直方图：X 轴和 Y 轴值显示不正确
[英]histogram : X-axis & Y-axis values not displaying correctly
问题描述
我想绘制直方图，但值显示不正确。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def hist():
df=pd.read_csv('C:\\Users\Bhuwan Bhatt\Desktop\IP PROJECT\Book1.csv',encoding='ISO-8859-1')
df=df.sort_values('TotalMedal',ascending=False)
df1=df.head(n=10)
df1=df1.loc[:,'Country']
print(df1)
bins=np.arange(len(df1))
plt.hist(df1,bins,color='lime',edgecolor='k')
plt.xticks(rotation=20)
plt.title('HISTOGRAM COUNTRY V/S MEDALS',color='hotpink')
plt.xlabel('Country~~~~>',color='navy')
plt.ylabel('No. of Medals~~~~>',color='navy')
plt.show()
hist()
输出出现：
y 轴上的值是这样的 0.00-0.25-0.50 到 2.00
我希望 y 轴值符合“TotalMedal”，例如500-1000-1500-2000 ....
使用的 CSV：
Country SummerTimesPart Sumgold Sumsilver Sumbronze SummerTotal WinterTimesPart Wingold Winsilver Winbronze WinterTotal TotalTimesPart Tbronzemedal Tsilvermedal Tgoldmedal TotalMedal
Afghanistan 14 0 0 2 2 0 0 0 0 0 14 2 0 0 2
Algeria 13 5 4 8 17 3 0 0 0 0 16 8 4 5 17
Argentina 24 21 25 28 74 19 0 0 0 0 43 28 25 21 74
Armenia 6 2 6 6 14 7 0 0 0 0 13 6 6 2 14
Australasia 2 3 4 5 12 0 0 0 0 0 2 5 4 3 12
Australia 26 147 163 187 497 19 5 5 5 15 45 192 168 152 512
Austria 27 18 33 36 87 23 64 81 87 232 50 123 114 82 319
Azerbaijan 6 7 11 24 42 6 0 0 0 0 12 24 11 7 42
Bahamas 16 6 2 6 14 0 0 0 0 0 16 6 2 6 14
Bahrain 9 2 1 0 3 0 0 0 0 0 9 0 1 2 3
Barbados 12 0 0 1 1 0 0 0 0 0 12 1 0 0 1
Belarus 6 12 27 39 78 7 8 5 5 18 13 44 32 20 96
Belgium 26 40 53 55 148 21 1 2 3 6 47 58 55 41 154
Bermuda 18 0 0 1 1 8 0 0 0 0 26 1 0 0 1
Bohemia 3 0 1 3 4 0 0 0 0 0 3 3 1 0 4
Botswana 10 0 1 0 1 0 0 0 0 0 10 0 1 0 1
Brazil 22 30 36 63 129 8 0 0 0 0 30 63 36 30 129
British WestIndies 1 0 0 2 2 0 0 0 0 0 1 2 0 0 2
Bulgaria 20 51 87 80 218 20 1 2 3 6 40 83 89 52 224
Burundi 6 1 1 0 2 0 0 0 0 0 6 0 1 1 2
Cameroon 14 3 1 2 6 1 0 0 0 0 15 2 1 3 6
Canada 26 64 102 136 302 23 73 64 62 199 49 198 166 137 501
Chile 23 2 7 4 13 17 0 0 0 0 40 4 7 2 13
China 10 224 167 155 546 11 13 28 21 62 21 176 195 237 608
Colombia 19 5 9 14 28 2 0 0 0 0 21 14 9 5 28
Costa Rica 15 1 1 2 4 6 0 0 0 0 21 2 1 1 4
Ivory Coast 13 1 1 1 3 0 0 0 0 0 13 1 1 1 3
