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- The following Excel image has a VBA program (see below). What will be the result after the program is executed? Any cells not displayed are currently empty, and any cells with numbers are formatted numeric. A B C D 1 2 3 UN Gross Domestic Product (GDP) per Capita 4 5 6 7 8 9 10 11 12 Total Year/Country Angola Portugal France 2000 2005 2010 2015 2020 10 10 10 10 10 50 100 100 100 100 100 500 1,000 1,000 1,000 1,000 1,000 5,000 E Mexico 10,000 10,000 10,000 10,000 10,000 50,000Import the dataset Student.csv. Display only the variable names of the Student.csv dataset. code in R language. StudentID First Last Math Science Social Studies 11 Bob Smith 90 80 67 12 Jane Weary 75 80 10 Dan Thornton, III 65 75 70 40 Mary O'Leary 90 95 92Json document: P = { "p": [ { "logType": “PDF, "accountId": “xxxx” }, { "logType": “PDF”, "accountId": “xxxx” }, { "logType": “PDF”, "accountId": “xxxx” }, { "logType": “PDF”, "accountId": “xxxx” }, { "logType": “PDF”, "accountId": “xxxx” } ] } Then, I want to count the total of logType and accountId like this in python { "logType": 5, "accountId": 5 }
- Data Frame: my code import numpy as npimport pandas as pdnp.random.seed(1)import datetimetoday = datetime.date.today()someday = datetime.date(2016, 1, 1)diff = today - somedayrows,cols = diff.days,6data = np.random.rand(rows,cols) tidx = pd.date_range('2016-01-01', periods=rows, freq='D')data_frame = pd.DataFrame(data, columns=['a','b','c','d','e','f'], index=tidx)print(data_frame) Question - Plot the six sets of row values of df_accumulate in separate graphs using subplots , then plot the accumulate sum of each of the columns (random numbers).def print_table( values: tuple[float, ) -> None: ], drag_coeff: float, increments: int, step: float 3 4 Parameters: 6 values (tuple[float, ...]): mass, force, ref_area, density, init_velocity, lift_velocity, start_position, time_inc drag_coeff (float): The drag coefficient. increments (int): The number of drag coefficients displayed. step (float): The difference between each drag coefficient. 8 9 10 11 12 Returns: 13 None 14 II II 15 For this function you need to compute the distance before lift-off for a range of drag coefficients and then you need to print these results in a table. The drag coefficient of an aeroplane has a significant impact on the plane's ability to lift-off. If the drag coefficient is sufficiently high, the plane will not actually be able to generate enough speed to lift off. In this task you will write a function which will explore this phenomenon.Automated Title: In eq_world_map_3.py, we specified the title manually when defining my_layout, which means we have to remember to update the title every time the source file changes. Instead, you can use the title for the data set in the metadata part of the JSON file. Pull this value, assign it to a variable, and use this for the title of the map when you’re defining my_layout.
- Create the following data frame, afterwards invert Sex for all individuals. Age Height Weight Sex Alex 25 177 57 F 31 163 69 Lilly Mark F 23 190 83 M Oliver 52 179 75 M Martha 76 163 70 F Lucas 49 183 83 M Caroline 26 164 53 FData Frame: import numpy as npimport pandas as pdnp.random.seed(1)import datetimetoday = datetime.date.today()someday = datetime.date(2016, 1, 1)diff = today - somedayrows,cols = diff.days,6data = np.random.rand(rows,cols) tidx = pd.date_range('2016-01-01', periods=rows, freq='D')data_frame = pd.DataFrame(data, columns=['a','b','c','d','e','f'], index=tidx)print(data_frame) Question - Calculate the cumulative sum of the Data frame (using the <DataFrame>.cumsum() function) andstore it in a new data frame called: df_accumulate. Display the first five rows of your data to verifythe values.Data Frame: import numpy as npimport pandas as pdnp.random.seed(1)import datetimetoday = datetime.date.today()someday = datetime.date(2016, 1, 1)diff = today - somedayrows,cols = diff.days,6data = np.random.rand(rows,cols) tidx = pd.date_range('2016-01-01', periods=rows, freq='D')data_frame = pd.DataFrame(data, columns=['a','b','c','d','e','f'], index=tidx)print(data_frame) Question - Show the first 30 rows of your data (using a Pandas data frame function).
- Data Frame: import numpy as npimport pandas as pdnp.random.seed(1)import datetimetoday = datetime.date.today()someday = datetime.date(2016, 1, 1)diff = today - somedayrows,cols = diff.days,6data = np.random.rand(rows,cols) tidx = pd.date_range('2016-01-01', periods=rows, freq='D')data_frame = pd.DataFrame(data, columns=['a','b','c','d','e','f'], index=tidx)print(data_frame) Question - Calculate the cumulative sum of the Data frame (using the <DataFrame>.cumsum() function) andstore it in a new data frame called: df_accumulate and Plot the six sets of row values of df_accumulate in a graphData Frame: import numpy as npimport pandas as pdnp.random.seed(1)import datetimetoday = datetime.date.today()someday = datetime.date(2016, 1, 1)diff = today - somedayrows,cols = diff.days,6data = np.random.rand(rows,cols) tidx = pd.date_range('2016-01-01', periods=rows, freq='D')data_frame = pd.DataFrame(data, columns=['a','b','c','d','e','f'], index=tidx)print(data_frame) Question - Plot the six sets of row values of df_accumulate in separate graphs using subplots , then plot the accumulate sum of each of the columns (random numbers).The constants are: COLUMN_ID = 0COLUMN_NAME = 1COLUMN_HIGHWAY = 2COLUMN_LAT = 3COLUMN_LON = 4COLUMN_YEAR_BUILT = 5COLUMN_LAST_MAJOR_REHAB = 6COLUMN_LAST_MINOR_REHAB = 7COLUMN_NUM_SPANS = 8COLUMN_SPAN_DETAILS = 9COLUMN_DECK_LENGTH = 10COLUMN_LAST_INSPECTED = 11COLUMN_BCI = 12 INDEX_BCI_YEARS = 0INDEX_BCI_SCORES = 1MISSING_BCI = -1.0 EARTH_RADIUS = 6371 This is an example of one of the bridges which is a list. def create_example_bridge_1() -> list:"""Return a bridge in our list-format to use for doctest examples. This bridge is the same as the bridge from row three of the dataset.""" return [1, 'Highway 24 Underpass at Highway 403','403', 43.167233, -80.275567, '1965', '2014', '2009', 4,[12.0, 19.0, 21.0, 12.0], 65.0, '04/13/2012',[['2013', '2012', '2011', '2010', '2009', '2008', '2007','2006', '2005', '2004', '2003', '2002', '2001', '2000'],[MISSING_BCI, 72.3, MISSING_BCI, 69.5, MISSING_BCI, 70.0, MISSING_BCI,70.3, MISSING_BCI, 70.5, MISSING_BCI, 70.7, 72.9, MISSING_BCI]]] def…