Use Python Code Your task is : Use pandas to read the csv table. Example df = pd.read_csv('COVID19.csv' ), then make a visualization of confirmed cases of COVID-19 by country using matplotlib, seaborn, etc. Just tell me how to do it ! Why this question always get rejected ?! Covid-19 Confirmed table (csv) : Province/State Country Lat Long ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 2/1/2020 2/2/2020 2/3/2020 2/4/2020 2/5/2020 2/6/2020 2/7/2020 2/8/2020 2/9/2020 ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 3/1/2020 3/2/2020 3/3/2020 3/4/2020 3/5/2020 3/6/2020 3/7/2020 3/8/2020 3/9/2020 ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 4/1/2020 4/2/2020 4/3/2020 4/4/2020 4/5/2020 4/6/2020 4/7/2020   Afghanistan 33 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 5 7 7 7 11 16 21 22 22 22 24 24 40 40 74 84 94 110 110 120 170 174 237 273 281 299 349 367 423   Albania 41.1533 20.1683 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 10 12 23 33 38 42 51 55 59 64 70 76 89 104 123 146 174 186 197 212 223 243 259 277 304 333 361 377 383   Algeria 28.0339 1.6596 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 3 5 12 12 17 17 19 20 20 20 24 26 37 48 54 60 74 87 90 139 201 230 264 302 367 409 454 511 584 716 847 986 1171 1251 1320 1423 1468   Andorra 42.5063 1.5218 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 39 39 53 75 88 113 133 164 188 224 267 308 334 370 376 390 428 439 466 501 525 545   Angola -11.2027 17.8739 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 3 3 3 4 4 5 7 7 7 8 8 8 10 14 16 17   Antigua and Barbuda 17.0608 -61.7964 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 3 3 3 7 7 7 7 7 7 7 9 15 15 15 15 19   Argentina -38.4161 -63.6167 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 8 12 12 17 19 19 31 34 45 56 68 79 97 128 158 266 301 387 387 502 589 690 745 820 1054 1054 1133 1265 1451 1451 1554 1628   Armenia 40.0691 45.0382 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 4 8 18 26 52 78 84 115 136 160 194 235 249 265 290 329 407 424 482 532 571 663 736 770 822 833 853 Australian Capital Territory Australia -35.4735 149.0124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 3 4 6 9 19 32 39 39 53 62 71 77 78 80 84 87 91 93 96 96 96 New South Wales Australia -33.8688 151.2093 0 0 0 0 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 13 22 22 26 28 38 48 55 65 65 92 112 134 171 210 267 307 353 436 669 669 818 1029 1219 1405 1617 1791 2032 2032 2182 2298 2389 2493 2580 2637 2686 Northern Territory Australia -12.4634 130.8456 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 3 3 5 5 6 6 12 12 15 15 15 17 19 21 22 26 27 28 28 Queensland Australia -28.0167 153.4 0 0 0 0 0 0 0 1 3 2 3 2 2 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 11 11 13 13 13 15 15 18 20 20 35 46 61 68 78 94 144 184 221 259 319 397 443 493 555 625 656 689 743 781 835 873 900 907 921 934 South Australia Australia -34.9285 138.6007 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 5 5 7 7 7 7 7 9 9 16 19 20 29 29 37 42 50 67 100 134 170 170 235 257 287 299 305 337 367 367 396 407 407 411 411 Tasmania Australia -41.4545 145.9707 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 3 3 5 5 6 7 7 10 10 10 16 22 28 28 36 47 47 62 66 66 69 69 72 74 80 82 86 89 Victoria Australia -37.8136 144.9631 0 0 0 0 1 1 1 1 2 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 7 7 9 9 10 10 10 11 11 15 18 21 21 36 49 57 71 94 121 121 121 229 355 355 411 466 520 574 685 769 821 917 968 1036 1085 1115 1135 1158 1191 Western Australia Australia -31.9505 115.8605 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 3 3 3 3 4 6 9 9 14 17 17 28 31 35 52 64 90 120 140 175 175 231 231 278 311 355 364 392 400 400 436 453 460 460   Austria 47.5162 14.5501 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 3 3 9 14 18 21 29 41 55 79 104 131 182 246 302 504 655 860 1018 1332 1646 2013 2388 2814 3582 4474 5283 5588 6909 7657 8271 8788 9618 10180 10711 11129 11524 11781 12051 12297 12639   Azerbaijan 40.1431 47.5769 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 6 6 9 9 9 11 11 11 15 15 23 28 28 28 44 44 53 65 72 87 93 122 165 182 209 273 298 359 400 443 521 584 641 717   Bahamas 25.0343 -77.3963 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 3 3 4 4 4 5 5 9 10 10 11 14 14 21 24 24 28 28 29 33   Bahrain 26.0275 50.55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 23 33 33 36 41 47 49 49 52 55 60 85 85 95 110 195 195 195 210 214 214 228 256 278 285 305 334 377 392 419 458 466 476 499 515 567 569 643 672 688 700 756 811   Bangladesh 23.685 90.3563 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 5 8 10 14 17 20 25 27 33 39 39 44 48 48 48 49 51 54 56 61 70 88 123 164   Barbados 13.1939 -59.5432 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 5 5 6 14 17 18 18 18 24 26 33 33 34 34 46 51 52 56 60 63   Belarus 53.7098 27.9534 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 6 6 6 6 6 6 9 9 12 27 27 27 36 36 51 51 69 76 76 81 81 86 86 94 94 94 152 152 163 304 351 440 562 700 861

