DropnaBlogPost.ipynb

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        "# Professional Pandas: Handling Missing Data With Pandas Dropna\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Ah, happiness. We all seek it, yet it's as elusive as a consistent Wi-Fi signal in a rural area. Do you know what else is elusive? Good, clean data. Like pursuing happiness, the quest for pristine data is a journey fraught with pitfalls and \"null\" ends—pun intended.\n",
        "\n",
        "In this blog, we will explore the [World Happiness Report](https://worldhappiness.report/) and try to understand how to deal with missing data.\n",
        "\n",
        "So, buckle up! We're about to venture into the labyrinth of missing data and emerge unscathed, armed with the mighty `.dropna` method.\n"
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        "## The Basics: What is `dropna`?\n",
        "\n",
        "Oh, you haven't met `.dropna` method yet? Allow me to introduce you to the Marie Kondo of data cleaning. This Pandas method asks a simple yet profound question: \"Does this missing value spark joy?\" If not, it's out the door—or off the DataFrame, to be precise.\n",
        "\n",
        "```python\n",
        "# Syntax for the uninitiated\n",
        "dataframe.dropna()\n",
        "```\n",
        "\n",
        "The `dropna` method, in essence, scans your DataFrame for any cells that dare to be empty (`NaN` or `<NA>` (Not a Number)), and then takes them out of the equation—literally. Generally it is used to drop rows with missing values, but it can also drop columns and base the decision to drop on some threshold of missing data.\n",
        "\n",
        "### A Simple Example to Light Your Way\n",
        "\n",
        "Imagine a DataFrame that lists happiness factors for different countries, but some of the data is missing. Here’s a simplistic example:"
      ]
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Life Expectancy</th>\n",
              "      <th>Freedom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Happyland</td>\n",
              "      <td>1.5</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sadtown</td>\n",
              "      <td>0.5</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mehville</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Whoville</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "     Country  GDP  Life Expectancy  Freedom\n",
              "0  Happyland  1.5             80.0      0.9\n",
              "1    Sadtown  0.5             50.0      0.2\n",
              "2   Mehville  NaN              NaN      0.5\n",
              "3   Whoville  NaN             92.0      0.8"
            ]
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          "execution_count": 37,
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      "source": [
        "import pandas as pd\n",
        "\n",
        "# Creating a DataFrame with some missing values\n",
        "data = {\n",
        "    'Country': ['Happyland', 'Sadtown', 'Mehville', 'Whoville'],\n",
        "    'GDP': [1.5, 0.5, None, None],\n",
        "    'Life Expectancy': [80, 50, None, 92],\n",
        "    'Freedom': [0.9, 0.2, 0.5, .8]\n",
        "}\n",
        "\n",
        "simple = pd.DataFrame(data)\n",
        "simple"
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        "Notice the `NaN` under 'GDP' and 'Life Expectancy' for Mehville and 'GDP' for Whoville? Now, consider calculating the average GDP. What does that mean if the values are missing? One easy way to get around that problem is to remove the offending rows:"
      ]
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Life Expectancy</th>\n",
              "      <th>Freedom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Happyland</td>\n",
              "      <td>1.5</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sadtown</td>\n",
              "      <td>0.5</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
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              "     Country  GDP  Life Expectancy  Freedom\n",
              "0  Happyland  1.5             80.0      0.9\n",
              "1    Sadtown  0.5             50.0      0.2"
            ]
          },
          "execution_count": 38,
          "metadata": {},
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      "source": [
        "# Dropping rows with missing values\n",
        "simple.dropna()"
      ]
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      "source": [
        "And voila! Mehville and Whoville are out. Now, whether you want to remove that row is a question you can debate. I'll give some simple rules later.\n",
        "\n",
        "We can also drop columns with missing values (however, in practice, I avoid this):"
      ]
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Freedom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Happyland</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sadtown</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mehville</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Whoville</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "     Country  Freedom\n",
              "0  Happyland      0.9\n",
              "1    Sadtown      0.2\n",
              "2   Mehville      0.5\n",
              "3   Whoville      0.8"
            ]
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      "source": [
        "simple.dropna(axis='columns')"
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      "source": [
        "The `thresh` parameter allows you to specify the minimum number of non-null values a row or column must have to survive the `dropna` purge—essentially setting a threshold for how \"empty\" is too empty. Below, we keep all rows with three non-missing values:"
      ]
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Life Expectancy</th>\n",
              "      <th>Freedom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Happyland</td>\n",
              "      <td>1.5</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sadtown</td>\n",
              "      <td>0.5</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Whoville</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.8</td>\n",
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              "     Country  GDP  Life Expectancy  Freedom\n",
              "0  Happyland  1.5             80.0      0.9\n",
              "1    Sadtown  0.5             50.0      0.2\n",
              "3   Whoville  NaN             92.0      0.8"
            ]
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        "simple.dropna(thresh=3)"
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      "source": [
        "## Setting the Stage: The World Happiness Report Data\n",
        "\n",
        "The World Happiness Report provides annual rankings of countries based on various metrics that aim to capture the well-being and happiness of their citizens. It’s a comprehensive survey with economic, social, and psychological indicators.\n",
        "\n",
        "The dataset is available on the official [World Happiness Report website](https://worldhappiness.report/). After downloading, you will get an Excel file which can be loaded into a Pandas DataFrame.\n",
        "\n",
        "### Loading the Data into a DataFrame\n",
        "\n",
        "I downloaded the data from 2023. To load the downloaded dataset into Python, use the `read_excel` function:"
      ]
    },
