3.adding to dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e299bce",
   "metadata": {},
   "source": [
    "# Appending to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8314cda3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    " \n",
    "df = pd.DataFrame(columns = ['A','B','C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea3dfbd8",
   "metadata": {},
   "outputs": [
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       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C]\n",
       "Index: []"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796042f2",
   "metadata": {},
   "source": [
    "df.loc --> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html\n",
    "\n",
    "to access data by using row/values we use df.loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30bee41e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[0,'A'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "312a5162",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "   A    B    C\n",
       "0  1  NaN  NaN"
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     "execution_count": 4,
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3918b0e",
   "metadata": {},
   "outputs": [
    {
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       "   A    B    C\n",
       "0  1  NaN  NaN\n",
       "1  2    3    4"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[1] = [2,3,4]\n",
    "df"
   ]
  },
  {
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   "execution_count": 6,
   "id": "a264166e",
   "metadata": {},
   "outputs": [
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       "   A    B    C\n",
       "0  1  NaN  NaN\n",
       "1  2    3    4\n",
       "2  3    9    9"
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     "execution_count": 6,
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   "source": [
    "df.loc[2] = {'A':3, 'C':9, 'B':9}\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22fd14a",
   "metadata": {},
   "source": [
    "The first input in .loc[] is the index. If you use an existing index, you will overwrite the values in that\n",
    "row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9af4f11",
   "metadata": {},
   "outputs": [
    {
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       "   A    B    C\n",
       "0  1  NaN  NaN\n",
       "1  5    6    7\n",
       "2  3    9    9"
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     "execution_count": 7,
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   "source": [
    "df.loc[1] = [5, 6, 7]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "080e5160",
   "metadata": {},
   "outputs": [
    {
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       "   A  B    C\n",
       "0  1  8  NaN\n",
       "1  5  6    7\n",
       "2  3  9    9"
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   "source": [
    "df.loc[0, 'B'] = 8\n",
    "df"
   ]
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  {
   "cell_type": "markdown",
   "id": "c44936a9",
   "metadata": {},
   "source": [
    "### Adding a dataframe to another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7e2a429",
   "metadata": {},
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       "      <td>a1</td>\n",
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       "    A   B\n",
       "0  a1  b1\n",
       "1  a2  b2"
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df1 = pd.DataFrame([['a1','b1'],['a2','b2']],columns=['A','B'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e87d982",
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   "outputs": [
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       "    B   C\n",
       "0  b1  c1\n",
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     "execution_count": 12,
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   "source": [
    "df2 = pd.DataFrame([['b1','c1'],['b2','c2']],columns=['B','C'])\n",
    "df2"
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   "cell_type": "markdown",
   "id": "ee25762f",
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   "source": [
    "The two DataFrames are not required to have the same set of columns. The append method does\n",
    "not change either of the original DataFrames. Instead, it returns a new DataFrame by appending\n",
    "the original two. Appending a DataFrame to another one is quite simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93d85f4d",
   "metadata": {},
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       "     A   B    C\n",
       "0   a1  b1  NaN\n",
       "1   a2  b2  NaN\n",
       "0  NaN  b1   c1\n",
       "1  NaN  b2   c2"
      ]
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     "execution_count": 14,
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   "source": [
    "df1.append(df2)\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "12bae81a",
   "metadata": {},
   "source": [
    "As you can see, it is possible to have duplicate indices (0 in this example). To avoid this issue, you\n",
    "may ask Pandas to reindex the new DataFrame for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d51e298",
   "metadata": {},
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       "     A   B    C\n",
       "0   a1  b1  NaN\n",
       "1   a2  b2  NaN\n",
       "2  NaN  b1   c1\n",
       "3  NaN  b2   c2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "df1.append(df2,ignore_index=True)"
   ]
  },
  {
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   "execution_count": null,
   "id": "38307728",
   "metadata": {},
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