data-cleaning/pandas-data-type-mismatch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Data Type Mismatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook explains how to identify data in columns with the wrong data type with `pandas`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial uses:\n",
    "* [pandas](https://pandas.pydata.org/docs/)\n",
    "* [datetime](https://docs.python.org/3/library/datetime.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a dataframe that contains multiple occurances of duplication for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A': ['text']*20,\n",
    "                   'B': [1, 2.2]*10,\n",
    "                   'C': [True, False]*10,\n",
    "                   'D': pd.to_datetime('2020-01-01')\n",
    "                  })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, add some mistyped data to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
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       "      <th>D</th>\n",
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       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>text</td>\n",
       "      <td>A+B</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-12.6</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>text</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>text</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      A    B      C                    D\n",
       "0                     1  1.0   True  2020-01-01 00:00:00\n",
       "1                    -2  2.2  False  2020-01-01 00:00:00\n",
       "2                  text  1.0   True                   12\n",
       "3                  text  2.2  False  2020-01-01 00:00:00\n",
       "4                  text  1.0      1  2020-01-01 00:00:00\n",
       "5                  text  2.2  False  2020-01-01 00:00:00\n",
       "6                  text  1.0   True  2020-01-01 00:00:00\n",
       "7                  text  A+B  False  2020-01-01 00:00:00\n",
       "8                  text  1.0   True  2020-01-01 00:00:00\n",
       "9                  text  2.2  -12.6  2020-01-01 00:00:00\n",
       "10  2021-01-01 00:00:00  1.0   True  2020-01-01 00:00:00\n",
       "11                 text  2.2  False  2020-01-01 00:00:00\n",
       "12                 text  1.0   text           2020-01-01\n",
       "13                 text  2.2  False  2020-01-01 00:00:00\n",
       "14                 text  1.0   True  2020-01-01 00:00:00\n",
       "15                 text  2.2  False  2020-01-01 00:00:00\n",
       "16                 text  1.0   True  2020-01-01 00:00:00\n",
       "17                 text  2.2  False  2020-01-01 00:00:00\n",
       "18                 text  1.0   True  2020-01-01 00:00:00\n",
       "19                 text  2.2  False  2020-01-01 00:00:00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,0] = 1\n",
    "df.iloc[1,0] = -2\n",
    "df.iloc[10,0] = pd.to_datetime('2021-01-01')\n",
    "df.iloc[5,1] = '2.2'\n",
    "df.iloc[7,1] = 'A+B'\n",
    "df.iloc[4,2] = 1\n",
    "df.iloc[5,2] = 'False'\n",
    "df.iloc[9,2] = -12.6\n",
    "df.iloc[12,2] = 'text'\n",
    "df.iloc[2,3] = 12\n",
    "df.iloc[12,3] = '2020-01-01'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify mistyped data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `applymap` and `isinstance` will return a Boolean dataframe with **True** when the data type matches and **False** when the data type does not match."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        A      B      C      D\n",
       "0    True   True   True  False\n",
       "1    True   True   True  False\n",
       "2   False   True   True   True\n",
       "3   False   True   True  False\n",
       "4   False   True   True  False\n",
       "5   False  False  False  False\n",
       "6   False   True   True  False\n",
       "7   False  False   True  False\n",
       "8   False   True   True  False\n",
       "9   False   True   True  False\n",
       "10  False   True   True  False\n",
       "11  False   True   True  False\n",
       "12  False   True  False  False\n",
       "13  False   True   True  False\n",
       "14  False   True   True  False\n",
       "15  False   True   True  False\n",
       "16  False   True   True  False\n",
       "17  False   True   True  False\n",
       "18  False   True   True  False\n",
       "19  False   True   True  False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric = df.applymap(lambda x: isinstance(x, (int, float)))\n",
    "numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since only column **B** is supposed to be numeric, this can be made more specific by running `applymap` only on column **B**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "5     False\n",
       "6      True\n",
       "7     False\n",
       "8      True\n",
       "9      True\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "Name: B, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric = df.applymap(lambda x: isinstance(x, (int, float)))['B']\n",
    "numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this Boolean series to return the non-numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>text</td>\n",
       "      <td>2.2</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>text</td>\n",
       "      <td>A+B</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
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      "text/plain": [
       "      A    B      C                    D\n",
       "5  text  2.2  False  2020-01-01 00:00:00\n",
       "7  text  A+B  False  2020-01-01 00:00:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~numeric]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2     False\n",
       "3      True\n",
       "4      True\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8      True\n",
       "9      True\n",
       "10     True\n",
       "11     True\n",
       "12    False\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "Name: D, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = df.applymap(lambda x: isinstance(x, (datetime.datetime)))['D']\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this Boolean series to return the non-numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "       A    B     C           D\n",
       "2   text  1.0  True          12\n",
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   "source": [
    "df[~dt]"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8      True\n",
       "9      True\n",
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       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "Name: A, dtype: bool"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "strings = df.applymap(lambda x: isinstance(x, (str)))['A']\n",
    "strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this Boolean series to return the non-numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
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   "outputs": [
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       "                      A    B      C                    D\n",
       "0                     1  1.0   True  2020-01-01 00:00:00\n",
       "1                    -2  2.2  False  2020-01-01 00:00:00\n",
       "10  2021-01-01 00:00:00  1.0   True  2020-01-01 00:00:00"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[~strings]"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4     False\n",
       "5     False\n",
       "6      True\n",
       "7      True\n",
       "8      True\n",
       "9     False\n",
       "10     True\n",
       "11     True\n",
       "12    False\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "Name: C, dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torf = df.applymap(lambda x: isinstance(x, (bool)))['C']\n",
    "torf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this Boolean series to return the non-numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "       A    B      C                    D\n",
       "4   text  1.0      1  2020-01-01 00:00:00\n",
       "5   text  2.2  False  2020-01-01 00:00:00\n",
       "9   text  2.2  -12.6  2020-01-01 00:00:00\n",
       "12  text  1.0   text           2020-01-01"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[~torf]"
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  }
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