    {
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      "source": [
        "\n",
        "!pip install -U pandas pyarrow"
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      "metadata": {
        "id": "5f7a4609-43d6-48ed-a91f-2eafd4a16a79"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "url = 'https://happiness-report.s3.amazonaws.com/2023/DataForTable2.1WHR2023.xls'\n",
        "df = pd.read_excel(url, dtype_backend='pyarrow')"
      ]
    },
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Country name</th>\n",
              "      <th>year</th>\n",
              "      <th>Life Ladder</th>\n",
              "      <th>Log GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy at birth</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "      <th>Positive affect</th>\n",
              "      <th>Negative affect</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2008</td>\n",
              "      <td>3.723590</td>\n",
              "      <td>7.350416</td>\n",
              "      <td>0.450662</td>\n",
              "      <td>50.500000</td>\n",
              "      <td>0.718114</td>\n",
              "      <td>0.167652</td>\n",
              "      <td>0.881686</td>\n",
              "      <td>0.414297</td>\n",
              "      <td>0.258195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2009</td>\n",
              "      <td>4.401778</td>\n",
              "      <td>7.508646</td>\n",
              "      <td>0.552308</td>\n",
              "      <td>50.799999</td>\n",
              "      <td>0.678896</td>\n",
              "      <td>0.190809</td>\n",
              "      <td>0.850035</td>\n",
              "      <td>0.481421</td>\n",
              "      <td>0.237092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2010</td>\n",
              "      <td>4.758381</td>\n",
              "      <td>7.613900</td>\n",
              "      <td>0.539075</td>\n",
              "      <td>51.099998</td>\n",
              "      <td>0.600127</td>\n",
              "      <td>0.121316</td>\n",
              "      <td>0.706766</td>\n",
              "      <td>0.516907</td>\n",
              "      <td>0.275324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2011</td>\n",
              "      <td>3.831719</td>\n",
              "      <td>7.581259</td>\n",
              "      <td>0.521104</td>\n",
              "      <td>51.400002</td>\n",
              "      <td>0.495901</td>\n",
              "      <td>0.163571</td>\n",
              "      <td>0.731109</td>\n",
              "      <td>0.479835</td>\n",
              "      <td>0.267175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2012</td>\n",
              "      <td>3.782938</td>\n",
              "      <td>7.660506</td>\n",
              "      <td>0.520637</td>\n",
              "      <td>51.700001</td>\n",
              "      <td>0.530935</td>\n",
              "      <td>0.237588</td>\n",
              "      <td>0.775620</td>\n",
              "      <td>0.613513</td>\n",
              "      <td>0.267919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2194</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>3.616480</td>\n",
              "      <td>7.783066</td>\n",
              "      <td>0.775388</td>\n",
              "      <td>52.625000</td>\n",
              "      <td>0.762675</td>\n",
              "      <td>-0.051219</td>\n",
              "      <td>0.844209</td>\n",
              "      <td>0.657524</td>\n",
              "      <td>0.211726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2195</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2019</td>\n",
              "      <td>2.693523</td>\n",
              "      <td>7.697755</td>\n",
              "      <td>0.759162</td>\n",
              "      <td>53.099998</td>\n",
              "      <td>0.631908</td>\n",
              "      <td>-0.047464</td>\n",
              "      <td>0.830652</td>\n",
              "      <td>0.658434</td>\n",
              "      <td>0.235354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2196</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2020</td>\n",
              "      <td>3.159802</td>\n",
              "      <td>7.596050</td>\n",
              "      <td>0.717243</td>\n",
              "      <td>53.575001</td>\n",
              "      <td>0.643303</td>\n",
              "      <td>0.006313</td>\n",
              "      <td>0.788523</td>\n",
              "      <td>0.660658</td>\n",
              "      <td>0.345736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2197</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021</td>\n",
              "      <td>3.154578</td>\n",
              "      <td>7.656878</td>\n",
              "      <td>0.685151</td>\n",
              "      <td>54.049999</td>\n",
              "      <td>0.667636</td>\n",
              "      <td>-0.075575</td>\n",
              "      <td>0.756945</td>\n",
              "      <td>0.609917</td>\n",
              "      <td>0.241682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2198</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2022</td>\n",
              "      <td>3.296220</td>\n",
              "      <td>7.670123</td>\n",
              "      <td>0.666172</td>\n",
              "      <td>54.525002</td>\n",
              "      <td>0.651987</td>\n",
              "      <td>-0.069513</td>\n",
              "      <td>0.752632</td>\n",
              "      <td>0.640609</td>\n",
              "      <td>0.191350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2199 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Country name  year  Life Ladder  Log GDP per capita  Social support  \\\n",
              "0     Afghanistan  2008     3.723590            7.350416        0.450662   \n",
              "1     Afghanistan  2009     4.401778            7.508646        0.552308   \n",
              "2     Afghanistan  2010     4.758381            7.613900        0.539075   \n",
              "3     Afghanistan  2011     3.831719            7.581259        0.521104   \n",
              "4     Afghanistan  2012     3.782938            7.660506        0.520637   \n",
              "...           ...   ...          ...                 ...             ...   \n",
              "2194     Zimbabwe  2018     3.616480            7.783066        0.775388   \n",
              "2195     Zimbabwe  2019     2.693523            7.697755        0.759162   \n",
              "2196     Zimbabwe  2020     3.159802            7.596050        0.717243   \n",
              "2197     Zimbabwe  2021     3.154578            7.656878        0.685151   \n",
              "2198     Zimbabwe  2022     3.296220            7.670123        0.666172   \n",
              "\n",
              "      Healthy life expectancy at birth  Freedom to make life choices  \\\n",
              "0                            50.500000                      0.718114   \n",
              "1                            50.799999                      0.678896   \n",
              "2                            51.099998                      0.600127   \n",
              "3                            51.400002                      0.495901   \n",
              "4                            51.700001                      0.530935   \n",
              "...                                ...                           ...   \n",
              "2194                         52.625000                      0.762675   \n",
