr10qJFi783KDTUzK9cuTJZ6/jwww+lc+fOkiNHDq/blyxZYjKuKleuLE8++aTJrAIABEaASrv41a5tjda3e7d+14vMmCHy559ObyEAAACATJMxdfLkSbl27ZoUKVLE63ad37FjR5KP11pUW7duNcEp3258999/v5QtW1b27NkjL730krRt29YEu8J07HIfsbGxZrKdOXPmhl4XAODGA1QdOojcfrvIsmVWFz8NUOlUsaKVQVW8uNNbCQAAACDgu/LdCA1I1ahRQ+rVq+d1u2ZQ2fT+mjVrSvny5U0WVfPmzeOtZ9SoUTJ8+PB02WYAQMoDVHYXPw1Q7dplTVqDqnFjkdKlrcwqAAAAAIHH0a58BQsWNBlMx44d87pd55OqD3X+/HlTX+qxxx5L8nnKlStnnmu3Xmr3Y9CgQRITE+OeDjIcFABkKFpeUANUffuK1KplBaK0/tS0aSI6zoUGqlwup7cSAAAAQEAFprJmzSp16tSRxYsXu2+Li4sz8w0aNEj0sfPmzTPd7x555JEkn+fQoUOmxlRUVJTf+7VQeu7cub0mAEDGDFDde69Vg6puXRHtna3XEmbOFPnvf0V++03PI05vJQAAAICACEyp/v37y+TJk2XatGmyfft2U6hcs6HsUfq6du1qMpr8deO79957pUCBAl63nzt3TgYMGCCrVq2Sffv2mSBXhw4dpEKFCtK6det0e10AgLTt4teuncizz4o0bKgXOjTbVuSTT0QmTBDZuFHk2jWntxIAAABAhq8x1alTJzlx4oQMHTpUjh49KrVr15YFCxa4C6IfOHDAjNTnaefOnbJ8+XL5/vvv461PuwZu2bLFBLqio6OlWLFi0qpVKxk5cqTJjAIAZB65com0bGnVmlq92pr++kvkyy91dFYraHXLLSJZsji9pQAAAAAyZGBK9e3b10z+aMFyX5UrVxZXAsVEIiMjZeHCham+jQCAjCsyUqRpUxHtBb5+vcjKlTrCqsiCBdaofrfdZnX90+UAAAAAZBwZIjAFAEBq0MRYzZLSwVo3bRL55ReR6GiRH3+0RvW7+WYrSKVdAQEAAAA4j8AUACDTCQ+3MqS0G9/WrSIrVlg1qNasEVm7VqRqVSu7qnhxp7cUAAAACG4EpgAAmZaWKKxZU6RGDZE//rC6+O3ZY43ep1Pp0laAqlIlkZAQp7cWAAAACD4EpgAAmZ4GncqXtybNnNIA1a+/iuzfb006wKsGqGrVsrKtAAAAAKQPmt8AgKCig77ee6/InXdao/hpsfRTp0S+/lrkp59Ebr3VmrJnd3pLAQAAgMyPwBQAICjlzi3SsqXI7beLbNggsmqVNZKfDga7fLnV/a9+fSuQBQAAACBtEJgCAEiwj+Sn3fh0JL9t26xufkeOiGzcaE1ah0oDVJUrWzWrAAAAAKQeAlMAAIhIWJiVJVW9usjBg9YIfhqosutQ5cljdfHTkf4iI53eWgAAACBzIDAFAIBPofRSpaxJu/atXWvVoYqJEVm0yOrqp0XSNcOqcGG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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# создаем данные для датафрейма\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82],\n",
+    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\"]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Построим histplot\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(df['Баллы'], kde=True, color='blue')\n",
+    "plt.title(\"Распределение Баллов\")\n",
+    "\n",
+    "# Построим scatterplot\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.scatterplot(x=\"Возраст\", y=\"Баллы\", data=df, hue=\"Категория\", palette=\"Set1\")\n",
+    "plt.title(\"Диаграмма рассеяния: Возраст vs Баллы\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e3db813-c976-4244-a25e-200999074720",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "56802999-1661-4d2d-af83-7ea5ea70ec13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 588.236x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# создаем данные для датафрейма\n",
+    "data = {\n",
+    "   \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82],\n",
+    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\"]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Используем pairplot для визуализации взаимосвязей между числовыми признаками\n",
+    "sns.pairplot(df, hue=\"Категория\", palette=\"Set1\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Используем heatmap для визуализации корреляции между числовыми признаками (выбираем только числовые столбцы)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(df[[\"Возраст\", \"Баллы\"]].corr(), annot=True, cmap='coolwarm', linewidths=0.5)\n",
+    "plt.title(\"Тепловая карта корреляции\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf8b848-9fa8-4d50-8c3e-7d889d37c62f",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "6b83e497-8686-4e10-814c-7805776d4372",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████| 100/100 [00:10<00:00,  9.91it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "for i in tqdm(range(100)):\n",
+    "    time.sleep(0.1)  # Симуляция долгого процесса"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee5065d-de15-4cfb-9162-e9dc80cd483b",
+   "metadata": {},
+   "source": [
+    "изучил progress-bar tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ca7ffb56-198d-4b82-b7c0-25028b163d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных:  25%|████            | 1/4 [00:00<00:01,  2.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Гриша\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных:  50%|████████        | 2/4 [00:01<00:01,  2.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Никита\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных:  75%|████████████    | 3/4 [00:01<00:00,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Саня\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных: 100%|████████████████| 4/4 [00:02<00:00,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обработаны данные: Кирилл\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# создаем данные для датафрейма\n",
