How to convert Numpy array to Panda DataFrame

You could flatten the numpy array:

import numpy as npimport pandas as pddata = [[400.31865662],        [401.18514808],        [404.84015554],        [405.14682194],        [405.67735105],        [273.90969447],        [274.0894528]]arr = np.array(data)df = pd.DataFrame(data=arr.flatten())print(df)

Output

            00  400.3186571  401.1851482  404.8401563  405.1468224  405.6773515  273.9096946  274.089453

Since I assume the many visitors of this post aren't here for OP's specific and un-reproducible issue, here's a general answer:

df = pd.DataFrame(array)

The strength of pandas is to be nice for the eye (like Excel), so it's important to use column names.

import numpy as npimport pandas as pdarray = np.random.rand(5, 5)

array([[0.723, 0.177, 0.659, 0.573, 0.476],       [0.77 , 0.311, 0.533, 0.415, 0.552],       [0.349, 0.768, 0.859, 0.273, 0.425],       [0.367, 0.601, 0.875, 0.109, 0.398],       [0.452, 0.836, 0.31 , 0.727, 0.303]])

columns = [f'col_{num}' for num in range(5)]index = [f'index_{num}' for num in range(5)]

Here's where the magic happens:

df = pd.DataFrame(array, columns=columns, index=index)

            col_0     col_1     col_2     col_3     col_4index_0  0.722791  0.177427  0.659204  0.572826  0.476485index_1  0.770118  0.311444  0.532899  0.415371  0.551828index_2  0.348923  0.768362  0.858841  0.273221  0.424684index_3  0.366940  0.600784  0.875214  0.108818  0.397671index_4  0.451682  0.836315  0.310480  0.727409  0.302597

There is another way, which isn't mentioned in the other answers. If you have a NumPy array which is essentially a row vector (or column vector) i.e. shape like (n, ) , then you could do the following :

# sample arrayx = np.zeros((20))# empty dataframedf = pd.DataFrame()# add the array to df as a columndf['column_name'] = x

This way you can add multiple arrays as separate columns.