              "2195                         53.099998                      0.631908   \n",
              "2196                         53.575001                      0.643303   \n",
              "2197                         54.049999                      0.667636   \n",
              "2198                         54.525002                      0.651987   \n",
              "\n",
              "      Generosity  Perceptions of corruption  Positive affect  Negative affect  \n",
              "0       0.167652                   0.881686         0.414297         0.258195  \n",
              "1       0.190809                   0.850035         0.481421         0.237092  \n",
              "2       0.121316                   0.706766         0.516907         0.275324  \n",
              "3       0.163571                   0.731109         0.479835         0.267175  \n",
              "4       0.237588                   0.775620         0.613513         0.267919  \n",
              "...          ...                        ...              ...              ...  \n",
              "2194   -0.051219                   0.844209         0.657524         0.211726  \n",
              "2195   -0.047464                   0.830652         0.658434         0.235354  \n",
              "2196    0.006313                   0.788523         0.660658         0.345736  \n",
              "2197   -0.075575                   0.756945         0.609917         0.241682  \n",
              "2198   -0.069513                   0.752632         0.640609         0.191350  \n",
              "\n",
              "[2199 rows x 11 columns]"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03126f40-dbcb-4777-bfb3-674d688e7e15",
      "metadata": {
        "id": "03126f40-dbcb-4777-bfb3-674d688e7e15"
      },
      "source": [
        "### Understanding the Columns\n",
        "\n",
        "The dataset includes several columns that capture various dimensions of happiness and well-being.\n",
        "\n",
        "Here are the [key columns](https://happiness-report.s3.amazonaws.com/2023/WHR+23_Statistical_Appendix.pdf):\n",
        "\n",
        "- `Country name`: The name of the country.\n",
        "- `year`: The year the data was collected.\n",
        "- `Life Ladder`: An overall measure of well-being.\n",
        "- `Log GDP per capita`: The logarithm of GDP per capita, capturing economic standing.\n",
        "- `Social support`: Gauges the extent of social support networks.\n",
        "- `Healthy life expectancy at birth`: The expected number of years of healthy living.\n",
        "- `Freedom to make life choices`: A measure of personal freedoms.\n",
        "- `Generosity`: Captures charitable behavior and attitudes.\n",
        "- `Perceptions of corruption`: Indicates the level of perceived public sector corruption.\n",
        "- `Positive affect`: Measures positive emotional experiences.\n",
        "- `Negative affect`: Measures negative emotional experiences.\n",
        "\n",
        "Here's a plot to visualize the median negative emotional experiences by continent over the years. You'll note that some of the lines are not contiguous -- there are missing values. Let's see if we can devise a plan to deal with this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b80bc4c7-c3bc-40d5-ad7e-043529b529b2",
      "metadata": {
        "id": "b80bc4c7-c3bc-40d5-ad7e-043529b529b2",
        "outputId": "d821307f-42e6-444b-fd4e-66a0113922ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x17831d1b0>"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def get_continent(country_name):\n",
        "    africa = ['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Burundi', 'Cameroon', 'Central African Republic', 'Chad', 'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Djibouti', 'Egypt', 'Eswatini', 'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Ivory Coast', 'Kenya', 'Lesotho', 'Liberia', 'Libya', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius', 'Morocco', 'Mozambique', 'Namibia', 'Niger', 'Nigeria', 'Rwanda', 'Senegal', 'Seychelles', 'Sierra Leone', 'Somalia', 'Somaliland region', 'South Africa', 'South Sudan', 'Sudan', 'Tanzania', 'Togo', 'Tunisia', 'Uganda', 'Zambia', 'Zimbabwe']\n",
        "    asia = ['Afghanistan', 'Armenia', 'Azerbaijan', 'Bahrain', 'Bangladesh', 'Bhutan', 'Cambodia', 'China', 'Cyprus', 'Georgia', 'Hong Kong S.A.R. of China', 'India', 'Indonesia', 'Iran', 'Iraq', 'Israel', 'Japan', 'Jordan', 'Kazakhstan', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Lebanon', 'Malaysia', 'Maldives', 'Mongolia', 'Myanmar', 'Nepal', 'North Korea', 'Oman', 'Pakistan', 'Palestinian Territories', 'Philippines', 'Qatar', 'Saudi Arabia', 'Singapore', 'South Korea', 'Sri Lanka', 'State of Palestine', 'Syria', 'Taiwan Province of China', 'Tajikistan', 'Thailand', 'Turkiye', 'Turkmenistan', 'United Arab Emirates', 'Uzbekistan', 'Vietnam', 'Yemen']\n",
        "    europe = ['Albania', 'Andorra', 'Austria', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria', 'Croatia', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland', 'Italy', 'Kosovo', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Malta', 'Moldova', 'Monaco', 'Montenegro', 'Netherlands', 'North Macedonia', 'Norway', 'Poland', 'Portugal', 'Romania', 'Russia', 'San Marino', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom', 'Vatican City']\n",
        "    n_america = ['Antigua and Barbuda', 'Bahamas', 'Barbados', 'Belize', 'Canada', 'Costa Rica', 'Cuba', 'Dominica', 'Dominican Republic', 'El Salvador', 'Grenada', 'Guatemala', 'Haiti', 'Honduras', 'Jamaica', 'Mexico', 'Nicaragua', 'Panama', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent and the Grenadines', 'Trinidad and Tobago', 'United States']\n",
        "    s_america = ['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Peru', 'Suriname', 'Uruguay', 'Venezuela']\n",
        "    oceania = ['Australia', 'Fiji', 'Kiribati', 'Marshall Islands', 'Micronesia', 'Nauru', 'New Zealand', 'Palau', 'Papua New Guinea', 'Samoa', 'Solomon Islands', 'Tonga', 'Tuvalu', 'Vanuatu']\n",
        "\n",
        "    if country_name in africa:\n",
        "        return 'Africa'\n",
        "    elif country_name in asia:\n",
        "        return 'Asia'\n",
        "    elif country_name in europe:\n",
        "        return 'Europe'\n",
        "    elif country_name in n_america:\n",
        "        return 'North America'\n",
        "    elif country_name in s_america:\n",
        "        return 'South America'\n",
        "    elif country_name in oceania:\n",
        "        return 'Oceania'\n",
        "    else:\n",
        "        return 'Unknown'\n",
        "\n",
        "(df\n",
        " .assign(Continent=df['Country name'].apply(get_continent))\n",
        " .groupby(['year', 'Continent'])\n",
        " ['Negative affect']\n",
        " .median()\n",
        " .unstack()\n",
        " .plot()\n",
        " .legend(bbox_to_anchor=(1,1))\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91ba1d75-9548-4bcc-8346-c732490de3d4",
      "metadata": {
        "id": "91ba1d75-9548-4bcc-8346-c732490de3d4"
      },
      "source": [
        "## Identifying Missing Values\n",
        "\n",
        "Before proceeding, let's identify if there are any missing values in the dataset. You can find out which columns contain missing values using the following Pandas code:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64811228-9b6d-435d-bc26-036312836687",
