data-science

问题 I have a dataframe that looks like this: from to datetime other ------------------------------------------------- 11 1 2016-11-06 22:00:00 - 11 1 2016-11-06 20:00:00 - 11 1 2016-11-06 15:45:00 - 11 12 2016-11-06 15:00:00 - 11 1 2016-11-06 12:00:00 - 11 18 2016-11-05 10:00:00 - 11 12 2016-11-05 10:00:00 - 12 1 2016-10-05 10:00:59 - 12 3 2016-09-06 10:00:34 - I want to groupby "from" and then "to" columns and then sort the "datetime" in descending order and then finally want to calculate the

问题 The data is in the following link : http://www.fdic.gov/bank/individual/failed/banklist.html I want only the banks which closed in 2017. How can I do it in Pandas ? failed_banks= pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html') failed_banks[0] What should I do after these lines of code to extract the desired result? 回答1: Ideally you would use # assuming pandas successfully parsed this column as datetime object # and pandas version >= 0.16 failed_banks= pd.read_html(

问题 When running rpart , I am getting an error message saying : Something is wrong; all the Accuracy metric values are missing: The dataset can be found here and has no NAs, can someone help? > rf.5.cv.1 # Random Forest # 891 samples # 6 predictor # 2 classes: '0', '1' # No pre-processing # Resampling: Cross-Validated (10 fold, repeated 10 times) # Summary of sample sizes: 802, 802, 803, 801, 801, 802, ... # Resampling results across tuning parameters: # mtry Accuracy Kappa # 2 0.8383655 0

问题 I have a 2-column DataFrame, column-1 corresponds to customer, column-2 corresponds to the city this customer has visited. The DataFrame looks like the following: print(df) customer visited_city 0 John London 1 Mary Melbourne 2 Steve Paris 3 John New_York 4 Peter New_York 5 Mary London 6 John Melbourne 7 John New_York I would like to convert the above DataFrame into a row-vector format , such that each row represents a unique user with the row vector indicating the cities visited. print(wide

问题 I have a 2-column DataFrame, column-1 corresponds to customer, column-2 corresponds to the city this customer has visited. The DataFrame looks like the following: print(df) customer visited_city 0 John London 1 Mary Melbourne 2 Steve Paris 3 John New_York 4 Peter New_York 5 Mary London 6 John Melbourne 7 John New_York I would like to convert the above DataFrame into a row-vector format , such that each row represents a unique user with the row vector indicating the cities visited. print(wide

问题 I am currently performing multi class SVM with linear kernel using python's scikit library. The sample training data and testing data are as given below: Model data: x = [[20,32,45,33,32,44,0],[23,32,45,12,32,66,11],[16,32,45,12,32,44,23],[120,2,55,62,82,14,81],[30,222,115,12,42,64,91],[220,12,55,222,82,14,181],[30,222,315,12,222,64,111]] y = [0,0,0,1,1,2,2] I want to plot the decision boundary and visualize the datasets. Can someone please help to plot this type of data. The data given above

问题 Using the iris dataset I'm trying to calculate a z score for each of the variables. I have the data in tidy format, by performing the following: library(reshape2) library(dplyr) test <- iris test <- melt(iris,id.vars = 'Species') That gives me the following: Species variable value 1 setosa Sepal.Length 5.1 2 setosa Sepal.Length 4.9 3 setosa Sepal.Length 4.7 4 setosa Sepal.Length 4.6 5 setosa Sepal.Length 5.0 6 setosa Sepal.Length 5.4 But when I try to create a z-score column for each group (e

问题 Background: I am using R along with some packages to pull JSON data from a ticketing system. I'm pulling all the users and want to build a reporting structure. I have a data set that contains employees and their managers. The columns are named as such ("Employee" and "Manager"). I am trying to build a tree of a reporting structure that goes up to the root. We are in an IT organization, but I am pulling all employee data, so this would look something like: Company -> Business Unit -> Executive

问题 Given a pandas crosstab, how do you convert that into a stacked dataframe? Assume you have a stacked dataframe. First we convert it into a crosstab. Now I would like to revert back to the original stacked dataframe. I searched a problem statement that addresses this requirement, but could not find any that hits bang on. In case I have missed any, please leave a note to it in the comment section. I would like to document the best practice here. So, thank you for your support. I know that

问题 I'm trying to create a model matrix in sparklyr. There is a function ml_create_dummy_variables() for creating dummy variables for one categorical variable at a time. As far as I can tell there is no model.matrix() equivalent for creating a model matrix in one step. It's easy to use ml_create_dummy_variables() but I don't understand why the new dummy variables aren't stored in the Spark dataframe. Consider this example: ###create dummy data to figure out how model matrix formulas work in