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GSS Dataset Tutorial

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We are analyzing the data-set Premarital Sex for today's class.  This is to visualize the opinion on Premarital Sex from 1989 to 2008.  We plot the line graphs for the three groups based on Average Sex per year:  1) Not wrong at all 2) Sometimes wrong 3) Almost always wrong and Always wrong We have an interesting result already. Over the past 20 years, those opposed to premarital sex have been having more and more sex on average each year, while those who think it is not wrong at all have been having less and less.  Now, we plot the Respondent's opinion on pre-martial sex based on Average Age of Respondent. Since younger people have sex much more frequently than older people (create a new worksheet plotting SexPerYear against AGE if you want to see for yourself), we can see that the puzzling trends in sexual activity are likely linked to the changing average age of these difference groups. The answer appe...
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Playing with Tableau

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Getting Started, Loading Data and Creating Bubble Charts  We have used the Sales dataset for this exercise. The data has 3 categories- 1) Furniture 2) Office Supplies 3) Technology All these categories have various sub-categories. We visualize the profits across the sub-categories. We color code the circles as per the categories.  The size of the circle indicates the amount of profit for the particular sub-category. We can see that the Technology category has the highest profits while Furniture has the lowest profits with losses in Bookcases and Tables sub-categories. In the next exercise, we will be analyzing region wise trends in Profit and Discount from our Sales data. Since we are interested in geographical comparisons, our natural choice will be a map. As seen above, the maps illustrate profit and discount ranges by region. We can determine that losses (red) are incurred on the coasts with profits (green) in the midwest and north. W...
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Visualizing with Tableau-1

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We have tried to visualize the USDA_activity_dataset.xls for this exercise. In the first view, we create a map for each state showing obesity rates at the county level. Now we have a map showing a dot for every county which has a record. There are 3 unknown locations which have been removed. In the second sheet, we have filtered by State:Alabama and color coded the obesity rates. The red circles are the ones where adult obesity rate is high. The obesity rate is highest for County:Greene with rate 43.50 The obesity rate is lowest for County:Baldwin with rate 24.50 In the third sheet, we have done scatter plots to see the relationship between obesity and health-related behaviours at county level. We can see that there is a linear increase int he obesity rates w.r.t Smoking, Eating less fruits and vegetables and less exercise. Thus, there are many attributes contributing for the adult obesity rates.
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Working with Pandas

Working with Pandas In [1]: import numpy as np import pandas as pd s = pd . Series ([ 1 , 3 , 5 , np . nan , 6 , 8 ]) print ( s ) 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 In [2]: dates = pd . date_range ( '20130101' , periods = 6 ) print ( dates ) DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [5]: df = pd . DataFrame ( np . random . randn ( 6 , 4 ), index = dates , columns = ( 'A' , 'B' , 'C' , 'D' )) #using series print ( df ) A B C D 2013-01-01 0.375465 1.208336 0.402344 0.620279 2013-01-02 -1.013392 0.697856 -...
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