categorical-data

I have a dataframe that includes columns with multiple attributes separated by commas: df = pd.DataFrame({'id': [1,2,3], 'labels' : ["a,b,c", "c,a", "d,a,b"]}) id labels 0 1 a,b,c 1 2 c,a 2 3 d,a,b (I know this isn't an ideal situation, but the data originates from an external source.) I want to turn the multi-attribute columns into multiple columns, one for each label, so that I can treat them as categorical variables. Desired output: id a b c d 0 1 True True True False 1 2 True False True False 2 3 True True False True I can get the set of all possible attributes ( [a,b,c,d] ) fairly easily,

Closed . This question needs details or clarity . It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post . Closed 5 years ago . I would like to make a boxplot that shows how time spent doing a behaviour(Alert) is affected by two variables (Period= Morning/Afternoon and Visitor Level= High/Low). Alert ~ Period + Vis.Level 'Alert' is a set of 12 numbers that show the amount of time spent awake with the other two as the significant categorical variables. I have looked at other examples but none seem to fit this type of

What would be the SQL query script if I want to get the total items and total revenue for each manager including his team? Suppose I have this table items_revenue with columns: | id |is_manager|manager_id| name |no_of_items| revenue | | 1 | 1 | 0 | Manager1 | 621 | 833 | | 2 | 1 | 0 | Manager2 | 458 | 627 | | 3 | 1 | 0 | Manager3 | 872 | 1027 | ... | 8 | 0 | 1 | Member1 | 1258 | 1582 | | 9 | 0 | 2 | Member2 | 5340 | 8827 | | 10 | 0 | 3 | Member3 | 3259 | 5124 | All the managers and their respective members are in the above view table. Member1 is under Manager1, Member2 is under Manager2, and

Hej guys, I'm trying to project multi labeled categorical data into a dense space using embeddings. Here's an toy example. Let's say I have four categories and want to project them into a 2D space. Furthermore I got two instances, the first one belonging to category 0 and the second one to category 1. The code will look something like this: sess = tf.InteractiveSession() embeddings = tf.Variable(tf.random_uniform([4, 2], -1.0, 1.0)) sess.run(tf.global_variables_initializer()) y = tf.nn.embedding_lookup(embeddings, [0,1]) y.eval() and return something like this: array([[ 0.93999457, -0.83051205

I have the following data frame: import pandas as pd # Create DataFrame df = pd.DataFrame( {'id':[2967, 5335, 13950, 6141, 6169],\ 'Player': ['Cedric Hunter', 'Maurice Baker' ,\ 'Ratko Varda' ,'Ryan Bowen' ,'Adrian Caldwell'],\ 'Year': [1991 ,2004 ,2001 ,2009 ,1997],\ 'Age': [27 ,25 ,22 ,34 ,31],\ 'Tm':['CHH' ,'VAN' ,'TOT' ,'OKC' ,'DAL'],\ 'G':[6 ,7 ,60 ,52 ,81]}) df.set_index('Player', inplace=True) It shows: Out[128]: Age G Tm Year id Player Cedric Hunter 27 6 CHH 1991 2967 Maurice Baker 25 7 VAN 2004 5335 Ratko Varda 22 60 TOT 2001 13950 Ryan Bowen 34 52 OKC 2009 6141 Adrian Caldwell 31 81

I am facing a difficulty while plotting a parallel coordinates plot using the ggparcoord from the GGally package. As there are two categorical variables, what I want to show in the visualisation is like the image below. I've found that in ggparcoord , groupColumn is only allowed to a single variable to group (colour) by, and surely I can use showPoints to mark the values on the axes, but i also need to vary the shape of these markers according to the categorical variables. Is there other package that can help me to realise my idea? Any response will be appreciated! Thanks! It's not that