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- I want this code using local search not backtracking!please delete X AREAS AND SEND MEConsider the following code: import pandas as pd df = pd.DataFrame({'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada'], 'year': [2000, 2001, 2002, 2001], 'pop': [1.5, 1.7, 3.6, 2.4]}) How can you sort the data? df.sort_values(by=['year']) Select one: True False
- Complete and modify the code: 1 def match(x,y): 2 """Return the number with the smallest absolute value 3 4 >>> match(-10,5) 5 -10 6 7 """ 8 9 return x 10 11 match(0, -2) 12 match(10, -21) 13 match(-5, 25)C++ i will submit it on harckerrank please provide the exact code that doesn't need any change.Which code was used to make the plot below from the following dataset called df ?
- Perform an AJAX GET query to retrieve glossary terms in JSON format from [example.json]. Make sure to specify the value “application/json” in the accept header, or the request will return a documentation page instead. (where do i put "application/json"?) Display each of the entries under the “results” key as a table row. Then, create a new copy of that. Modify the returned results such that all instances of the word “Account” are displayed in a bold font-weight.In cell H2, get the unique Area values using UNIQUE(). In column I, use COUNTIF() to get the count of each Area. Pass the data range from A2 to A45 as absolute addresses, and the filter criteria from H2 to H9. In column J, use COUNTIFS() to get the count of each area with more than 20 Swallow-tails. the first two arguments are the same as the previous step, then add a condition for column D to be greater than 20. Area Locality Skipper Swallowtail White-yellow Blue Brush-footed Area Count of Area Count of Area with 20 Swallowtails Indian Subcontinent Indian Subcontinent 307 94 99 458 482 Western Himalaya Western Himalaya 63 31 42 129 152 Western Himalaya Kangra Hills 25 23 37 56 87 Western Himalaya Shimla Hills 41 21 34 88 115 Western Himalaya Dehradun Valley 22 11 19 42 54 Western Himalaya Mussoorie Hills 54 23 32 88 126 Western Himalaya Mussoorie Town 14 10 13 44 65 Western Himalaya Kumaon Hills 52 26 37 109 147…This is Pandas Which of the following would be a correct way to return rows 0 through 5 of columns "Name" and "Age" from the data frame df? df.loc[0:5, ["Name", "Age"]] df.loc[:5, "Name", "Age"] df.loc[0:6, ["Name", "Age"]] df.loc[:6, "Name", "Age"]
- using R Studio Using built-in dataset, create R code to extract first 15 english letter in lower case and last 15 letters in upper case and extract letters between 10th to 20th letters in upper casesUpdate the code below for me create a pie chart based on the data provided below. import matplotlib.pyplot as plt # Sample data referral_sources = ['Website', 'Word of Mouth', 'Advertisement', 'Social Media'] counts = [30.34%, 14.5%, 50.70%, 60.9%] plt.bar(referral_sources, counts) plt.title('Referral Sources Count') plt.xlabel('Referral Source') plt.ylabel('Count')why when add new index in python data frame its make all columns nan and when I remove it not make column nan ? ex ) I create data frame with index [f1 t0 f30] when I create to f9 its ok all but when add f10 make all before data null?