Building a DataFrame with column names in Python

Question

In trying to get familiar with DataFrame in Python I got stuck in the following step...

I try to create a dataframe from an array:

from numpy import *
import pandas as pd

dat = array([[0.3888888888888889, 0.3888888888888889, 0.3888888888888889, 0.436943311456892, 0.7905900031193156, 0.5020468092219706, 0.8389717734280283, 0.7604923090797432, 0.8206054422776056, '0'],
[0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.4457200178477334, 0.8114779465247448, 0.506899600792241, 0.8368566485573798, 0.760617288778523, 0.8195489478905984, '1'],
[0.2777777777777778, 0.2777777777777778, 0.05555555555555555, 0.4426231291814084, 0.7883413226205706, 0.5037172133121759, 0.8370362549229062, 0.7599752704033258, 0.8184218722901648, '2'],
[0.1111111111111111, 0.1111111111111111, 0.16666666666666666, 0.4651807845446571, 0.7983379003654792, 0.5250604537887904, 0.8463875215362144, 0.7533582308429306, 0.8241548325954007, '3'],
[0.5000000000000001, 0.5000000000000001, 0.3333333333333333, 0.4457200178477334, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '4'],
[0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.44943322185630036, 0.7843622888520198, 0.5055757644148106, 0.8351253941103399, 0.7604171267769607, 0.8185442945328569, '5'],
[0.3888888888888889, 0.3888888888888889, 0.3333333333333333, 0.4424914587425397, 0.7877430312713435, 0.5029950110274568, 0.836692391332608, 0.760611529525946, 0.8198150075184326, '6'],
[0.3333333333333333, 0.05555555555555555, 0.7777777777777778, 0.4389415113841421, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '7'],
[0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.42770705188736874, 0.7976039510596705, 0.5057230657076256, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '8'],
[0.2222222222222222, 0.2777777777777778, 0.5000000000000001, 0.43182322765312314, 0.7971732873351607, 0.5072390458086798, 0.84541364942531, 0.7613416598875292, 0.8239037851005895, '9']])

dat = pd.DataFrame([dat], columns = ["Var 1", "Var 2", "Var 3", "Var 4", "Var 5", "Var 6", "Var 7", "Var 8", "Var 9", "Var 10"])

... and I get the following error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-68-e8aa575cd3c7> in <module>()
----> 1 dat = pd.DataFrame([dat], columns = ["Var 1", "Var 2", "Var 3", "Var 4", "Var 5", "Var 6", "Var 7", "Var 8", "Var 9", "Var 10"])

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    277                 else:
    278                     mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 279                                              copy=copy)
    280             else:
    281                 mgr = self._init_dict({}, index, columns, dtype=dtype)

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in _init_ndarray(self, values, index, columns, dtype, copy)
    410         # by definition an array here
    411         # the dtypes will be coerced to a single dtype
--> 412         values = _prep_ndarray(values, copy=copy)
    413 
    414         if dtype is not None:

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in _prep_ndarray(values, copy)
   5323         values = values.reshape((values.shape[0], 1))
   5324     elif values.ndim != 2:
-> 5325         raise ValueError('Must pass 2-d input')
   5326 
   5327     return values

ValueError: Must pass 2-d input

Python is still very unfamiliar to me, but I was trying to follow this post.

Answer 1

You're making the input into DataFrame as a list containing one element or a list in one dimension. You should be passing the actual array. Therefore, remove the brackets surrounding dat:

In [9]: dat = pd.DataFrame(dat, columns = ["Var %d" % (i + 1) for i in range(10)])

In [10]: dat
Out[10]:
            Var 1            Var 2            Var 3           Var 4  \
0  0.388888888889   0.388888888889   0.388888888889  0.436943311457
1  0.388888888889   0.388888888889   0.222222222222  0.445720017848
2  0.277777777778   0.277777777778  0.0555555555556  0.442623129181
3  0.111111111111   0.111111111111   0.166666666667  0.465180784545
4             0.5              0.5   0.333333333333  0.445720017848
5  0.388888888889   0.388888888889   0.222222222222  0.449433221856
6  0.388888888889   0.388888888889   0.333333333333  0.442491458743
7  0.333333333333  0.0555555555556   0.777777777778  0.438941511384
8  0.444444444444   0.444444444444   0.444444444444  0.427707051887
9  0.222222222222   0.277777777778              0.5  0.431823227653

            Var 5           Var 6           Var 7           Var 8  \
0  0.790590003119  0.502046809222  0.838971773428   0.76049230908
1  0.811477946525  0.506899600792  0.836856648557  0.760617288779
2  0.788341322621  0.503717213312  0.837036254923  0.759975270403
3  0.798337900365  0.525060453789  0.846387521536  0.753358230843
4  0.787804059391  0.506899600792  0.836856648557  0.760501605832
5  0.784362288852  0.505575764415   0.83512539411  0.760417126777
6  0.787743031271  0.502995011027  0.836692391333  0.760611529526
7  0.787804059391  0.506899600792  0.836856648557  0.760501605832
8   0.79760395106  0.505723065708  0.836856648557  0.760501605832
9  0.797173287335  0.507239045809  0.845413649425  0.761341659888

            Var 9 Var 10
0  0.820605442278      0
1  0.819548947891      1
2   0.81842187229      2
3  0.824154832595      3
4  0.819548947891      4
5  0.818544294533      5
6  0.819815007518      6
7  0.819548947891      7
8  0.819548947891      8
9  0.823903785101      9

Don't mind the list comprehension for the columns field. I just didn't want to type out all of those Vars :).