      "metadata": {
        "id": "64811228-9b6d-435d-bc26-036312836687",
        "outputId": "f8325fe9-91c2-4a61-8075-8072c20f35a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Country name                          0\n",
              "year                                  0\n",
              "Life Ladder                           0\n",
              "Log GDP per capita                   20\n",
              "Social support                       13\n",
              "Healthy life expectancy at birth     54\n",
              "Freedom to make life choices         33\n",
              "Generosity                           73\n",
              "Perceptions of corruption           116\n",
              "Positive affect                      24\n",
              "Negative affect                      16\n",
              "dtype: int64"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Identify missing values\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41c9c295-15e1-41e7-8812-4e321488480a",
      "metadata": {
        "id": "41c9c295-15e1-41e7-8812-4e321488480a"
      },
      "source": [
        "In Python, boolean values `True` and `False` are treated as 1 and 0, respectively, when used in numerical calculations. Therefore, when you use `.sum` on a DataFrame of `True` and `False` values, it effectively counts the number of `True` values by summing them up as 1s, while the `False` values contribute zero to the sum."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f07ae93d-0685-4613-9be7-3ce16181ce35",
      "metadata": {
        "id": "f07ae93d-0685-4613-9be7-3ce16181ce35"
      },
      "source": [
        "This command returns a Series with the column names in the index and the number of missing values for column.\n",
        "\n",
        "I also like to take the mean to quantify the percent missing:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ffd2af96-1503-4af8-a44f-5a2edea434a7",
      "metadata": {
        "id": "ffd2af96-1503-4af8-a44f-5a2edea434a7",
        "outputId": "f56f9977-810b-464d-9162-8cd87d853c4a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Country name                        0.000000\n",
              "year                                0.000000\n",
              "Life Ladder                         0.000000\n",
              "Log GDP per capita                  0.909504\n",
              "Social support                      0.591178\n",
              "Healthy life expectancy at birth    2.455662\n",
              "Freedom to make life choices        1.500682\n",
              "Generosity                          3.319691\n",
              "Perceptions of corruption           5.275125\n",
              "Positive affect                     1.091405\n",
              "Negative affect                     0.727603\n",
              "dtype: float64"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Identify missing values\n",
        "df.isna().mean().mul(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "623deabe-4ce2-40eb-93e7-7890e5f63b3e",
      "metadata": {
        "id": "623deabe-4ce2-40eb-93e7-7890e5f63b3e"
      },
      "source": [
        "It looks with many columns are missing 1-5% of the time."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "802f6eb7-85d0-4766-915c-1600f10037c0",
      "metadata": {
        "id": "802f6eb7-85d0-4766-915c-1600f10037c0"
      },
      "source": [
        "Armed with Python's boolean mathematics, we can also count the missing values per row."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "41746050-f6aa-4587-86f0-fd84e92e0fb8",
      "metadata": {
        "id": "41746050-f6aa-4587-86f0-fd84e92e0fb8",
        "outputId": "2e0d70fd-602d-4b47-94b1-311ef3752e09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       0\n",
              "1       0\n",
              "2       0\n",
              "3       0\n",
              "4       0\n",
              "       ..\n",
              "2194    0\n",
              "2195    0\n",
              "2196    0\n",
              "2197    0\n",
              "2198    0\n",
              "Length: 2199, dtype: int64"
            ]
          },
          "execution_count": 166,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum(axis='columns')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "87072618-5b5e-411f-b5e3-02a42add8ebe",
      "metadata": {
        "id": "87072618-5b5e-411f-b5e3-02a42add8ebe"
      },
      "source": [
        "Let's throw a quick `.value_counts` on that to try and make sense of how many values might be missing for rows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c6ec9119-a506-4330-814b-6da39cfee856",
      "metadata": {
        "id": "c6ec9119-a506-4330-814b-6da39cfee856",
        "outputId": "3b94be20-456d-4327-edd6-d4eae0fdcbbc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    1958\n",
              "1     179\n",
              "2      33\n",
              "3      23\n",
              "6       5\n",
              "5       1\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df\n",
        " .isna()\n",
        " .sum(axis='columns')\n",
        " .value_counts()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "05fff804-c623-4d29-9e8d-602690ea8b29",
      "metadata": {
        "id": "05fff804-c623-4d29-9e8d-602690ea8b29"
      },
      "source": [
        "It looks like most rows aren't missing any values, but five rows are missing six values!\n",
        "\n",
        "We could use a filter with `.loc` to examine which rows are missing three values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "66ffff63-c98a-4530-9ef0-b321c7d29468",
      "metadata": {
        "id": "66ffff63-c98a-4530-9ef0-b321c7d29468",
        "outputId": "a69fc8b7-490c-4a50-ca33-32eca2650de2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>Country name</th>\n",
              "      <th>year</th>\n",
              "      <th>Life Ladder</th>\n",
              "      <th>Log GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy at birth</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "      <th>Positive affect</th>\n",
              "      <th>Negative affect</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>2010</td>\n",
              "      <td>5.463567</td>\n",
              "      <td>9.306355</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>65.5</td>\n",
              "      <td>0.592696</td>\n",
              "      <td>-0.209753</td>\n",
              "      <td>0.618038</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>2014</td>\n",
              "      <td>6.354898</td>\n",
              "      <td>9.355415</td>\n",
              "      <td>0.818189</td>\n",
              "      <td>65.900002</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.558359</td>\n",
              "      <td>0.176866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>2016</td>\n",
              "      <td>5.340854</td>\n",
              "      <td>9.383312</td>\n",
              "      <td>0.748588</td>\n",
              "      <td>66.099998</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.565026</td>\n",
              "      <td>0.377112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>China</td>\n",
              "      <td>2006</td>\n",
              "      <td>4.560495</td>\n",
              "      <td>8.696139</td>\n",
              "      <td>0.747011</td>\n",
              "      <td>65.660004</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.657659</td>\n",
