Python Exercise: Display first 5 rows from COVID-19 dataset
Python Project: COVID-19 Exercise-1 with Solution
Write a Python program to display first 5 rows from COVID-19 dataset. Also print the dataset information and check the missing values.
Sample Solution:
Python Code:
import pandas as pd
covid_data= pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-17-2020.csv')
print(covid_data)
print("\nDataset information:")
print(covid_data.info())
print("\nMissing data information:")
print(covid_data.isna().sum())
Sample Output:
Dataset information:
<class 'pandas.core.frame.DataFrame'>
Province/State ... Longitude
0 Hubei ... 112.2707
1 NaN ... 12.5674
2 NaN ... 53.6880
3 NaN ... -3.7492
4 NaN ... 10.4515
5 NaN ... 127.7669
6 France ... 2.2137
7 NaN ... 8.2275
8 United Kingdom ... -3.4360
9 New York ... -74.9481
10 Netherlands ... 5.2913
11 NaN ... 8.4689
12 Guangdong ... 113.4244
13 NaN ... 14.5501
14 Henan ... 113.6140
15 NaN ... 4.4699
16 Zhejiang ... 120.0934
17 NaN ... 18.6435
18 Washington ... -121.4905
19 Hunan ... 111.7088
20 Anhui ... 117.2264
21 Denmark ... 9.5018
22 Jiangxi ... 115.7221
23 NaN ... 138.2529
24 Shandong ... 118.1498
25 California ... -119.6816
26 Diamond Princess ... 139.6649
27 NaN ... 101.9758
28 Jiangsu ... 119.4550
29 Chongqing ... 107.8740
30 Sichuan ... 102.7103
31 Heilongjiang ... 127.7615
32 Beijing ... 116.4142
33 NaN ... -8.2245
34 NaN ... 51.1839
35 NaN ... 15.4730
36 NaN ... 21.8243
37 Shanghai ... 121.4491
38 NaN ... 34.8516
39 NaN ... -51.9253
40 NaN ... 25.7482
41 Hebei ... 114.5149
42 Fujian ... 117.9874
43 NaN ... 14.9955
44 New Jersey ... -74.5210
45 NaN ... 103.8198
46 Guangxi ... 108.7881
47 Shaanxi ... 108.8701
48 NaN ... 19.1451
49 NaN ... 69.3451
50 NaN ... 50.5577
51 NaN ... 25.0136
52 NaN ... -7.6921
53 NaN ... -19.0208
54 Massachusetts ... -71.5301
55 Florida ... -81.6868
56 New South Wales ... 151.2093
57 NaN ... -71.5430
58 NaN ... 30.8025
59 Louisiana ... -91.8678
60 NaN ... 121.7740
61 Ontario ... -85.3232
62 NaN ... 24.9668
63 NaN ... 100.9925
64 Yunnan ... 101.4870
65 NaN ... 113.9213
66 NaN ... 45.0792
67 Hainan ... 109.7453
68 Hong Kong ... 114.2000
69 Illinois ... -88.9861
70 Colorado ... -105.3111
71 NaN ... 43.6793
72 Guizhou ... 106.8748
73 Georgia ... -83.6431
74 NaN ... 78.9629
75 NaN ... 6.1296
76 Tianjin ... 117.3230
77 Gansu ... 103.8343
78 Shanxi ... 112.2922
79 NaN ... 47.4818
80 Liaoning ... 122.6085
81 NaN ... 35.8623
82 NaN ... -75.0152
83 NaN ... 105.3188
84 Pennsylvania ... -77.2098
85 Texas ... -97.5635
86 NaN ... 12.4578
87 British Columbia ... -127.6476
88 NaN ... 53.8478
89 Victoria ... 144.9631
90 Jilin ... 126.1923
91 NaN ... -102.5528
92 NaN ... 45.0382
93 Queensland ... 153.4000
94 NaN ... 121.0000
95 Xinjiang ... 85.2401
96 Inner Mongolia ... 113.9448
97 Ningxia ... 106.1655
98 Alberta ... -116.5765
99 Quebec ... -73.5491
100 Tennessee ... -86.6923
101 NaN ... 19.6990
102 Wisconsin ... -89.6165
103 NaN ... -80.7821
104 NaN ... -63.6167
105 Connecticut ... -72.7554
106 NaN ... 25.4858
107 Ohio ... -82.7649
108 Virginia ... -78.1700
109 Oregon ... -122.0709
110 NaN ... 108.2772
111 NaN ... -74.2973
112 NaN ... 15.2000
113 NaN ... 21.0059
114 Michigan ... -84.5361
115 North Carolina ... -79.8064
116 NaN ... 22.9375
117 NaN ... 1.6596
118 Maryland ... -76.8021
119 Minnesota ... -93.9002
120 NaN ... -78.1834
121 NaN ... 114.7277
122 Nevada ... -117.0554
123 NaN ... 20.1683
124 Utah ... -111.8624
125 NaN ... 19.5033
126 NaN ... 24.6032
127 Faroe Islands ... -6.9118
128 NaN ... 35.2433
129 Diamond Princess ... 139.6380
130 South Carolina ... -80.9450
131 NaN ... 33.4299
132 NaN ... 80.7718
133 NaN ... -83.7534
134 NaN ... 1.5218
135 Alabama ... -86.9023
136 NaN ... 14.3754
137 NaN ... -7.0926
138 NaN ... 27.9534
139 NaN ... 43.3569
140 NaN ... 36.2384
141 NaN ... 104.9910
142 NaN ... 66.9237
143 NaN ... -66.5897
144 Maine ... -69.3819
145 Western Australia ... 115.8605
146 NaN ... 28.3699
147 Indiana ... -86.2583
148 South Australia ... 138.6007
149 NaN ... -55.7658
150 NaN ... 47.5769
151 NaN ... 17.6791