+    "data = {\n",
+    "    \"Имя\": [\"Гриша\", \"Никита\", \"Саня\", \"Кирилл\"],\n",
+    "    \"Возраст\": [19, 20, 20, 20],\n",
+    "    \"Баллы\": [80, 75, 92, 82]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Используем tqdm для итерации по строкам датафрейма\n",
+    "\n",
+    "\n",
+    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Обработка данных\"):\n",
+    "    time.sleep(0.5)  # Симуляция долгой обработки данных\n",
+    "    # Дополнительная обработка строки \n",
+    "    print(f\"Обработаны данные: {row['Имя']}\")\n",
+    "    \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eb364bf-e4d8-4382-b715-ecdcac12714f",
+   "metadata": {},
+   "source": [
+    "пункт 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3d3b5adf-33ae-4eb1-ba68-94b4ab2f3203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Загрузка данных: 100%|█████████████████████████████████████████████████████████████████████| 100/100\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# Используем tqdm с кастомными стилями\n",
+    "for i in tqdm(range(100), desc=\"Загрузка данных\", ncols=100, bar_format=\"{l_bar}{bar}| {n_fmt}/{total_fmt}\"):\n",
+    "    time.sleep(0.01)  # Симуляция долгого процесса\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50cab1a4-c5da-4fd7-b580-dbd7fb80fbfb",
+   "metadata": {},
+   "source": [
+    "пункт 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "44b7d39f-fdc4-4c3d-891d-387f2a4060cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 13979 entries, 0 to 13978\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   Country Name  13979 non-null  object \n",
+      " 1   Country Code  13979 non-null  object \n",
+      " 2   Year          13979 non-null  int64  \n",
+      " 3   Value         13979 non-null  float64\n",
+      "dtypes: float64(1), int64(1), object(2)\n",
+      "memory usage: 437.0+ KB\n",
+      "None\n",
+      "               Year         Value\n",
+      "count  13979.000000  1.397900e+04\n",
+      "mean    1994.672866  1.207380e+12\n",
+      "std       17.731413  5.537517e+12\n",
+      "min     1960.000000  1.150263e+04\n",
+      "25%     1980.000000  2.233880e+09\n",
+      "50%     1996.000000  1.672591e+10\n",
+      "75%     2010.000000  2.058542e+11\n",
+      "max     2023.000000  1.054350e+14\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Загрузка данных: 100%|█████████████████████████████████████████████████████████████████████| 100/100\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# Загрузка данных\n",
+    "file_path = \"D:/Files/2 week/data.csv\"  # Укажите путь к вашему файлу\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "# Базовый анализ данных\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "\n",
+    "# Применяем tqdm для обработки\n",
+    "for i in tqdm(range(100), desc=\"Загрузка данных\", ncols=100, bar_format=\"{l_bar}{bar}| {n_fmt}/{total_fmt}\"):\n",
+    "    time.sleep(0.01)  # Симуляция долгого процесса\n",
+    "\n",
+    "# Увеличиваем размер графиков\n",
+    "plt.figure(figsize=(18, 6))\n",
+    "\n",
+    "# 1. Гистограмма распределения ВВП\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.histplot(df['Value'], bins=50, kde=True)\n",
+    "plt.xscale('log')  # Логарифмическая шкала для лучшей читаемости\n",
+    "plt.title(\"Распределение ВВП\")\n",
+    "plt.xlabel(\"Value (log scale)\")\n",
+    "\n",
+    "# 2. Точечный график (scatterplot) зависимости ВВП от года\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.scatterplot(x=df[\"Year\"], y=df[\"Value\"], hue=df[\"Country Name\"], alpha=0.5, s=10, legend=False)\n",
+    "plt.title(\"ВВП по годам\")\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Value\")\n",
+    "\n",
+    "# 3. Boxplot (ящик с усами) для распределения ВВП по странам\n",
+    "plt.subplot(1, 3, 3)\n",
+    "top_countries = df.groupby(\"Country Name\")[\"Value\"].median().nlargest(20).index  # ТОП-20 стран по медианному ВВП\n",
+    "filtered_df = df[df[\"Country Name\"].isin(top_countries)]\n",
+    "sns.boxplot(x=\"Country Name\", y=\"Value\", data=filtered_df)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title(\"Распределение ВВП (ТОП-20 стран)\")\n",
+    "plt.xlabel(\"Country Name\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9659690a-77b9-49f2-b471-835fb6fb9765",
+   "metadata": {},
+   "source": [
+    "Выполнил самостоятельное задание используя таблицу данных по ВВП стран (источник: https://datahub.io/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}