              "      <td>0.16958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>2006</td>\n",
              "      <td>5.417869</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.969595</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.281458</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.596187</td>\n",
              "      <td>0.276602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>524</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>2010</td>\n",
              "      <td>5.005811</td>\n",
              "      <td>7.934187</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>54.599998</td>\n",
              "      <td>0.76373</td>\n",
              "      <td>-0.068953</td>\n",
              "      <td>0.59691</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>2018</td>\n",
              "      <td>4.638934</td>\n",
              "      <td>9.167903</td>\n",
              "      <td>0.799544</td>\n",
              "      <td>67.599998</td>\n",
              "      <td>0.76242</td>\n",
              "      <td>-0.1868</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>2019</td>\n",
              "      <td>4.452548</td>\n",
              "      <td>9.164418</td>\n",
              "      <td>0.79256</td>\n",
              "      <td>67.599998</td>\n",
              "      <td>0.725756</td>\n",
              "      <td>-0.165088</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>2020</td>\n",
              "      <td>4.093992</td>\n",
              "      <td>9.127326</td>\n",
              "      <td>0.70884</td>\n",
              "      <td>67.599998</td>\n",
              "      <td>0.778533</td>\n",
              "      <td>-0.151104</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1031</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>2007</td>\n",
              "      <td>5.103906</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.847812</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.381364</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.894462</td>\n",
              "      <td>0.613723</td>\n",
              "      <td>0.236699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1207</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>2018</td>\n",
              "      <td>5.197575</td>\n",
              "      <td>9.892906</td>\n",
              "      <td>0.913315</td>\n",
              "      <td>69.775002</td>\n",
              "      <td>0.854759</td>\n",
              "      <td>0.015678</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1323</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>2010</td>\n",
              "      <td>4.383247</td>\n",
              "      <td>8.672808</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>62.5</td>\n",
              "      <td>0.6629</td>\n",
              "      <td>-0.15965</td>\n",
              "      <td>0.900453</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1485</th>\n",
              "      <td>Oman</td>\n",
              "      <td>2011</td>\n",
              "      <td>6.852982</td>\n",
              "      <td>10.53862</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>62.34</td>\n",
              "      <td>0.916293</td>\n",
              "      <td>0.009673</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.295164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1755</th>\n",
              "      <td>Somaliland region</td>\n",
              "      <td>2009</td>\n",
              "      <td>4.991400</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.879567</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.746304</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.513372</td>\n",
              "      <td>0.707874</td>\n",
              "      <td>0.112012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1756</th>\n",
              "      <td>Somaliland region</td>\n",
              "      <td>2010</td>\n",
              "      <td>4.657363</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.829005</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.820182</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.471094</td>\n",
              "      <td>0.631947</td>\n",
              "      <td>0.083426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1757</th>\n",
              "      <td>Somaliland region</td>\n",
              "      <td>2011</td>\n",
              "      <td>4.930572</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.787962</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.858104</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.357341</td>\n",
              "      <td>0.690514</td>\n",
              "      <td>0.122244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1758</th>\n",
              "      <td>Somaliland region</td>\n",
              "      <td>2012</td>\n",
              "      <td>5.057314</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.786291</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.758219</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.333832</td>\n",
              "      <td>0.686732</td>\n",
              "      <td>0.152428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1842</th>\n",
              "      <td>State of Palestine</td>\n",
              "      <td>2022</td>\n",
              "      <td>4.907760</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.859655</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.694853</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.835795</td>\n",
              "      <td>0.584111</td>\n",
              "      <td>0.361776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1897</th>\n",
              "      <td>Taiwan Province of China</td>\n",
              "      <td>2020</td>\n",
              "      <td>6.751068</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.900833</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.798835</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.710567</td>\n",
              "      <td>0.743169</td>\n",
              "      <td>0.082737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1898</th>\n",
              "      <td>Taiwan Province of China</td>\n",
              "      <td>2021</td>\n",
              "      <td>6.246744</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.866298</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.818467</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.675439</td>\n",
              "      <td>0.667417</td>\n",
              "      <td>0.12298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1899</th>\n",
              "      <td>Taiwan Province of China</td>\n",
              "      <td>2022</td>\n",
              "      <td>6.607147</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.88282</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.800192</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.657556</td>\n",
              "      <td>0.716587</td>\n",
              "      <td>0.095399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1966</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>2009</td>\n",
              "      <td>5.025470</td>\n",
              "      <td>9.237887</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>66.220001</td>\n",
              "      <td>0.781496</td>\n",
              "      <td>-0.124444</td>\n",
              "      <td>0.722211</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2044</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>2013</td>\n",
