How can I solve with Python Numpy? The tables are given. Only the instructions should be done in the exercise. Question 1. Let's clean up our data. First, filter out the HOM, HYP, and NEP rows from the table for the reasons described in the above paragraph. Next, jointogether the abbreviations table and our causes of death table so that we have a more detailed discription of each disease in each row. Lastly, drop the columnwhich contains the acronym of the disease, and rename the column with the full description 'Cause of Death'. Assign the variable cleaned_causes to theresulting table.n [147]: causes.drop('HOM','HYP', 'NEP')abbreviations.drop('HOM', 'HYP','NEP')cleaned causes - (causes, 'Cause of Death', abbreviations)abbreviations.relabeled('Cause of Death (Full Description)', 'Cause of Death')cleaned causes=causes.drop(Cause of Death (Full Description)cleaned_causesFile "", line 6cleaned causes=causes.drop(Cause of Death (Full Description)SyntaxError: invalid syntaxWe're going to examine the changes in causes of death over time. To make a plot of those numbers, we need to have a table with one row per year, and theinformation about all the causes of death for each year.Question 2. Create a table with one row for each year and a column for each kind of death, where each cell contains the number of deaths by that cause inthat year. Call the table cleaned_causes_by_year.In [ ]: cleaned_causes_by_year = ...cleaned causes_by_year.show()Question 3. Make a plot of all the causes of death by year, using your cleaned-up version of the dataset. There should be a single plot with one line per causeof death.Hint: Use the Table method plot. If you pass only a single argument, a line will be made for each of the other columns.In [ ]:...After seeing the plot above, we would now like to examine the distributions of diseases over the years using percentages. Below, we have assigneddistributions to a table with all of the same columns, but the raw counts in the cells are replaced by the percentage of the the total number of deaths by aparticular disease that happened in that specific year.Try to understand the code below.In [8]: def percents (array_x):return np.round ( (array_x/sum(array_x))*100, 2)labels - cleaned_causes_by_year.labelsdistributions - Table().with_columns(labels[0], cleaned_causes_by_year.column(@),labels[1], percents (cleaned_causes_by_year.column(1)),labels[2], percents(cleaned_causes_by_year.column(2)),labels[3], percents(cleaned_causes_by_year.column(3)),labels[4], percents(cleaned_causes_by_year.column(4)),labels[5], percents (cleaned_causes_by_year.column(5)),labels[6], percents(cleaned_causes_by_year.column(6)),labels[7], percents(cleaned_causes_by_year.column(7)),labels[8], percents(cleaned_causes_by_year.column(8)),labels[9], percents(cleaned_causes_by_year.column(9)),labels[10], percents (cleaned_causes_by_year.column(1e)),labels[11], percents (cleaned_causes_by_year.column(11)))distributions.show()Moma Out[28]:Year ZIP Code Cause of Death CountLocation199990002SUI1(33.94969, -118.246213)199990005HÔM1(34.058508, -118.301197)199990006ALZ1 (34.049323, -118.291687)199990007ALZ1 (34.029442, -118.287095)199990009DIA1(33.9452, -118.3832)199990009LIV1(33.9452, -118.3832)199990009OTH1(33.9452, -118.3832)199990010STK1 (34.060633, -118.302664)199990010CLD1 (34.060633, -118.302664)199990010DIA1 (34.060633, -118.302664)(320142 rows omitted)The causes of death in the data are abbreviated. We've provided a table called abbreviations.csv to translate theIn [29]: abbreviations = Table.read_table('abbreviations.csv')abbreviations.show()Cause of DeathCause of Death (Full Description)AIDAcquired Immune Deficiency Syndrome (AIDS)ALZAlzheimer's DiseaseCANMalignant Neoplasms (Cancers)CLDChronic Lower Respiratory Disease (CLRD)CPDChronic Obstructive Pulmonary Disease (COPD)DIADiabetes MellitusHIVHuman Immunodeficiency Virus Disease (HIVD)HOMHomicideHTDDiseases of the HeartHYP Essential Hypertension and Hypertensive Renal DiseaseINJUnintentional InjuriesLIVChronic Liver Disease and CirrhosisNEPKidney Disease (Nephritis)OTHAll Other CausesPNFPneumonia and InfluenzaSTKCerebrovascular Disease (Stroke)SUIIntentional Self Harm (Suicide)

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Question 1. Let's clean up our data. First, filter out the HOM, HYP, and NEP rows from the table for the reasons described in the above paragraph. Next, join together the abbreviations table and our causes of death table so that we have a more detailed discription of each disease in each row. Lastly, drop the columr which contains the acronym of the disease, and rename the column with the full description 'Cause of Death'. Assign the variable cleaned causes to the resulting table.