You can create a limitless number of to-do lists with xList, to help you organize separate tasks. xList distills GTD principles into a clean and simple user interface which helps you focus on the completion of your next task. With GTD, your overwhelming to-do list becomes a manageable set of tasks. XList is compliant to GTD method Getting Things Done. This feature is helpful when searching or in categorizing information such as due dates, priority levels, and contexts. xList has a built in tagging feature, which allows you to tag additional information to your tasks. Organize your tasks with xList and you will be left with more time that can be spent on completing your action items. XList records your tasks with the flexibility to make your to-do list as simple or as detailed as you desire. It allows tagging, prioritizing, and filtering to ensure that you do not miss anything. import matplotlib.pyplot as plt inertias = ks = range(1,11) for k in ks: inertias.append(KMeans(n_clusters=k).fit(data).inertia_) plt.plot(ks, inertias, '-o') plt.xticks(ks) plt.XList is a to-do list manager with a effortless interface. One way to find the number of clusters in the data is with an inertia plot, and to look for the ‘elbow’. We want three, but we also want them to contain enough observations that it’s worthwhile creating a specific T-shirt size for them. The next step is to decide how many clusters are in the data. The first step was to split the data into subsets, remove rows with 0 values (59 of 1584 in this case), then converted to a numpy matrix for clustering: import numpy as np t_shirt_columns = young = df = 83)].loc old_1 = df >= 156)].loc old_2 = df >= 156)].loc drop_list = for i in range(len(middle.index)): if 0 in middle.ix.values: drop_list.append(i) middle = middle.drop(middle.index) data = middle.as_matrix() shoulder_data = df] shoulder_data = shoulder_data.apply( lambda x: x//12) y_1 = shoulder_data = 1].groupby('YEAR').mean() y_2 = shoulder_data = 2].groupby('YEAR').mean() x = list(shoulder_oupby('YEAR').mean().index) With this information, it is estimated that Sex 1 is Male, and Sex 2 is Female. A plot of the average sitting heights per age for both Sex 1 and Sex 2 suggests that there isn’t much difference between Males and Females until around age 15, when it would be assumed that Males grow slightly taller. There is also scope for overlap of Small, Medium and Large across these boundaries, that is, the Large Shirt for 7–12 year olds might be a similar size to the small for 13 to 20 year olds.Īlso of interest is the relationship between Sex and size. As such, there are three age intervals where shirt sizes need to be determined 0 to 6, 7 to 12, 13 to 20. 6–7 years old and 12–13 are these two age jumps where there is a visible increase growth rates. This shows a roughly linear growth rate, with two ages where there is a dramatic increase in height. demographic_attributes = df = df.drop(demographic_attributes, axis = 'columns') Demographic data was deemed to not be useful in this situation, as the aim was to design T-shirt shirts, so 22 demographic variables were also removed. import pandas as pd df = pd.read_csv('data.csv', index_col=0) remove_cols = for i in df.columns: if 3900 - df.loc.astype(bool).sum() > 2000: remove_cols.append(i) df = df.drop(remove_cols, axis = 'columns')ħ6 variables were removed using this criteria.Īs stated above, the dataset contains both anthropometric and demographic data. As such, any variables which contained more than 2,000 Null values (zero in this case) were removed. The first step was to examine the data, and it was seen that many objects contained empty values.
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