Write a Python script/code in the Jupyter Notebook to do the given problems. Using data wrangling and data visualization technique with storytelling, analyze the data and present different (i) data frames; and (ii) visuals using the dataset given. 1. Create the following data frames based on the format provided: Example: Vis = [“Name”, “Gender”, “Track”, “Math<70”]; hometown is constant as Visayas a. Filename: Instru = [“Name”, “GEAS”, “Electronics >70”]; where track is constant as Instrumentation and hometown Luzonb. Filename: Mindy = [ “Name”, “Track”, “Electronics”, “Average >=55”]; where hometown is constant as Mindanao and gender FemaleNameGenderTrackHometownMathElectronicsGEASCommunicationS1MaleInstrumentationLuzon58897578S2FemaleCommunicationMindanao52759052S3FemaleInstrumentationMindanao83747757S4MaleInstrumentationVisayas65589168S5MaleCommunicationLuzon59864388S6FemaleMicroelectronicsVisayas88458683S7FemaleInstrumentationLuzon66606048S8MaleInstrumentationLuzon49816453S9MaleInstrumentationLuzon50366342S10MaleMicroelectronicsMindanao80846144S11FemaleCommunicationVisayas48564867S12MaleCommunicationVisayas89678464S13FemaleMicroelectronicsLuzon88358343S14FemaleMicroelectronicsLuzon83778973S15FemaleMicroelectronicsMindanao69414086S16FemaleCommunicationLuzon71708781S17FemaleMicroelectronicsMindanao81797745S18MaleCommunicationVisayas81408152S19MaleMicroelectronicsLuzon79637971S20FemaleCommunicationMindanao59606285S21FemaleMicroelectronicsVisayas83516872S22FemaleCommunicationVisayas64398958S23MaleInstrumentationLuzon84707447S24FemaleMicroelectronicsVisayas85456041S25MaleCommunicationLuzon74919442S26FemaleInstrumentationVisayas71478362S27MaleMicroelectronicsVisayas70474086S28MaleCommunicationVisayas85538053S29MaleInstrumentationMindanao73487162S30MaleInstrumentationLuzon78815756

Name: Write a Python script/code in the Jupyter Notebook to do the given pro
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Description: Write a Python script/code in the Jupyter Notebook to do the given problems. Using data wrangling and data visualization technique with storytelling, analyze the data and present different (i) data frames; and (ii) visuals using the dataset given. 1

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Write a Python script/code in the Jupyter Notebook to do the given problems.

Using data wrangling and data visualization technique with storytelling, analyze the data and present different (i) data frames; and (ii) visuals using the dataset given.

1. Create the following data frames based on the format provided: Example: Vis = [“Name”, “Gender”, “Track”, “Math<70”]; hometown is constant as Visayas

a. Filename: Instru = [“Name”, “GEAS”, “Electronics >70”]; where track is constant as Instrumentation and hometown Luzon

b. Filename: Mindy = [ “Name”, “Track”, “Electronics”, “Average >=55”]; where hometown is constant as Mindanao and gender Female

Name	Gender	Track	Hometown	Math	Electronics	GEAS	Communication
S1	Male	Instrumentation	Luzon	58	89	75	78
S2	Female	Communication	Mindanao	52	75	90	52
S3	Female	Instrumentation	Mindanao	83	74	77	57
S4	Male	Instrumentation	Visayas	65	58	91	68
S5	Male	Communication	Luzon	59	86	43	88
S6	Female	Microelectronics	Visayas	88	45	86	83
S7	Female	Instrumentation	Luzon	66	60	60	48
S8	Male	Instrumentation	Luzon	49	81	64	53
S9	Male	Instrumentation	Luzon	50	36	63	42
S10	Male	Microelectronics	Mindanao	80	84	61	44
S11	Female	Communication	Visayas	48	56	48	67
S12	Male	Communication	Visayas	89	67	84	64
S13	Female	Microelectronics	Luzon	88	35	83	43
S14	Female	Microelectronics	Luzon	83	77	89	73
S15	Female	Microelectronics	Mindanao	69	41	40	86
S16	Female	Communication	Luzon	71	70	87	81
S17	Female	Microelectronics	Mindanao	81	79	77	45
S18	Male	Communication	Visayas	81	40	81	52
S19	Male	Microelectronics	Luzon	79	63	79	71
S20	Female	Communication	Mindanao	59	60	62	85
S21	Female	Microelectronics	Visayas	83	51	68	72
S22	Female	Communication	Visayas	64	39	89	58
S23	Male	Instrumentation	Luzon	84	70	74	47
S24	Female	Microelectronics	Visayas	85	45	60	41
S25	Male	Communication	Luzon	74	91	94	42
S26	Female	Instrumentation	Visayas	71	47	83	62
S27	Male	Microelectronics	Visayas	70	47	40	86
S28	Male	Communication	Visayas	85	53	80	53
S29	Male	Instrumentation	Mindanao	73	48	71	62
S30	Male	Instrumentation	Luzon	78	81	57	56

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2. Create a visualization that shows how the different features contributes to average grade. Does chosen track in college, gender, or hometown contributes to a higher average score?

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1. Create the following data frames based on the format provided: Example: Vis = [“Name”, “Gender”, “Track”, “Math<70”]; hometown is constant as Visayas

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