Python convert large numpy array to pandas dataframe

Question

I have a chunk of code that I received that only works with pandas dataframes as input. I currently have a pretty large numpy array. I need to convert this into a pandas dataframe.

The Dataframe will be 288 rows (289 counting the columns names) and 1801 columns. I have an array of size 1801 that will be all of the column names in the dataframe. Then I have an array of size (288) which will fill the first column. Then I have an array of shape (1800, 288) that will fill columns 2-1801. is there an easy way to turn this into a dataframe without individually defining all 1801 columns?

I know I could define columns like column2=array[0,:], column3=array[1,:] but that will be alot of work for 1801 columns.

Answer 1

You can pass a numpy array directly to the DataFrame constructor:

In [11]: a = np.random.rand(3, 5)

In [12]: a
Out[12]:
array([[ 0.46154984,  0.08813473,  0.57746049,  0.42924157,  0.34689139],
       [ 0.29731858,  0.83300176,  0.15884604,  0.44753895,  0.56840054],
       [ 0.02479636,  0.76544594,  0.24388046,  0.06679485,  0.94890838]])

In [13]: pd.DataFrame(a)
Out[13]:
          0         1         2         3         4
0  0.461550  0.088135  0.577460  0.429242  0.346891
1  0.297319  0.833002  0.158846  0.447539  0.568401
2  0.024796  0.765446  0.243880  0.066795  0.948908

In [14]: pd.DataFrame(a.T)
Out[14]:
          0         1         2
0  0.461550  0.297319  0.024796
1  0.088135  0.833002  0.765446
2  0.577460  0.158846  0.243880
3  0.429242  0.447539  0.066795
4  0.346891  0.568401  0.948908