categorical-data

问题 I am trying to completely understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear: Categorical feature and data example: Color: red, white, black Why categorical: red < white < black is logically incorrect Ordinal feature and data example: Condition: old, renovated, new Why ordinal: old < renovated < new is logically correct Categorical-to-numeric and ordinal-to-numeric encoding methods: One-Hot encoding for categorical data Arbitrary

问题 I am trying to completely understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear: Categorical feature and data example: Color: red, white, black Why categorical: red < white < black is logically incorrect Ordinal feature and data example: Condition: old, renovated, new Why ordinal: old < renovated < new is logically correct Categorical-to-numeric and ordinal-to-numeric encoding methods: One-Hot encoding for categorical data Arbitrary

问题 I am trying to completely understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear: Categorical feature and data example: Color: red, white, black Why categorical: red < white < black is logically incorrect Ordinal feature and data example: Condition: old, renovated, new Why ordinal: old < renovated < new is logically correct Categorical-to-numeric and ordinal-to-numeric encoding methods: One-Hot encoding for categorical data Arbitrary

问题 I have the following pandas dataframe: import pandas as pd df = pd.read_csv("filename.csv") df A B C D E 0 a 0.469112 -0.282863 -1.509059 cat 1 c -1.135632 1.212112 -0.173215 dog 2 e 0.119209 -1.044236 -0.861849 dog 3 f -2.104569 -0.494929 1.071804 bird 4 g -2.224569 -0.724929 2.234213 elephant ... I would like to create more columns based on the identity of categorical values in column E such that the dataframe looks like this: df A B C D cat dog bird elephant .... 0 a 0.469112 -0.282863 -1

问题 I am working on a data science project in which I have to compute the euclidian distance between every pair of observations in a dataset. Since I am working with very large datasets, I have to use an efficient implementation of pairwise distances computation (both in terms of memory usage and computation time). One solution is to use the pdist function from Scipy, which returns the result in a 1D array, without duplicate instances. However, this function is not able to deal with categorical

问题 I am working on a data science project in which I have to compute the euclidian distance between every pair of observations in a dataset. Since I am working with very large datasets, I have to use an efficient implementation of pairwise distances computation (both in terms of memory usage and computation time). One solution is to use the pdist function from Scipy, which returns the result in a 1D array, without duplicate instances. However, this function is not able to deal with categorical

问题 I have to plot a line chart with x-axis about time. The x-axis is like ["00:00", "00:05", "00:10:, ... , "23:55"], making it not numeric but categorical. However, I may not have a full list of data on y-axis. eg. there is data only from "00"00" to "09:00". The data must start from "00:00". The chart I made can only show the range which has a y value. (eg. "00"00 to "09:00"), but I want to have a chart with full x-axis even though some parts of the graph is empty. I read the documentation that

问题 I have to plot a line chart with x-axis about time. The x-axis is like ["00:00", "00:05", "00:10:, ... , "23:55"], making it not numeric but categorical. However, I may not have a full list of data on y-axis. eg. there is data only from "00"00" to "09:00". The data must start from "00:00". The chart I made can only show the range which has a y value. (eg. "00"00 to "09:00"), but I want to have a chart with full x-axis even though some parts of the graph is empty. I read the documentation that

问题 I want to do linear regression with the lm function. My dependent variable is a factor called AccountStatus : 1:0 days in arrears, 2:30-60 days in arrears, 3:60-90 days in arrears and 4:90+ days in arrears. (4) As independent variable I have several numeric variables: Loan to value , debt to income and interest rate . Is it possible to do a linear regression with these variables? I looked on the internet and found something about dummy's, but those were all for the independent variable. This

问题 I am wondering how I can keep track of the original columns of a dataset once I perform data preprocessing on it? In the below code df_columns would tell me that column 0 in df_array is A , column 1 is B and so forth... However when once I encode categorical column B df_columns is no longer valid for keeping track of df_dummies import pandas as pd import numpy as np animal = ['dog','cat','horse'] df = pd.DataFrame({'A': np.random.rand(9), 'B': [animal[np.random.randint(3)] for i in range(9)],