152 NaN ... 21.7453
153 NaN ... -14.4524
154 Kentucky ... -84.6701
155 New Hampshire ... -71.5639
156 NaN ... 23.8813
157 NaN ... 55.9754
158 NaN ... 9.5375
159 Iowa ... -93.2105
160 New Mexico ... -106.2485
161 Rhode Island ... -71.5118
162 NaN ... 67.7100
163 Arkansas ... -92.3731
164 District of Columbia ... -77.0268
165 NaN ... -70.1627
166 Grand Princess ... -122.6655
167 Mississippi ... -89.6787
168 Nebraska ... -98.2681
169 Arizona ... -111.4312
170 Oklahoma ... -96.9289
171 Qinghai ... 95.9956
172 NaN ... -61.5510
173 Kansas ... -96.7265
174 NaN ... -61.0242
175 Delaware ... -75.5071
176 NaN ... -1.5616
177 NaN ... 31.1656
178 NaN ... 73.2207
179 Macau ... 113.5500
180 NaN ... -77.2975
181 NaN ... 174.8860
182 Vermont ... -72.7107
183 NaN ... -63.5887
184 NaN ... -53.1258
185 Missouri ... -92.2884
186 South Dakota ... -99.4388
187 Wyoming ... -107.3025
188 NaN ... 90.3563
189 NaN ... 11.5021
190 Hawaii ... -157.4983
191 NaN ... 64.5853
192 Reunion ... 55.2471
193 NaN ... -58.4438
194 NaN ... 55.5364
195 Montana ... -110.4544
196 Grand Princess ... -122.6655
197 Manitoba ... -98.8139
198 New Brunswick ... -66.4619
199 NaN ... -86.2419
200 Idaho ... -114.4788
201 Tasmania ... 145.9707
202 Nova Scotia ... -63.7443
203 Saskatchewan ... -106.4509
204 French Guiana ... -53.0000
205 NaN ... -1.0232
206 NaN ... -58.9302
207 NaN ... 9.5554
208 NaN ... 7.4246
209 NaN ... 29.8739
210 Guadeloupe ... -61.5833
211 NaN ... -90.2308
212 Channel Islands ... -2.3644
213 NaN ... -5.5471
214 NaN ... -77.7812
215 NaN ... 40.4897
216 NaN ... 103.8467
217 NaN ... -61.2225
218 Puerto Rico ... -66.5901
219 NaN ... 55.4920
220 NaN ... -69.9683
221 Newfoundland and Labrador ... -57.6604
222 NaN ... 21.7587
223 French Polynesia ... -149.4068
224 Saint Barthelemy ... -62.8333
225 NaN ... 144.7937
226 NaN ... 37.9062
227 Curacao ... -68.9900
228 NaN ... 8.6753
229 Alaska ... -152.4044
230 Guam ... 144.7937
231 North Dakota ... -99.7840
232 Gibraltar ... -5.3536
233 Australian Capital Territory ... 149.0124
234 NaN ... -59.5432
235 St Martin ... -63.0501
236 NaN ... 20.9030
237 NaN ... 19.3000
238 NaN ... 18.4904
239 NaN ... -60.9789
240 Virgin Islands ... -64.8963
241 NaN ... -61.7964
242 Northern Territory ... 130.8456
243 NaN ... 2.3158
244 NaN ... 90.4336
245 Prince Edward Island ... -63.4168
246 NaN ... 20.9394
247 Tibet ... 88.0924
248 NaN ... 15.8277
249 NaN ... 10.0000
250 NaN ... 31.4659
251 Mayotte ... 45.1383
252 NaN ... 11.6094
253 NaN ... -42.6043
254 NaN ... -9.6966
255 NaN ... 12.4534
256 NaN ... -9.4295
257 NaN ... -10.9408
258 NaN ... 45.1662
259 NaN ... 84.1240
260 NaN ... -61.2872
261 NaN ... 46.1996
262 NaN ... 30.2176
263 NaN ... -56.0278
264 NaN ... 34.8888
265 NaN ... -76.0000
266 NaN ... -16.6000
267 NaN ... 0.8248
268 West Virginia ... -80.9545
269 Cayman Islands ... -81.2546
270 From Diamond Princess ... 139.6380
271 NaN ... -2.5800
272 NaN ... -2.1100
273 NaN ... -66.5000
274 NaN ... 15.5560
275 NaN ... 35.2332
[276 rows x 8 columns]
Dataset information:
RangeIndex: 276 entries, 0 to 275
Data columns (total 8 columns):
Province/State 126 non-null object
Country/Region 276 non-null object
Last Update 276 non-null object
Confirmed 276 non-null int64
Deaths 276 non-null int64
Recovered 276 non-null int64
Latitude 276 non-null float64
Longitude 276 non-null float64
dtypes: float64(2), int64(3), object(3)
memory usage: 17.4+ KB
None
Missing data information:
Province/State 150
Country/Region 0
Last Update 0
Confirmed 0
Deaths 0
Recovered 0
Latitude 0
Longitude 0
dtype: int64
Python Code Editor:
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Python: Tips of the Day
Groups the elements of a list based on the given function.
Example:
def tips_group_by(lst, fn):
return {key : [el for el in lst if fn(el) == key] for key in map(fn, lst)}
from math import floor
print(tips_group_by([5.5, 4.2, 6.3], floor))
print(tips_group_by(['mango', 'apple', 'banana'], len))
Output:
{5: [5.5], 4: [4.2], 6: [6.3]}
{5: ['mango', 'apple'], 6: ['banana']}
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