data-analysis/055-pandas-append/pandas-append.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas append()\n",
    "\n",
    "This is a notebook for the medium article [Pandas append()](https://bindichen.medium.com/5-tricks-to-master-pandas-append-ede4318cc700)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Appending a single row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({\n",
    "    'name': ['A', 'B', 'C', 'D'],\n",
    "    'math': [60,89,82,70],\n",
    "    'physics': [66,95,83,66],\n",
    "    'chemistry': [61,91,77,70]\n",
    "})\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appending a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
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       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "4    E    60       70         80"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Appending a series\n",
    "my_series = pd.Series(\n",
    "    ['E', 60, 70, 80],  \n",
    "    index=['name', 'math', 'physics', 'chemistry']\n",
    ")\n",
    "\n",
    "df1.append(my_series, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appending a dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
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       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>60</td>\n",
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       "      <td>90</td>\n",
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      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "4    E    60       80         90"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Appending a dict\n",
    "my_dict = {'name': 'E', 'math': 60, 'physics': 80, 'chemistry': 90 }\n",
    "\n",
    "df1.append(my_dict, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appending a list - wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>chemistry</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>A</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>B</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>C</td>\n",
       "      <td>83.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>D</td>\n",
       "      <td>66.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     0  chemistry  math name  physics\n",
       "0  NaN       61.0  60.0    A     66.0\n",
       "1  NaN       91.0  89.0    B     95.0\n",
       "2  NaN       77.0  82.0    C     83.0\n",
       "3  NaN       70.0  70.0    D     66.0\n",
       "4    E        NaN   NaN  NaN      NaN\n",
       "5   60        NaN   NaN  NaN      NaN\n",
       "6   70        NaN   NaN  NaN      NaN\n",
       "7   80        NaN   NaN  NaN      NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Appending a list - wrong\n",
    "my_list = ['E', 60, 70, 80]\n",
    "\n",
    "df1.append(my_list, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appending a list - correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>chemistry</th>\n",
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       "      <td>91</td>\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
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       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
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      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "4    E    60       70         80"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Appending a list - correct\n",
    "\n",
    "my_list = ['E', 60, 70, 80]\n",
    "\n",
    "# Convert a lint to Pandas Series\n",
    "a_series = pd.Series(my_list, index=df1.columns)\n",
    "\n",
    "df1.append(a_series, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Appending multiple rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({\n",
    "    'name': ['A', 'B', 'C', 'D'],\n",
    "    'math': [60,89,82,70],\n",
    "    'physics': [66,95,83,66],\n",
    "    'chemistry': [61,91,77,70]\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
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      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. A DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
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       "      <th>0</th>\n",
       "      <td>E</td>\n",
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       "      <td>90</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    E    66       60         90\n",
       "1    F    95       89         81\n",
       "2    G    83       82         78\n",
       "3    H    66       70         90"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "df2 = pd.DataFrame({\n",
    "    'name': ['E', 'F', 'G', 'H'],\n",
    "    'math': [66,95,83,66],\n",
    "    'physics': [60,89,82,70],\n",
    "    'chemistry': [90,81,78,90]\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "0    E    66       60         90\n",
       "1    F    95       89         81\n",
       "2    G    83       82         78\n",
       "3    H    66       70         90"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "4    E    66       60         90\n",
       "5    F    95       89         81\n",
       "6    G    83       82         78\n",
       "7    H    66       70         90"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ingore index\n",
    "df1.append(df2, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 A list of dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'E', 'math': 66, 'physics': 60, 'chemistry': 90},\n",
       " {'name': 'F', 'math': 95, 'physics': 89, 'chemistry': 81},\n",
       " {'name': 'G', 'math': 83, 'physics': 82, 'chemistry': 78},\n",
       " {'name': 'H', 'math': 66, 'physics': 70, 'chemistry': 90}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## A list of dict\n",
    "my_dict_list = [\n",
    "  {'name' : 'E', 'math' : 66, 'physics' : 60, 'chemistry': 90 },\n",
    "  {'name' : 'F', 'math' : 95, 'physics' : 89, 'chemistry': 81 },\n",
    "  {'name' : 'G', 'math' : 83, 'physics' : 82, 'chemistry': 78 },\n",
    "  {'name' : 'H', 'math' : 66, 'physics' : 70, 'chemistry': 90 }\n",
    "]\n",
    "my_dict_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "4    E    66       60         90\n",
       "5    F    95       89         81\n",
       "6    G    83       82         78\n",
       "7    H    66       70         90"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(my_dict_list, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 A list of lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A list of lists\n",
    "my_list_of_list = [\n",
    "    ['E', 66, 60, 90],\n",
    "    ['F', 95, 89, 81],\n",
    "    ['G', 83, 82, 78],\n",
    "    ['H', 66, 70, 90]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    E    66       60         90\n",
       "1    F    95       89         81\n",
       "2    G    83       82         78\n",
       "3    H    66       70         90"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert a list of lists to a DataFrame\n",
    "my_df = pd.DataFrame(my_list_of_list, columns=df1.columns)\n",
    "my_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A    60       66         61\n",
       "1    B    89       95         91\n",
       "2    C    82       83         77\n",
       "3    D    70       66         70\n",
       "4    E    66       60         90\n",
       "5    F    95       89         81\n",
       "6    G    83       82         78\n",
       "7    H    66       70         90"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(my_df, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. When column names don't align"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missing columns\n",
