use https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=MCRFPUS2&f=M to prepare data for time-series analysis Data needs to be melted All missing data must be removed Year should be converted to ‘string’ data type such that the Year is 4-digit string ‘Month’ and ‘Year’ should be combined and converted to Pandas datetime data type and promoted as index Unnecessary columns should be removed

use https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=MCRFPUS2&f=M to prepare data for time-series analysis Data needs to be melted All missing data must be removed Year should be converted to ‘string’ data type such that the Year is 4-digit string ‘Month’ and ‘Year’ should be combined and converted to Pandas datetime data type and promoted as index Unnecessary columns should be removed

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Introduction to data base
What precisely is metadata? An answer to this question may be found in the result set's metadata. Result set information may be beneficial in a variety of situations.
calculate_trip_time( iata_src: str, iata_dst: str, flight_walk: List[str], flights: Flight Dir float: def ) -> """ Return a float corresponding to the amount of time required to travel from the source airport to the destination airport to the destination airport, as outlined by the flight_walk. The start time of the trip should be considered zero. In other words, assuming we start the trip at 12:00am, this function should return the time it takes for the trip to finish, including all the waiting times before, and between the flights. If there is no path available, return -1.0 >>> calculate_trip_time("AA1", "AA2", ["AA1", "AA2"], TEST_FLIGHTS_DIR_FOUR_ 2.0 "AA7", ["AA7", "AA1"], TEST_FLIGHTS_DIR_FOUR_ "AA7", ["AA1", "AA7"], TEST_FLIGHTS_DIR_FOUR_ "AA1", ["AA1"], TEST_FLIGHTS_DIR_FOUR_CITIES) "AA2", ["AA4", "AA1", "AA2"], TEST_FLIGHTS_DI "AA3", ["AA1", "AA2", "AA3"], TEST_FLIGHTS_DI >>> calculate_trip_time("AA1", "AA4", ["AA1", "AA4"], TEST_FLIGHTS_DIR_FOUR_ 2.0 || || || >>>…
* with explanation
Using R lab please provide the code and answer:Find the data set “HELPrct”. Construct a side by side boxplots (can be horizontal orvertical) to compare the effect of homeless or housed on the average number of drinks perday for the patients. Add title “XXXXXX – Side by side Boxplot to compare the effect ofhomeless on average number ofDrinks”.Based on the side by side boxplot; write a simple conclusion.Find the data set “HELPrct”. Construct a side by side boxplots (can be horizontal orvertical) to compare the effect of different race groups on the average number of drinksper day for the patients. Add title “XXXXXX – Side by side Boxplot to compare the effectof race groups vs on averagenumber of Drinks”.Based on the side by side boxplot; write a simple conclusion.
Normalization Diagram. See the attached picture.
The StayWell maintenance team wants to minimize the number of planned visits to properties. The team wants to learn all the properties that have different planned service dates. You need to return all available data in the SERVICE_REQUEST table, where requests are made for the same property with a different NEXT_SERVICE_DATE. Return a list of all properties where service requests are made with different NEXT_SERVICE_DATE values.
Composite datatypes can only have multi row &multi column values in them Answers: Yes No
Explain in brief only about the given syntax diagrams.
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…
Explain why you sometimes have to reserve much more space for a numeric field than any of the initial stored values require.
Normalization this data base 1Nf 2Nf 3Nf