如何使用 python 数据表库从值矩阵（列表列表）和特征列表创建数据表 dataframe

Question

Given a list of n features:

lf = ['f1','f2',...,'fn']

Given a list of m lists, each nested list contain n value (a matrix of m rows and n columns):

matrix =  
[
[r0_v1, r0_v2, ..., r0_vn]  
[r1_v1, r1_v2, ..., r1_vn]  
.
.
.
[rm_v1, rm_v2, ..., rm_vn]  
]

What is the correct way to create a datatable datafarme using python datatable library ?

I tried something similar to pandas dataframe in the following source code:

import pandas as pd
import datatable as dt

# pandas create dataframe
pd_df = pd.DataFrame(matrix,columns=lf) # work fine

# datatable create dataframe
dt_df = dt.Frame(matrix,names=lf) # get error, the rows are considered as columns

But i get an error ValueError: The names argument contains n elements, which is more than the number of columns being created (m)
Which means that the rows are considered as columns.

Thanks for your help.

Answer 1

To create datatable datafarme from matrix and list of features use dt.Frame(matrix_values, names=list_features)
Use np.array to convert from list of lists to 2d array: matrix = np.array(matrix)

import datatable as dt
import numpy as np

lf = ['f1','f2','f3','f4','f5']

matrix = [
    [0,0,0,0,0],
    [1,1,1,1,1],
    [2,2,2,2,2],
]

matrix = np.array(matrix)

dt_df = dt.Frame(matrix,names=lf)

print(dt_df)