Croatia 7 11 10 12 33 8 4 6 1 11 15 13 16 15 44
Cuba 20 78 68 80 226 0 0 0 0 0 20 80 68 78 226
Cyprus 10 0 1 0 1 11 0 0 0 0 21 0 1 0 1
Czech Republic 6 15 17 24 56 7 9 11 11 31 13 35 28 24 87
Czechoslovakia 16 49 49 45 143 16 2 8 15 25 32 60 57 51 168
Denmark 27 45 74 75 194 14 0 1 0 1 41 75 75 45 195
Djibouti 8 0 0 1 1 0 0 0 0 0 8 1 0 0 1
Dominican Republic 14 3 2 2 7 0 0 0 0 0 14 2 2 3 7
Ecuador 14 1 1 0 2 1 0 0 0 0 15 0 1 1 2
Egypt 22 7 10 15 32 1 0 0 0 0 23 15 10 7 32
Eritrea 5 0 0 1 1 1 0 0 0 0 6 1 0 0 1
Estonia 12 9 9 16 34 10 4 2 1 7 22 17 11 13 41
Ethiopia 13 22 11 21 54 2 0 0 0 0 15 21 11 22 54
Fiji 14 1 0 0 1 3 0 0 0 0 17 0 0 1 1
Finland 25 101 85 117 303 23 43 63 61 167 48 178 148 144 470
France 28 212 241 263 716 23 36 35 53 124 51 316 276 248 840
Gabon 10 0 1 0 1 0 0 0 0 0 10 0 1 0 1
Georgia 6 8 7 17 32 7 0 0 0 0 13 17 7 8 32
Germany 16 191 194 230 615 12 92 88 60 240 28 290 282 283 855
UnitedTeamofGermany 3 28 54 36 118 3 8 6 5 19 6 41 60 36 137
East Germany 5 153 129 127 409 6 39 36 35 110 11 162 165 192 519
West Germany 5 56 67 81 204 6 11 15 13 39 11 94 82 67 243
Ghana 14 0 1 3 4 2 0 0 0 0 16 3 1 0 4
Great Britain 28 263 295 293 851 23 11 4 17 32 51 310 299 274 883
Greece 28 33 43 40 116 19 0 0 0 0 47 40 43 33 116
Grenada 9 1 1 0 2 0 0 0 0 0 9 0 1 1 2
Guatemala 14 0 1 0 1 1 0 0 0 0 15 0 1 0 1
Guyana 17 0 0 1 1 0 0 0 0 0 17 1 0 0 1
Haiti 15 0 1 1 2 0 0 0 0 0 15 1 1 0 2
Hong Kong 16 1 1 1 3 5 0 0 0 0 21 1 1 1 3
Hungary 26 175 147 169 491 23 1 2 4 7 49 173 149 176 498
Iceland 20 0 2 2 4 18 0 0 0 0 38 2 2 0 4
India 24 9 7 12 28 10 0 0 0 0 34 12 7 9 28
Indonesia 15 7 13 12 32 0 0 0 0 0 15 12 13 7 32
Iran 16 21 21 27 69 11 0 0 0 0 27 27 21 21 69
Iraq 14 0 0 1 1 0 0 0 0 0 14 1 0 0 1
Ireland 21 9 10 12 31 7 0 0 0 0 28 12 10 9 31
Israel 16 1 1 7 9 7 0 0 0 0 23 7 1 1 9
Italy 27 206 178 193 577 23 40 36 48 124 50 241 214 246 701
Jamaica 17 22 35 21 78 8 0 0 0 0 25 21 35 22 78
Japan 22 142 136 161 439 21 14 22 22 58 43 183 158 156 497
Jordan 10 1 0 0 1 0 0 0 0 0 10 0 0 1 1
Kazakhstan 6 15 21 28 64 7 1 3 4 8 13 32 24 16 72
Kenya 14 31 38 34 103 4 0 0 0 0 18 34 38 31 103
Kosovo 1 1 0 0 1 1 0 0 0 0 2 0 0 1 1