              "      <td>6.620951</td>\n",
              "      <td>11.040596</td>\n",
              "      <td>0.863716</td>\n",
              "      <td>65.279999</td>\n",
              "      <td>0.935979</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.291113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country name  year  Life Ladder  Log GDP per capita  \\\n",
              "29                     Algeria  2010     5.463567            9.306355   \n",
              "32                     Algeria  2014     6.354898            9.355415   \n",
              "33                     Algeria  2016     5.340854            9.383312   \n",
              "381                      China  2006     4.560495            8.696139   \n",
              "475                       Cuba  2006     5.417869                <NA>   \n",
              "524                   Djibouti  2010     5.005811            7.934187   \n",
              "992                     Jordan  2018     4.638934            9.167903   \n",
              "993                     Jordan  2019     4.452548            9.164418   \n",
              "994                     Jordan  2020     4.093992            9.127326   \n",
              "1031                    Kosovo  2007     5.103906                <NA>   \n",
              "1207                  Maldives  2018     5.197575            9.892906   \n",
              "1323                   Morocco  2010     4.383247            8.672808   \n",
              "1485                      Oman  2011     6.852982            10.53862   \n",
              "1755         Somaliland region  2009     4.991400                <NA>   \n",
              "1756         Somaliland region  2010     4.657363                <NA>   \n",
              "1757         Somaliland region  2011     4.930572                <NA>   \n",
              "1758         Somaliland region  2012     5.057314                <NA>   \n",
              "1842        State of Palestine  2022     4.907760                <NA>   \n",
              "1897  Taiwan Province of China  2020     6.751068                <NA>   \n",
              "1898  Taiwan Province of China  2021     6.246744                <NA>   \n",
              "1899  Taiwan Province of China  2022     6.607147                <NA>   \n",
              "1966                   Tunisia  2009     5.025470            9.237887   \n",
              "2044      United Arab Emirates  2013     6.620951           11.040596   \n",
              "\n",
              "      Social support  Healthy life expectancy at birth  \\\n",
              "29              <NA>                              65.5   \n",
              "32          0.818189                         65.900002   \n",
              "33          0.748588                         66.099998   \n",
              "381         0.747011                         65.660004   \n",
              "475         0.969595                              68.0   \n",
              "524             <NA>                         54.599998   \n",
              "992         0.799544                         67.599998   \n",
              "993          0.79256                         67.599998   \n",
              "994          0.70884                         67.599998   \n",
              "1031        0.847812                              <NA>   \n",
              "1207        0.913315                         69.775002   \n",
              "1323            <NA>                              62.5   \n",
              "1485            <NA>                             62.34   \n",
              "1755        0.879567                              <NA>   \n",
              "1756        0.829005                              <NA>   \n",
              "1757        0.787962                              <NA>   \n",
              "1758        0.786291                              <NA>   \n",
              "1842        0.859655                              <NA>   \n",
              "1897        0.900833                              <NA>   \n",
              "1898        0.866298                              <NA>   \n",
              "1899         0.88282                              <NA>   \n",
              "1966            <NA>                         66.220001   \n",
              "2044        0.863716                         65.279999   \n",
              "\n",
              "      Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
              "29                        0.592696   -0.209753                   0.618038   \n",
              "32                            <NA>        <NA>                       <NA>   \n",
              "33                            <NA>        <NA>                       <NA>   \n",
              "381                           <NA>        <NA>                       <NA>   \n",
              "475                       0.281458        <NA>                       <NA>   \n",
              "524                        0.76373   -0.068953                    0.59691   \n",
              "992                        0.76242     -0.1868                       <NA>   \n",
              "993                       0.725756   -0.165088                       <NA>   \n",
              "994                       0.778533   -0.151104                       <NA>   \n",
              "1031                      0.381364        <NA>                   0.894462   \n",
              "1207                      0.854759    0.015678                       <NA>   \n",
              "1323                        0.6629    -0.15965                   0.900453   \n",
              "1485                      0.916293    0.009673                       <NA>   \n",
              "1755                      0.746304        <NA>                   0.513372   \n",
              "1756                      0.820182        <NA>                   0.471094   \n",
              "1757                      0.858104        <NA>                   0.357341   \n",
              "1758                      0.758219        <NA>                   0.333832   \n",
              "1842                      0.694853        <NA>                   0.835795   \n",
              "1897                      0.798835        <NA>                   0.710567   \n",
              "1898                      0.818467        <NA>                   0.675439   \n",
              "1899                      0.800192        <NA>                   0.657556   \n",
              "1966                      0.781496   -0.124444                   0.722211   \n",
              "2044                      0.935979        <NA>                       <NA>   \n",
              "\n",
              "      Positive affect  Negative affect  \n",
              "29               <NA>             <NA>  \n",
              "32           0.558359         0.176866  \n",
              "33           0.565026         0.377112  \n",
              "381          0.657659          0.16958  \n",
              "475          0.596187         0.276602  \n",
              "524              <NA>             <NA>  \n",
              "992              <NA>             <NA>  \n",
              "993              <NA>             <NA>  \n",
              "994              <NA>             <NA>  \n",
              "1031         0.613723         0.236699  \n",
              "1207             <NA>             <NA>  \n",
              "1323             <NA>             <NA>  \n",
              "1485             <NA>         0.295164  \n",
