Jan 9, 2019
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Cognos Analytics Training Course - The Crosstab (Advanced Concepts)
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Those who advocate crosstab have forgotten what happened in 1953.
Making Data Management Decisions
We need to make to manipulate our data properly for acquiring research results. In order to do so we need to manage our data properly. I have already limited my study to a 50>life expectancy and life expectancy <75. So far my code is :
Further more I have labeled all the missing data. The code:
The output shows us that the missing data has now been labeled as NaN which makes work easier because now I know what and how much set of data is missing. The output is as follows:
Since it is a very lengthy output, I have pasted the output text instead of snapshots.
original counts for political score 8 6 9 4 -8 1 0 1 7 2 42 -1 1 6 1 -7 4 -9 1 5 2 10 28 -2 2 -10 1 Name: polityscore, dtype: int64 modified counts for political score NaN 42 8 6 9 4 -8 1 0 1 7 2 -1 1 6 1 -7 4 -9 1 5 2 10 28 -2 2 -10 1 Name: polityscore, dtype: int64
original counts for female employment rate 37.29999924 1 58.29999924 1 41.70000076 2 45.29999924 1 50.40000153 1 .. 30.10000038 1 63.40000153 1 39.59999847 2 56.70000076 1 53.5 1 Name: femaleemployrate, Length: 64, dtype: int64 modified counts for female employment rate NaN 25 37.29999924 1 58.29999924 1 41.70000076 2 45.29999924 1 .. 30.10000038 1 63.40000153 1 39.59999847 2 56.70000076 1 53.5 1 Name: femaleemployrate, Length: 64, dtype: int64
original counts for employment rate 62.40000153 1 56.90000153 1 44.20000076 1 59.29999924 1 42.5 1 .. 66.90000153 1 47.09999847 1 58.40000153 1 57.20000076 1 64.30000305 1 Name: employrate, Length: 66, dtype: int64 modified counts for employment rate NaN 25 62.40000153 1 56.90000153 1 44.20000076 1 59.29999924 1 .. 66.90000153 1 47.09999847 1 58.40000153 1 57.20000076 1 64.30000305 1 Name: employrate, Length: 66, dtype: int64
original counts for income per person 6334.105194 1 24496.04826 1 27595.09135 1 8614.120219 1 239.5187494 1 .. 18982.26929 1 1810.230533 1 12729.4544 1 21943.3399 1 9106.327234 1 Name: incomeperperson, Length: 80, dtype: int64 modified counts for income per person NaN 17 6334.105194 1 24496.04826 1 27595.09135 1 8614.120219 1 .. 18982.26929 1 1810.230533 1 12729.4544 1 21943.3399 1 9106.327234 1 Name: incomeperperson, Length: 80, dtype: int64 original counts for life expectancy 79.499 1 81.907 1 83.394 1 48.673 1 79.311 1 .. 81.097 1 76.142 1 81.804 1 78.371 1 47.794 1 Name: lifeexpectancy, Length: 75, dtype: int64 modified counts for life expectancy NaN 22 79.499 1 81.907 1 83.394 1 48.673 1 .. 81.097 1 76.142 1 81.804 1 78.371 1 47.794 1 Name: lifeexpectancy, Length: 75, dtype: int64
As it can be seen the polityscore ranges from -10 to 10 out of which many values are not even taken by the variable. Now it is easier to analyse if I deal with a data that ranges from 1-10. So i have recoded the polityscore.
Before recoding I have converted the parameters (under study) of the copy variable to numeric (float type).
Input and output are as follows:
I found female employment rate to employment rate ratio to know if what fraction of women work in a country affects its life expectancy. Input code and output are as follows:
I have printed only first 25 entries. As we can see that it is difficult to draw conclusion by studying individual entries, it is better to group them. Hence I have divided feer in 7 almost equal groups:
here i have also cross-checked if feer was grouped properly
Summary:
It can concludes that feer was grouped properly. Also since all the 7 groups have the same count value, the rate of women working in a country does not affect its life expectancy.
Popularly known as a Text table or Pivot view. This is used as a short term for cross-tabulation. Cross Tab chart shows the data in textual form. It shows the actual value of any dimension in the data. It is also defined as a series of rows and columns that consist of headers and numeric values. …
Popularly known as a Text table or Pivot view. This is used as a short term for cross-tabulation. Cross Tab chart shows the data in textual form. It shows the actual value of any dimension in the data. It is also defined as a series of rows and columns that consist of headers and numeric values. …
.NET Reporting Tools: Creating Master-Detail Report Using Cross Bands
oh shit! pivot tables in @postgresql using #crosstab
http://www.craigkerstiens.com/2013/06/27/Pivoting-in-Postgres/
http://stackoverflow.com/questions/3002499/postgresql-crosstab-query
http://www.postgresql.org/message-id/4252864.NjuqYTl098@linux-12
http://www.cureffi.org/2013/03/19/automatically-creating-pivot-table-column-names-in-postgresql/
http://martininghamsoftdev.blogspot.com/2011/01/how-to-use-cte-to-create-crosstab-query.html
probably the most comprehensive answer: http://stackoverflow.com/questions/15506199/dynamic-alternative-to-pivot-with-case-and-group-by/15514334#15514334
the whole point of using pivots is to obviate the enormous time spent in your app code iterating through row data in order to pivot it properly in the view.
in other words, should you pivot your tabular data in sql or in app code? sql is faster, app code is slow.
after 2 days of researching whether to use pivots in sql, before sending it to the app, the general conclusion is that it may not make sense due to postgres column limit of 1600;
if you have financial tables that are on a daily basis, then you can easily hit the column limit; of course, you can solve that through horizontal pagination, but the point is, if you are going to use pagination, then you don’t really need the time-save from pivoting; you can pull selective number of rows at a time and iterate through that.