It’s easier like this: k.reshape(k.shape + (1,)) But if all you want is to add an empty dimension at the end, you should use numpy.newaxis: import numpy as np k = k[…, np.newaxis] or k = k[…, None] (See the documentation on slicing).
If no data are missing, you can use numpy.reshape: print (np.reshape(df.values,(2,5))) [[‘Andrew’ ‘School of Music’ ‘Music: Sound of the wind’ ‘Dr. Seuss’ ‘Dr.Sass’] [‘Michelle’ ‘School of Theatrics’ ‘Music: Voice’ ‘Dr. A’ ‘Dr. B’]] print (pd.DataFrame(np.reshape(df.values,(2,5)), columns=[‘Name’,’School’,’Music’,’Mentor1′,’Mentor2′])) Name School Music Mentor1 Mentor2 0 Andrew School of Music Music: Sound of the wind Dr. Seuss Dr.Sass 1 … Read more
Cross-tabs or pivot tables is the answer. From there you can SELECT FROM … INSERT INTO … or create a VIEW from the single SELECT. Something like: SELECT country, MAX( IF( key=’President’, value, NULL ) ) AS President, MAX( IF( key=’Currency’, value, NULL ) ) AS Currency, … FROM table GROUP BY country;
Pure numpy numpy.loadtxt(open(“test.csv”, “rb”), delimiter=”,”, skiprows=1) Check out the loadtxt documentation. You can also use python’s csv module: import csv import numpy reader = csv.reader(open(“test.csv”, “rb”), delimiter=”,”) x = list(reader) result = numpy.array(x).astype(“float”) You will have to convert it to your favorite numeric type. I guess you can write the whole thing in one line: … Read more
If your real data is as regular as your sample data we can be quite efficient by noticing that reshaping a matrix is really just changing its dim attribute. 1st on very small data library(data.table) library(microbenchmark) library(tidyr) matrix_spread <- function(df1, key, value){ unique_ids <- unique(df1[[key]]) mat <- matrix( df1[[value]], ncol= length(unique_ids),byrow = TRUE) df2 <- … Read more
there are a few ways; using .pivot: >>> origin.pivot(index=’label’, columns=”type”)[‘value’] type a b c label x 1 2 3 y 4 5 6 z 7 8 9 [3 rows x 3 columns] using pivot_table: >>> origin.pivot_table(values=”value”, index=’label’, columns=”type”) value type a b c label x 1 2 3 y 4 5 6 z 7 8 … Read more
You could create a new identifier column with unique value for every student and then use pivot_wider to case multiple columns to wide. library(dplyr) df %>% mutate(name = as.integer(factor(Student))) %>% tidyr::pivot_wider(names_from = name, values_from = c(Student, score)) # CoachID Student_1 Student_2 Student_3 score_1 score_2 score_3 # <int> <fct> <fct> <fct> <int> <int> <int> #1 1 … Read more