title: With pandas, how to load multiple TXT files without header?
body: <p>In Python 3 and pandas I loaded several TXT files. They have no header and have the same structure - 46 columns and the same information theme in each column
  Example of three cases</p>
  
  <pre><code>candidatos1 = pd.read_csv("candidatos_2014/consulta_cand_2014_AC.txt",sep=';', header=None, encoding = 'latin_1') 
  
  candidatos1.info()
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  RangeIndex: 621 entries, 0 to 620
  Data columns (total 46 columns):
  0     621 non-null object
  1     621 non-null object
  2     621 non-null int64
  3     621 non-null int64
  4     621 non-null object
  5     621 non-null object
  6     621 non-null object
  7     621 non-null object
  8     621 non-null int64
  9     621 non-null object
  10    621 non-null object
  11    621 non-null int64
  12    621 non-null int64
  13    621 non-null int64
  14    621 non-null object
  15    621 non-null int64
  16    621 non-null object
  17    621 non-null int64
  18    621 non-null object
  19    621 non-null object
  20    621 non-null int64
  21    621 non-null object
  22    621 non-null object
  23    621 non-null object
  24    621 non-null int64
  25    621 non-null object
  26    621 non-null object
  27    621 non-null int64
  28    621 non-null int64
  29    621 non-null int64
  30    621 non-null object
  31    621 non-null int64
  32    621 non-null object
  33    621 non-null int64
  34    621 non-null object
  35    621 non-null int64
  36    621 non-null object
  37    621 non-null int64
  38    621 non-null object
  39    621 non-null object
  40    621 non-null int64
  41    621 non-null object
  42    621 non-null int64
  43    621 non-null int64
  44    621 non-null object
  45    621 non-null object
  dtypes: int64(20), object(26)
  memory usage: 223.2+ KB
  
  candidatos2 = pd.read_csv("candidatos_2014/consulta_cand_2014_AL.txt",sep=';', header=None, encoding = 'latin_1') 
  candidatos2.info()
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  RangeIndex: 479 entries, 0 to 478
  Data columns (total 46 columns):
  0     479 non-null object
  1     479 non-null object
  2     479 non-null int64
  3     479 non-null int64
  4     479 non-null object
  5     479 non-null object
  6     479 non-null object
  7     479 non-null object
  8     479 non-null int64
  9     479 non-null object
  10    479 non-null object
  11    479 non-null int64
  12    479 non-null int64
  13    479 non-null int64
  14    479 non-null object
  15    479 non-null int64
  16    479 non-null object
  17    479 non-null int64
  18    479 non-null object
  19    479 non-null object
  20    479 non-null int64
  21    479 non-null object
  22    479 non-null object
  23    479 non-null object
  24    479 non-null int64
  25    479 non-null object
  26    479 non-null object
  27    479 non-null int64
  28    479 non-null int64
  29    479 non-null int64
  30    479 non-null object
  31    479 non-null int64
  32    479 non-null object
  33    479 non-null int64
  34    479 non-null object
  35    479 non-null int64
  36    479 non-null object
  37    479 non-null int64
  38    479 non-null object
  39    479 non-null object
  40    479 non-null int64
  41    479 non-null object
  42    479 non-null int64
  43    479 non-null int64
  44    479 non-null object
  45    479 non-null object
  dtypes: int64(20), object(26)
  memory usage: 172.2+ KB
  
  candidatos3 = pd.read_csv("candidatos_2014/consulta_cand_2014_AM.txt",sep=';', header=None, encoding = 'latin_1') 
  candidatos3.info()
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  RangeIndex: 786 entries, 0 to 785
  Data columns (total 46 columns):
  0     786 non-null object
  1     786 non-null object
  2     786 non-null int64
  3     786 non-null int64
  4     786 non-null object
  5     786 non-null object
  6     786 non-null object
  7     786 non-null object
  8     786 non-null int64
  9     786 non-null object
  10    786 non-null object
  11    786 non-null int64
  12    786 non-null int64
  13    786 non-null int64
  14    786 non-null object
  15    786 non-null int64
  16    786 non-null object
  17    786 non-null int64
  18    786 non-null object
  19    786 non-null object
  20    786 non-null int64
  21    786 non-null object
  22    786 non-null object
  23    786 non-null object
  24    786 non-null int64
  25    786 non-null object
  26    786 non-null object
  27    786 non-null int64
  28    786 non-null int64
  29    786 non-null int64
  30    786 non-null object
  31    786 non-null int64
  32    786 non-null object
  33    786 non-null int64
  34    786 non-null object
  35    786 non-null int64
  36    786 non-null object
  37    786 non-null int64
  38    786 non-null object
  39    786 non-null object
  40    786 non-null int64
  41    786 non-null object
  42    786 non-null int64
  43    786 non-null int64
  44    786 non-null object
  45    786 non-null object
  dtypes: int64(20), object(26)
  memory usage: 282.5+ KB
  </code></pre>
  
  <p>Please, is there a way to load these files all at once in a single dataframe?</p>
  
  <p>Or do I need to load one at a time and then gather all the dataframes? How?</p>