notebooks/add_row_numbers.ipynb

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   "source": [
    "# Add row numbers to a DataFrame\n",
    "\n",
    "Use [`np.arange()`](https://numpy.org/doc/stable/reference/generated/numpy.arange.html).\n",
    "\n",
    "Source: [Generate row number in pandas python](https://www.datasciencemadesimple.com/generate-row-number-in-pandas-python-2/) (DataScience Made Simple)"
   ]
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   "source": [
    "# Setup\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
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   "execution_count": 2,
   "id": "58c71f65-3556-420a-976f-9303f5fda274",
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       "     A    B   C  D\n",
       "0  NaN  2.0 NaN  0\n",
       "1  3.0  4.0 NaN  1\n",
       "2  NaN  NaN NaN  5\n",
       "3  NaN  3.0 NaN  4"
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   "source": [
    "# Create some toy data\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    [\n",
    "        [np.nan, 2, np.nan, 0],\n",
    "        [3, 4, np.nan, 1],\n",
    "        [np.nan, np.nan, np.nan, 5],\n",
    "        [np.nan, 3, np.nan, 4]\n",
    "    ],\n",
    "    columns=list(\"ABCD\")\n",
    ")\n",
    "\n",
    "df"
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  {
   "cell_type": "markdown",
   "id": "050e52bc-37ac-4a1b-8868-2287d805a114",
   "metadata": {},
   "source": [
    "You could just create a row number from the index"
   ]
  },
  {
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   "execution_count": 3,
   "id": "f483a53f-da44-4b3a-bba7-abdb03a82108",
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       "      <th>C</th>\n",
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       "      <th>row_number</th>\n",
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       "     A    B   C  D  row_number\n",
       "0  NaN  2.0 NaN  0           0\n",
       "1  3.0  4.0 NaN  1           1\n",
       "2  NaN  NaN NaN  5           2\n",
       "3  NaN  3.0 NaN  4           3"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "# Add a row number column based on the index\n",
    "\n",
    "df.assign(row_number=df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f7e142-dda9-47c0-9b21-7f8dadb57694",
   "metadata": {},
   "source": [
    "However, if you've subset your data and haven't reset the index, or if you have a non-default index, this method will give you unexpected results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8377eae5-872e-4e43-bfb7-d3898071ba0e",
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       "     A    B   C  D\n",
       "0  NaN  2.0 NaN  0\n",
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       "3  NaN  3.0 NaN  4"
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   "source": [
    "# Subset the original data\n",
    "\n",
    "df_subset = df[~pd.isna(df[\"B\"])]\n",
    "\n",
    "df_subset"
   ]
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   "execution_count": 5,
   "id": "3ded86d7-fa5e-46c6-ab5e-2c51e6687090",
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       "     A    B   C  D  row_number\n",
       "0  NaN  2.0 NaN  0           0\n",
       "1  3.0  4.0 NaN  1           1\n",
       "3  NaN  3.0 NaN  4           3"
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   "source": [
    "# Show how using the index to create a row number might give unexpected\n",
    "# results on subset data.\n",
    "\n",
    "df_subset.assign(row_number=df_subset.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f8242e-4cb6-49c6-8a87-d687f1390571",
   "metadata": {},
   "source": [
    "Using `np.arange()` creates row numbers based on the row order, even when data has been subset."
   ]
  },
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   "execution_count": 6,
   "id": "7dd0258c-5da7-4b21-9117-765b75be1580",
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       "     A    B   C  D  row_number\n",
       "0  NaN  2.0 NaN  0           0\n",
       "1  3.0  4.0 NaN  1           1\n",
       "2  NaN  NaN NaN  5           2\n",
       "3  NaN  3.0 NaN  4           3"
      ]
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     "execution_count": 6,
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   ],
   "source": [
    "# Use np.arange() to add a row number column\n",
    "\n",
    "df.assign(row_number=np.arange(df.shape[0]))"
   ]
  },
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       "     A    B   C  D  row_number\n",
       "0  NaN  2.0 NaN  0           0\n",
       "1  3.0  4.0 NaN  1           1\n",
       "3  NaN  3.0 NaN  4           2"
      ]
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   "source": [
    "# Use np.arange() to add a row number column on data that has been subset\n",
    "# and therefore doesn't have an index that reflects the order of the rows.\n",
    "\n",
    "df_subset.assign(row_number=np.arange(df_subset.shape[0]))"
   ]
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   "source": [
    "In the examples above, I've used `pd.DataFrame.assign()` to create the columns without altering the source data. If using this method is unfamiliar to you, just know that you can also just assign the column value."
   ]
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       "     A    B   C  D  row_number\n",
       "0  NaN  2.0 NaN  0           0\n",
       "1  3.0  4.0 NaN  1           1\n",
       "2  NaN  NaN NaN  5           2\n",
       "3  NaN  3.0 NaN  4           3"
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   "source": [
    "# Show adding the row number column using assignment instead of\n",
    "# `pd.DataFrame.assign()`\n",
    "\n",
    "df[\"row_number\"] = np.arange(df.shape[0])\n",
    "\n",
    "df"
   ]
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