Python: create pandas data frame from list

Question

I use 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 is working 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 this time he gave me the following errors:

 --------------------------------------------------------------------------- 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 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 does the same approach work for one list, but not another? Any idea what could be wrong here? Thank you very much!

Answer 1

DataFrame.from_records treats a string as a list of characters. so it needs as many columns as the length of the row.

You can 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