reshape

来源： https://stackoverflow.com/questions/63882983/pivot-reshape-data-in-r

来源： https://stackoverflow.com/questions/63882983/pivot-reshape-data-in-r

问题 Is it possible to remove a dimension using Reshape or any other function. I have the following network. import keras from keras.layers.merge import Concatenate from keras.models import Model from keras.layers import Input, Dense from keras.layers import Dropout from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten from keras.layers import Conv2D, MaxPooling2D, Reshape, ZeroPadding2D import numpy as np #Number_of_splits = ((input_width-win_dim)+1)/stride_dim splits = ((40-5)

问题 In order to get a numpy array from a list I make the following: np.array([i for i in range(0, 12)]) And get: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) Then I would like to make a (4,3) matrix from this array: np.array([i for i in range(0, 12)]).reshape(4, 3) and I get the following matrix: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) But if I know that I will have 3 * n elements in the initial list how can I reshape my numpy array, because the following code np.array([i for

问题 I want to trasnform the following data frame: ID 1234 type band category 0 A B C to: ID type band category 1234 A B C where ID is the index column 回答1: Try df.stack(0).reset_index().drop('level_0', axis=1) Output: ID Type band category 0 1234 A B C 来源： https://stackoverflow.com/questions/62580244/data-frame-index-transformation-in-pandas

问题 I have a DataFrame with shape: 720*720 2D. I wanna convert it to 1D dimension without changing its values. How can I do this using Pandas? 回答1: Use numpy.ravel with converted DataFrame to numpy array: np.random.seed(123) df = pd.DataFrame(np.random.randint(10, size=(3,3))) print (df) 0 1 2 0 2 2 6 1 1 3 9 2 6 1 0 out = df.values.ravel('F') #alternative for pandas 0.24+ #out = df.to_numpy().ravel('F') print (out) [2 1 6 2 3 1 6 9 0] s = pd.Series(df.values.ravel('F')) #alternative for pandas 0

问题 This question already has answers here : How to reshape data from long to wide format (11 answers) Closed 21 days ago . I want to reshape/ rearrange a dataset, that is stored as a data.frame with 2 columns: id (non-unique, i.e. can repeat over several rows) --> stored as character value --> stored as numeric value (range 1:3) Sample data: id <- as.character(1001:1003) val_list <- data.frame(sample(1:3, size=12, replace=TRUE)) have <- data.frame(cbind(rep(id, 4), val_list)) colnames(have) <- c

问题 I was trying to implement a kronecker product function. Below are three ideas that I have: def kron(arr1, arr2): """columnwise outer product, avoiding relocate elements. """ r1, c1 = arr1.shape r2, c2 = arr2.shape nrows, ncols = r1 * r2, c1 * c2 res = np.empty((nrows, ncols)) for idx1 in range(c1): for idx2 in range(c2): new_c = idx1 * c2 + idx2 temp = np.zeros((r2, r1)) temp_kron = scipy.linalg.blas.dger( alpha=1.0, x=arr2[:, idx2], y=arr1[:, idx1], incx=1, incy=1, a=temp) res[:, new_c] = np

问题 I was trying to implement a kronecker product function. Below are three ideas that I have: def kron(arr1, arr2): """columnwise outer product, avoiding relocate elements. """ r1, c1 = arr1.shape r2, c2 = arr2.shape nrows, ncols = r1 * r2, c1 * c2 res = np.empty((nrows, ncols)) for idx1 in range(c1): for idx2 in range(c2): new_c = idx1 * c2 + idx2 temp = np.zeros((r2, r1)) temp_kron = scipy.linalg.blas.dger( alpha=1.0, x=arr2[:, idx2], y=arr1[:, idx1], incx=1, incy=1, a=temp) res[:, new_c] = np

问题 I'm trying to get my code to output the percentage on top of each individual bar. Right now, the percentages shown below are on the wrong side. My code combines Labels 1 and 2 and Labels 3 and 4, and then outputs those numbers on the incorrect sides. Is there a function to label those bars correctly? I'm including my code, the data from the .csv file, and the current visualization. library(ggplot2) library(reshape) library(likert) library(dplyr) setwd("~/Desktop/") df <- read.csv("Likert_Test