    "my_dict_list_missing = [\n",
    "  {'name' : 'E', 'chemistry': 90 },\n",
    "  {'name' : 'F', 'math' : 95, },\n",
    "  {'name' : 'G', 'math' : 83, 'physics' : 82,  },\n",
    "  {'name' : 'H', 'math' : 66, 'chemistry': 90 }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>60.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>61.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>83.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "0    A  60.0     66.0       61.0\n",
       "1    B  89.0     95.0       91.0\n",
       "2    C  82.0     83.0       77.0\n",
       "3    D  70.0     66.0       70.0\n",
       "4    E   NaN      NaN       90.0\n",
       "5    F  95.0      NaN        NaN\n",
       "6    G  83.0     82.0        NaN\n",
       "7    H  66.0      NaN       90.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(my_dict_list_missing, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New columns\n",
    "my_dict_list_new = [\n",
    "  {'name' : 'E', 'math' : 66, 'physics' : 60, 'chemistry': 90, 'biology': 80 },\n",
    "  {'name' : 'F', 'math' : 95, 'physics' : 89, 'chemistry': 81, 'biology': 60},\n",
    "  {'name' : 'G', 'math' : 83, 'physics' : 82, 'chemistry': 78, 'biology': 65 },\n",
    "  {'name' : 'H', 'math' : 66, 'physics' : 70, 'chemistry': 90, 'biology': 80}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "      <th>biology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry  biology\n",
       "0    A    60       66         61      NaN\n",
       "1    B    89       95         91      NaN\n",
       "2    C    82       83         77      NaN\n",
       "3    D    70       66         70      NaN\n",
       "4    E    66       60         90     80.0\n",
       "5    F    95       89         81     60.0\n",
       "6    G    83       82         78     65.0\n",
       "7    H    66       70         90     80.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(my_dict_list_new, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Sorting columns with `sort=True`\n",
    "\n",
    "Optional, default False. If True, sorts columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>chemistry</th>\n",
       "      <th>math</th>\n",
       "      <th>name</th>\n",
       "      <th>physics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>60</td>\n",
       "      <td>A</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>B</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77</td>\n",
       "      <td>82</td>\n",
       "      <td>C</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>D</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>E</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>F</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>G</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>H</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chemistry  math name  physics\n",
       "0         61    60    A       66\n",
       "1         91    89    B       95\n",
       "2         77    82    C       83\n",
       "3         70    70    D       66\n",
       "4         90    66    E       60\n",
       "5         81    95    F       89\n",
       "6         78    83    G       82\n",
       "7         90    66    H       70"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(df2, ignore_index=True, sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Avoid appending rows with the same index with `verify_integrity=True`\n",
    "\n",
    "Optional, default False. If True, you will get an error if you have two or more rows with the same index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>95</td>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>G</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  math  physics  chemistry\n",
       "1    E    66       60         90\n",
       "4    F    95       89         81\n",
       "5    G    83       82         78\n",
       "6    H    66       70         90"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "df2 = pd.DataFrame({\n",
    "    'name': ['E', 'F', 'G', 'H'],\n",
    "    'math': [66,95,83,66],\n",
    "    'physics': [60,89,82,70],\n",
    "    'chemistry': [90,81,78,90]\n",
    "}, index=[1, 4, 5, 6])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Indexes have overlapping values: Int64Index([1], dtype='int64')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-762e44c99e4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Error Message:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#   ValueError: Indexes have overlapping values: Int64Index([1], dtype='int64')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mappend\u001b[0;34m(self, other, ignore_index, verify_integrity, sort)\u001b[0m\n\u001b[1;32m   7749\u001b[0m             \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7750\u001b[0m             \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7751\u001b[0;31m             \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7752\u001b[0m         )\n\u001b[1;32m   7753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m         \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    285\u001b[0m     )\n\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_axes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_new_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m_get_new_axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m         return [\n\u001b[1;32m    520\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_concat_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbm_axis\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_comb_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         ]\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    519\u001b[0m         return [\n\u001b[1;32m    520\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_concat_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbm_axis\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_comb_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         ]\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m_get_concat_axis\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    578\u001b[0m             )\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_check_integrity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcat_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m_maybe_check_integrity\u001b[0;34m(self, concat_index)\u001b[0m\n\u001b[1;32m    586\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconcat_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m                 \u001b[0moverlap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcat_index\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconcat_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Indexes have overlapping values: {overlap}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Indexes have overlapping values: Int64Index([1], dtype='int64')"
     ]
    }
   ],
   "source": [
    "# Error Message: \n",
    "#   ValueError: Indexes have overlapping values: Int64Index([1], dtype='int64')\n",
    "df1.append(df2, verify_integrity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thanks for reading\n",
    "\n",
    "\n",
    "This is a notebook for the medium article [Pandas append()](https://bindichen.medium.com/5-tricks-to-master-pandas-append-ede4318cc700)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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