Creating a Pandas DataFrame from a Numpy array: How do I specify the index column and column headers?

I have a Numpy array consisting of a list of lists, representing a two-dimensional array with row labels and column names as shown below:

data = array([['','Col1','Col2'],['Row1',1,2],['Row2',3,4]])

I'd like the resulting DataFrame to have Row1 and Row2 as index values, and Col1, Col2 as header values

I can specify the index as follows:

df = pd.DataFrame(data,index=data[:,0]),

however I am unsure how to best assign column headers.

Solution 1:

You need to specify data, index and columns to DataFrame constructor, as in:

>>> pd.DataFrame(data=data[1:,1:],    # values
...              index=data[1:,0],    # 1st column as index
...              columns=data[0,1:])  # 1st row as the column names

edit: as in the @joris comment, you may need to change above to np.int_(data[1:,1:]) to have correct data type.

Solution 2:

Here is an easy to understand solution

import numpy as np
import pandas as pd

# Creating a 2 dimensional numpy array
>>> data = np.array([[5.8, 2.8], [6.0, 2.2]])
>>> print(data)
>>> data
array([[5.8, 2.8],
       [6. , 2.2]])

# Creating pandas dataframe from numpy array
>>> dataset = pd.DataFrame({'Column1': data[:, 0], 'Column2': data[:, 1]})
>>> print(dataset)
   Column1  Column2
0      5.8      2.8
1      6.0      2.2

Solution 3:

I agree with Joris; it seems like you should be doing this differently, like with numpy record arrays. Modifying "option 2" from this great answer, you could do it like this:

import pandas
import numpy

dtype = [('Col1','int32'), ('Col2','float32'), ('Col3','float32')]
values = numpy.zeros(20, dtype=dtype)
index = ['Row'+str(i) for i in range(1, len(values)+1)]

df = pandas.DataFrame(values, index=index)