Python: create a pandas data frame from a list

I am using the following code to create a data frame from a list:

test_list = ['a','b','c','d']
df_test = pd.DataFrame.from_records(test_list, columns=['my_letters'])
df_test

The above code works fine. Then I tried the same approach for another list:

import pandas as pd
q_list = ['112354401', '116115526', '114909312', '122425491', '131957025', '111373473']
df1 = pd.DataFrame.from_records(q_list, columns=['q_data'])
df1

But it gave me the following errors this time:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-24-99e7b8e32a52> in <module>()
      1 import pandas as pd
      2 q_list = ['112354401', '116115526', '114909312', '122425491', '131957025', '111373473']
----> 3 df1 = pd.DataFrame.from_records(q_list, columns=['q_data'])
      4 df1

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
   1021         else:
   1022             arrays, arr_columns = _to_arrays(data, columns,
-> 1023                                              coerce_float=coerce_float)
   1024 
   1025             arr_columns = _ensure_index(arr_columns)

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _to_arrays(data, columns, coerce_float, dtype)
   5550         data = lmap(tuple, data)
   5551         return _list_to_arrays(data, columns, coerce_float=coerce_float,
-> 5552                                dtype=dtype)
   5553 
   5554 

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _list_to_arrays(data, columns, coerce_float, dtype)
   5607         content = list(lib.to_object_array(data).T)
   5608     return _convert_object_array(content, columns, dtype=dtype,
-> 5609                                  coerce_float=coerce_float)
   5610 
   5611 

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _convert_object_array(content, columns, coerce_float, dtype)
   5666             # caller's responsibility to check for this...
   5667             raise AssertionError('%d columns passed, passed data had %s '
-> 5668                                  'columns' % (len(columns), len(content)))
   5669 
   5670     # provide soft conversion of object dtypes

AssertionError: 1 columns passed, passed data had 9 columns

Why would the same approach work for one list but not another? Any idea what might be wrong here? Thanks a lot!

DataFrame.from_records treats string as a character list. so it needs as many columns as length of string.

You could simply use the DataFrame constructor.

In [3]: pd.DataFrame(q_list, columns=['q_data'])
Out[3]:
      q_data
0  112354401
1  116115526
2  114909312
3  122425491
4  131957025
5  111373473

In[20]: test_list = [['a','b','c'], ['AA','BB','CC']]

In[21]: pd.DataFrame(test_list, columns=['col_A', 'col_B', 'col_C'])
Out[21]: 
  col_A col_B col_C
0     a     b     c
1    AA    BB    CC

In[22]: pd.DataFrame(test_list, index=['col_low', 'col_up']).T
Out[22]: 
  col_low col_up
0       a     AA
1       b     BB
2       c     CC

If you want to create a DataFrame from multiple lists you can simply zip the lists. This returns a 'zip' object. So you convert back to a list.

mydf = pd.DataFrame(list(zip(lstA, lstB)), columns = ['My List A', 'My List B'])

You could also take the help of numpy.

import numpy as np
df1 = pd.DataFrame(np.array(q_list),columns=['q_data'])