              "1755         0.707874         0.112012  \n",
              "1756         0.631947         0.083426  \n",
              "1757         0.690514         0.122244  \n",
              "1758         0.686732         0.152428  \n",
              "1842         0.584111         0.361776  \n",
              "1897         0.743169         0.082737  \n",
              "1898         0.667417          0.12298  \n",
              "1899         0.716587         0.095399  \n",
              "1966             <NA>             <NA>  \n",
              "2044             <NA>         0.291113  "
            ]
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df\n",
        " .loc[df.isna().sum(axis='columns') == 3])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fb7bd5a-bebc-4420-8640-105c0997aca6",
      "metadata": {
        "id": "7fb7bd5a-bebc-4420-8640-105c0997aca6"
      },
      "source": [
        "## When to drop data?\n",
        "\n",
        "Ah, the million-dollar question: When should you use the `.dropna` method?\n",
        "\n",
        "There are textbooks devoted to this subject. To keep this post short and focus on removing data with `.dropna`, I will summarize when we can drop rows: when the *missingness of data* has no relation with any observed or unobserved variables. In simpler terms, the data is missing purely by chance. It's the most straightforward form of missing data to manage because you can generally remove it without biasing your results if it is a small percentage of the data.\n",
        "\n",
        "When should we drop columns? Again, in short, when they don't add value. However, I would not use `.dropna` to remove columns even if I could. I would do exploratory data analysis (EDA), determine which columns I care about, and then use `.loc` to select those columns. (See my previous posts on `.loc` and `.iloc` for details.)\n",
        "\n",
        "Here is a function for determining whether data is *missing completely at random* (MCAR)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d90438e5-6150-4b13-8aeb-5872d40c80a6",
      "metadata": {
        "id": "d90438e5-6150-4b13-8aeb-5872d40c80a6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "829d059c-ba59-46a6-a7ea-b6ce5d82c366",
      "metadata": {
        "id": "829d059c-ba59-46a6-a7ea-b6ce5d82c366"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from scipy.stats import ttest_ind\n",
        "import warnings\n",
        "\n",
        "def is_mcar(df, column, p_value_threshold=0.05):\n",
        "    \"\"\"\n",
        "    Assess whether a column in a DataFrame is Missing Completely At Random (MCAR) using t-tests.\n",
        "\n",
        "    Parameters:\n",
        "    df (pandas.DataFrame): The DataFrame containing the data.\n",
        "    column (str): The column name to assess for MCAR.\n",
        "    p_value_threshold (float): The p-value above which to consider data as potentially MCAR. Default is 0.05.\n",
        "\n",
        "    Returns:\n",
        "    bool: True if the column is likely MCAR, False otherwise.\n",
        "    \"\"\"\n",
        "\n",
        "    # Check if the column has missing values\n",
        "    if df[column].isna().sum() == 0:\n",
        "        warnings.warn(f\"The column '{column}' has no missing values.\")\n",
        "        return False\n",
        "\n",
        "    def ttest_helper(sub_df, column):\n",
        "        mcar = True\n",
        "        for col in sub_df.select_dtypes(include=['number']).columns:\n",
        "            if col == column or col == 'Is_Missing':\n",
        "                continue\n",
        "            group1 = (sub_df\n",
        "                      .loc[sub_df['Is_Missing'] == True, col]\n",
        "                      .dropna()\n",
        "                     )\n",
        "            group2 = (sub_df\n",
        "                      .loc[sub_df['Is_Missing'] == False, col]\n",
        "                      .dropna()\n",
        "                     )\n",
        "            if len(group1) > 1 and len(group2) > 1:\n",
        "                t_stat, p_value = ttest_ind(group1, group2)\n",
        "                if p_value < p_value_threshold:\n",
        "                    mcar = False\n",
        "                    break\n",
        "        return mcar\n",
        "\n",
        "    return (df.assign(Is_Missing=df[column].isna())\n",
        "              .pipe(ttest_helper, column)\n",
        "           )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69ebbd68-abea-474c-bcbe-42148941c874",
      "metadata": {
        "id": "69ebbd68-abea-474c-bcbe-42148941c874"
      },
      "source": [
        "The function `is_mcar` takes a column and use a special test called a \"t-test\" to compare the people with missing scores to those with available scores, but using other numerical information we have from the columns, like health, income, or family support. If the test finds no real difference between the two groups, the missing column scores are probably just random and not tied to any of the other factors we're looking at. In that case, we say this data is \"Missing Completely At Random\" (MCAR).\n",
        "\n",
        "Let's run it on all of the columns:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "216f5fa3-9691-4ed8-95d5-80e1831734a7",
      "metadata": {
        "id": "216f5fa3-9691-4ed8-95d5-80e1831734a7",
        "outputId": "321bc985-3706-4993-866e-505f9e0e19c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "col='Country name' False\n",
            "col='year' False\n",
            "col='Life Ladder' False\n",
            "col='Log GDP per capita' False\n",
            "col='Social support' False\n",
            "col='Healthy life expectancy at birth' False\n",
            "col='Freedom to make life choices' False\n",
            "col='Generosity' False\n",
            "col='Perceptions of corruption' False\n",
            "col='Positive affect' False\n",
            "col='Negative affect' True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/qn/r8_0pgj1645dn1w69vqls6cw0000gn/T/ipykernel_54500/3428414951.py:20: UserWarning: The column 'Country name' has no missing values.\n",
            "  warnings.warn(f\"The column '{column}' has no missing values.\")\n",
            "/var/folders/qn/r8_0pgj1645dn1w69vqls6cw0000gn/T/ipykernel_54500/3428414951.py:20: UserWarning: The column 'year' has no missing values.\n",
            "  warnings.warn(f\"The column '{column}' has no missing values.\")\n",
            "/var/folders/qn/r8_0pgj1645dn1w69vqls6cw0000gn/T/ipykernel_54500/3428414951.py:20: UserWarning: The column 'Life Ladder' has no missing values.\n",
            "  warnings.warn(f\"The column '{column}' has no missing values.\")\n"
          ]
        }
      ],
      "source": [
        "for col in df.columns:\n",
        "    print(f'{col=} {is_mcar(df, col)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "afe7f767-508d-4280-98c7-b3b9382027a0",
      "metadata": {
        "id": "afe7f767-508d-4280-98c7-b3b9382027a0"
      },
      "source": [
        "Because .72% of the *Negative affect* values are missing completely at random, I'm going to remove those rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "154ef172-0396-4d51-8093-bfb943b7f1a9",
      "metadata": {
        "scrolled": true,
        "id": "154ef172-0396-4d51-8093-bfb943b7f1a9",
        "outputId": "5dc0809c-2cae-47b2-9399-4d6a01d8227a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>Country name</th>\n",