North Korea 10 16 16 22 54 9 0 1 1 2 19 23 17 16 56
South Korea 17 90 87 90 267 18 31 25 14 70 35 104 112 121 337
Kuwait 12 0 0 2 2 0 0 0 0 0 12 2 0 0 2
Kyrgyzstan 6 0 1 3 4 7 0 0 0 0 13 3 1 0 4
Latvia 11 3 11 5 19 11 1 3 5 9 22 10 14 4 28
Lebanon 17 0 2 2 4 17 0 0 0 0 34 2 2 0 4
Liechtenstein 17 0 0 0 0 19 2 2 6 10 36 6 2 2 10
Lithuania 9 6 6 13 25 9 0 0 0 0 18 13 6 6 25
Luxembourg 23 1 1 0 2 9 0 2 0 2 32 0 3 1 4
Malaysia 13 0 7 4 11 1 0 0 0 0 14 4 7 0 11
Mauritius 9 0 0 1 1 0 0 0 0 0 9 1 0 0 1
Mexico 23 13 24 32 69 9 0 0 0 0 32 32 24 13 69
Moldova 6 0 2 3 5 7 0 0 0 0 13 3 2 0 5
Mongolia 13 2 10 14 26 14 0 0 0 0 27 14 10 2 26
Montenegro 3 0 1 0 1 3 0 0 0 0 6 0 1 0 1
Morocco 14 6 5 12 23 7 0 0 0 0 21 12 5 6 23
Mozambique 10 1 0 1 2 0 0 0 0 0 10 1 0 1 2
Namibia 7 0 4 0 4 0 0 0 0 0 7 0 4 0 4
Netherlands 26 85 92 108 285 21 45 44 41 130 47 149 136 130 415
Netherlands Antilles 13 0 1 0 1 2 0 0 0 0 15 0 1 0 1
New Zealand 23 46 27 44 117 16 0 1 2 3 39 46 28 46 120
Niger 12 0 1 1 2 0 0 0 0 0 12 1 1 0 2
Nigeria 16 3 10 12 25 1 0 0 0 0 17 12 10 3 25
North Macedonia 6 0 0 1 1 6 0 0 0 0 12 1 0 0 1
Norway 25 56 49 47 152 23 132 125 111 368 48 158 174 188 520
Pakistan 17 3 3 4 10 3 0 0 0 0 20 4 3 3 10
Panama 17 1 0 2 3 0 0 0 0 0 17 2 0 1 3
Paraguay 12 0 1 0 1 1 0 0 0 0 13 0 1 0 1
Peru 18 1 3 0 4 2 0 0 0 0 20 0 3 1 4
Philippines 21 0 3 7 10 5 0 0 0 0 26 7 3 0 10
Poland 21 68 84 132 284 23 7 7 8 22 44 140 91 75 306
Portugal 24 4 8 12 24 8 0 0 0 0 32 12 8 4 24
Puerto Rico 18 1 2 6 9 7 0 0 0 0 25 6 2 1 9
Qatar 9 0 1 4 5 0 0 0 0 0 9 4 1 0 5
Romania 21 89 95 122 306 21 0 0 1 1 42 123 95 89 307
Russia 6 149 125 152 426 6 47 38 35 120 12 187 163 196 546
Russian Empire 3 1 4 3 8 0 0 0 0 0 3 3 4 1 8
Soviet Union 9 395 319 296 1,010 9 78 57 59 194 18 355 376 473 1204
Unified Team 1 45 38 29 112 1 9 6 8 23 2 37 44 54 135
Russia 0 0 0 0 0 1 2 6 9 17 1 9 6 2 17
Saudi Arabia 11 0 1 2 3 0 0 0 0 0 11 2 1 0 3
Samoa 9 0 1 0 1 0 0 0 0 0 9 0 1 0 1
Senegal 14 0 1 0 1 5 0 0 0 0 19 0 1 0 1
Serbia 4 3 6 6 15 3 0 0 0 0 7 6 6 3 15
SerbiaandMontenegro 1 0 2 0 2 1 0 0 0 0 2 0 2 0 2
Singapore 16 1 2 2 5 1 0 0 0 0 17 2 2 1 5
Slovakia 6 9 12 7 28 7 3 4 1 8 13 8 16 12 36
