examples/6_read_R_data.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to read R format data, which means the length of header line is length-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We prepared three files in ../data folder, one is R format, the othera are normal txt file"
   ]
  },
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       "      <td>78</td>\n",
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       "      <td>44</td>\n",
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       "        Col1  col2  col3  col4\n",
       "Index1     4     5     7     8\n",
       "Index2     9    10    45    78\n",
       "Index3    44    34    90    12"
      ]
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   "source": [
    "# R format, meaning header line is one less than main body\n",
    "pd.read_csv('../data/R_data.txt',sep='\\t')   # will automatically figure it out"
   ]
  },
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       "        col1  col2  col3  col4\n",
       "Index1     1     4     7     9\n",
       "Index2    10    45    78    90\n",
       "Index3    11    34    45    89"
      ]
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   "source": [
    "# normal format, means the first entry is empty, but the column number is the same (header and main body)\n",
    "pd.read_csv('../data/normal_data.txt',sep='\\t',index_col=0)"
   ]
  },
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       "      <td>Index2</td>\n",
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       "      <td>90</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>Index3</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
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       "      <td>89</td>\n",
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       "    Place  col1  col2  col3  col4\n",
       "0  Index1     1     4     7     9\n",
       "1  Index2    10    45    78    90\n",
       "2  Index3    11    34    45    89"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# complete format\n",
    "pd.read_csv('../data/complete_data.txt',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        col1  col2  col3  col4\n",
      "Place                         \n",
      "Index1     1     4     7     9\n",
      "Index2    10    45    78    90\n",
      "Index3    11    34    45    89\n",
      "Index(['Index1', 'Index2', 'Index3'], dtype='object', name='Place')\n",
      "Index(['col1', 'col2', 'col3', 'col4'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# complete format\n",
    "df=pd.read_csv('../data/complete_data.txt',sep='\\t',index_col=0)\n",
    "print(df)\n",
    "print(df.index)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As an extension, it is always good to check how pandas infer the name of columns and index, things can get more complicated when multi-index and skip-rows are involved"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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