The file london_weather.csv  contains weather data for the years 1979 to 2020. The first five lines of this file are: date,cloud_cover,sunshine,global_radiation,max_temp,mean_temp,min_temp,precipitation,pressure,snow_depth19790101,2,7,52,2.3,-4.1,-7.5,0.4,101900,919790102,6,1.7,27,1.6,-2.6,-7.5,0,102530,819790103,5,0,13,1.3,-2.8,-7.2,0,102050,419790104,8,0,13,-0.3,-2.6,-6.5,0,100840,2 Note, in particular, that the dates are given as eight-digit integers in the format YYYYMMDD. There appears to be some inconsistencies in this data, as there are rows for which the column max_temp contains an entry that is smaller than the corresponding entry in min_temp. Write a Python function wrong_temperature(year) that returns the number of days in a given year for which the column max_temp contains an entry that is smaller than the corresponding entry in the column min_temp.

TODO 12 Let's now split our input data X and labels y into a train and test set using the train_test_split() function (docs). Here we'll use the 80-20 split rule where we use 80% of the data for training and 20% for testing. Lastly, we'll seed our split using the random_state keyword argument which will make sure we create the same split every time we run the function. Use the train_test_split() function to get a train and test split. Store the output into X_train, X_test, y_train, and y_test. Pass the required arguments X and y. Further specify we want to use 20% of the training data by setting the test_size keyword argument. Lastly, pass the keyword argument random_state=42 to set the random seed so we get the same split every time we run this code. Print the shape for X_train. Print the shape for y_train. Print the shape for X_test. Print the shape for y_test. # TODO 12.1X_train, X_test, y_train, y_test =  todo_check([    (X_train.shape == (413, 29), 'X_train does not have the…

A table called "game1" contains three columns and twenty rows, but "game2" has the same column as game1 (ie 3) and fifteen rows. Both tables have 5 rows in common. What is the degree and cardinality of the resulting table if we take union?

Please show me a way how to extract and open tsv.gz extension file  for analyzing Twitter dataset using NetworkX using python. Please show me an example of how to read the dataset. This is the dataset I am having trouble to open full_dataset_clean.tsv.gz

You have the table T = (name, ssn, phone number) representing a person. Each name is uniquely identified by ssn, but the same name can have multiple phone numbers. Suppose you break the tables into two tables T1=(name, ssn) and T2= (name, phone number) a) What are the candidate keys of T, T1, and T2, respectively?

3. Consider the following tables and then provide a pipe diagram for the requested output. ID Age ID Gender ID Name ID Friend_ID 15 3 M Bob 6. 3 31 F 6. Maria Sally George Maria 8 45 F 8 8 11 11 33 11 F 2 11 8 19 2 11 8 ... ... ... ... ... ... a. The average age of users b. The name of the user with the most friends. c. The pairs of user ID for those that follow one another (e.g., if 3 is a friend of 6 and 6 is a friend of 3 then (3, 6) would be listed as one of the pairs). d. The 10 most common names for users under the age of 20. 4. As discussed in class, what are some advantages of functional programming? 5. Explain how parallel programing can be accomplished using Streams and functional programming in Java 8. 6. What aspects of functional programming align well parallel programming?

How to do this? I mean formula Create a Vlookup Table for Children. Give it a name and use that to create a new column Size of Family 0 "No Children" 2 or less "Small" 3 or more "Big"