Slovenia 7 5 8 10 23 8 2 5 10 17 15 20 13 7 40
South Africa 19 26 31 29 86 7 0 0 0 0 26 29 31 26 86
Spain 23 45 64 41 150 20 1 0 3 4 43 44 64 46 154
Sri Lanka 17 0 2 0 2 0 0 0 0 0 17 0 2 0 2
Sudan 12 0 1 0 1 0 0 0 0 0 12 0 1 0 1
Suriname 12 1 0 1 2 0 0 0 0 0 12 1 0 1 2
Sweden 27 145 170 179 494 23 57 46 55 158 50 234 216 202 652
Switzerland 28 50 75 67 192 23 56 45 52 153 51 119 120 106 345
Syria 13 1 1 1 3 0 0 0 0 0 13 1 1 1 3
Chinese Taipei 14 5 7 12 24 12 0 0 0 0 26 12 7 5 24
Tajikistan 6 1 1 2 4 4 0 0 0 0 10 2 1 1 4
Tanzania 13 0 2 0 2 0 0 0 0 0 13 0 2 0 2
Thailand 16 9 8 16 33 4 0 0 0 0 20 16 8 9 33
Togo 10 0 0 1 1 2 0 0 0 0 12 1 0 0 1
Tonga 9 0 1 0 1 2 0 0 0 0 11 0 1 0 1
TrinidadandTobago 17 3 5 11 19 3 0 0 0 0 20 11 5 3 19
Tunisia 14 4 2 7 13 0 0 0 0 0 14 7 2 4 13
Turkey 22 39 24 28 91 17 0 0 0 0 39 28 24 39 91
Uganda 15 2 3 2 7 0 0 0 0 0 15 2 3 2 7
Ukraine 6 34 30 56 120 7 3 1 4 8 13 60 31 37 128
UnitedArabEmirates 9 1 0 1 2 0 0 0 0 0 9 1 0 1 2
United States 27 1,022 795 706 2,523 23 105 112 88 305 50 794 907 1127 2828
Uruguay 21 2 2 6 10 1 0 0 0 0 22 6 2 2 10
Uzbekistan 6 7 6 18 31 7 1 0 0 1 13 18 6 8 32
Venezuela 18 2 4 9 15 4 0 0 0 0 22 9 4 2 15
Vietnam 15 1 3 1 5 0 0 0 0 0 15 1 3 1 5
Virgin Islands 12 0 1 0 1 7 0 0 0 0 19 0 1 0 1
Yugoslavia 18 28 31 31 90 16 0 3 1 4 34 32 34 28 94
Zambia 13 0 1 1 2 0 0 0 0 0 13 1 1 0 2
Zimbabwe 13 3 4 1 1 0 0 0 0 14 1 4 3 8
1 个解决方案
解决方案1
0 已采纳 2020-09-03 14:35:16
图像以这种方式出现是因为查询仅检索国家/地区列表，而不是它们的奖牌。 您需要的是以下查询 -
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def hist():
df=pd.read_csv('C:\\Users\Bhuwan Bhatt\Desktop\IP PROJECT\Book1.csv',encoding='ISO-8859-1')
df=df.sort_values('TotalMedal',ascending=False)
df1=df.head(n=10)
df1 = df1.loc[:,["Country","TotalMedal"]]
result = df1.groupby("Country")["TotalMedal"].sum() # (or use mean if you want)
plt.bar(result.index.tolist(),result.values.tolist())
# Your formatting goes there
plt.xticks(rotation=20)
plt.title('HISTOGRAM COUNTRY V/S MEDALS',color='hotpink')
plt.xlabel('Country~~~~>',color='navy')
plt.ylabel('No. of Medals~~~~>',color='navy')
plt.show()
hist()
Y 轴值在双条形图中未正确显示
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