              "      <th>year</th>\n",
              "      <th>Life Ladder</th>\n",
              "      <th>Log GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy at birth</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "      <th>Positive affect</th>\n",
              "      <th>Negative affect</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2008</td>\n",
              "      <td>3.723590</td>\n",
              "      <td>7.350416</td>\n",
              "      <td>0.450662</td>\n",
              "      <td>50.500000</td>\n",
              "      <td>0.718114</td>\n",
              "      <td>0.167652</td>\n",
              "      <td>0.881686</td>\n",
              "      <td>0.414297</td>\n",
              "      <td>0.258195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2009</td>\n",
              "      <td>4.401778</td>\n",
              "      <td>7.508646</td>\n",
              "      <td>0.552308</td>\n",
              "      <td>50.799999</td>\n",
              "      <td>0.678896</td>\n",
              "      <td>0.190809</td>\n",
              "      <td>0.850035</td>\n",
              "      <td>0.481421</td>\n",
              "      <td>0.237092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2010</td>\n",
              "      <td>4.758381</td>\n",
              "      <td>7.613900</td>\n",
              "      <td>0.539075</td>\n",
              "      <td>51.099998</td>\n",
              "      <td>0.600127</td>\n",
              "      <td>0.121316</td>\n",
              "      <td>0.706766</td>\n",
              "      <td>0.516907</td>\n",
              "      <td>0.275324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2011</td>\n",
              "      <td>3.831719</td>\n",
              "      <td>7.581259</td>\n",
              "      <td>0.521104</td>\n",
              "      <td>51.400002</td>\n",
              "      <td>0.495901</td>\n",
              "      <td>0.163571</td>\n",
              "      <td>0.731109</td>\n",
              "      <td>0.479835</td>\n",
              "      <td>0.267175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2012</td>\n",
              "      <td>3.782938</td>\n",
              "      <td>7.660506</td>\n",
              "      <td>0.520637</td>\n",
              "      <td>51.700001</td>\n",
              "      <td>0.530935</td>\n",
              "      <td>0.237588</td>\n",
              "      <td>0.775620</td>\n",
              "      <td>0.613513</td>\n",
              "      <td>0.267919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2194</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>3.616480</td>\n",
              "      <td>7.783066</td>\n",
              "      <td>0.775388</td>\n",
              "      <td>52.625000</td>\n",
              "      <td>0.762675</td>\n",
              "      <td>-0.051219</td>\n",
              "      <td>0.844209</td>\n",
              "      <td>0.657524</td>\n",
              "      <td>0.211726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2195</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2019</td>\n",
              "      <td>2.693523</td>\n",
              "      <td>7.697755</td>\n",
              "      <td>0.759162</td>\n",
              "      <td>53.099998</td>\n",
              "      <td>0.631908</td>\n",
              "      <td>-0.047464</td>\n",
              "      <td>0.830652</td>\n",
              "      <td>0.658434</td>\n",
              "      <td>0.235354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2196</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2020</td>\n",
              "      <td>3.159802</td>\n",
              "      <td>7.596050</td>\n",
              "      <td>0.717243</td>\n",
              "      <td>53.575001</td>\n",
              "      <td>0.643303</td>\n",
              "      <td>0.006313</td>\n",
              "      <td>0.788523</td>\n",
              "      <td>0.660658</td>\n",
              "      <td>0.345736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2197</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021</td>\n",
              "      <td>3.154578</td>\n",
              "      <td>7.656878</td>\n",
              "      <td>0.685151</td>\n",
              "      <td>54.049999</td>\n",
              "      <td>0.667636</td>\n",
              "      <td>-0.075575</td>\n",
              "      <td>0.756945</td>\n",
              "      <td>0.609917</td>\n",
              "      <td>0.241682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2198</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2022</td>\n",
              "      <td>3.296220</td>\n",
              "      <td>7.670123</td>\n",
              "      <td>0.666172</td>\n",
              "      <td>54.525002</td>\n",
              "      <td>0.651987</td>\n",
              "      <td>-0.069513</td>\n",
              "      <td>0.752632</td>\n",
              "      <td>0.640609</td>\n",
              "      <td>0.191350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2183 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Country name  year  Life Ladder  Log GDP per capita  Social support  \\\n",
              "0     Afghanistan  2008     3.723590            7.350416        0.450662   \n",
              "1     Afghanistan  2009     4.401778            7.508646        0.552308   \n",
              "2     Afghanistan  2010     4.758381            7.613900        0.539075   \n",
              "3     Afghanistan  2011     3.831719            7.581259        0.521104   \n",
              "4     Afghanistan  2012     3.782938            7.660506        0.520637   \n",
              "...           ...   ...          ...                 ...             ...   \n",
              "2194     Zimbabwe  2018     3.616480            7.783066        0.775388   \n",
              "2195     Zimbabwe  2019     2.693523            7.697755        0.759162   \n",
              "2196     Zimbabwe  2020     3.159802            7.596050        0.717243   \n",
              "2197     Zimbabwe  2021     3.154578            7.656878        0.685151   \n",
              "2198     Zimbabwe  2022     3.296220            7.670123        0.666172   \n",
              "\n",
              "      Healthy life expectancy at birth  Freedom to make life choices  \\\n",
              "0                            50.500000                      0.718114   \n",
              "1                            50.799999                      0.678896   \n",
              "2                            51.099998                      0.600127   \n",
              "3                            51.400002                      0.495901   \n",
              "4                            51.700001                      0.530935   \n",
              "...                                ...                           ...   \n",
              "2194                         52.625000                      0.762675   \n",
              "2195                         53.099998                      0.631908   \n",
              "2196                         53.575001                      0.643303   \n",
              "2197                         54.049999                      0.667636   \n",
              "2198                         54.525002                      0.651987   \n",
              "\n",
              "      Generosity  Perceptions of corruption  Positive affect  Negative affect  \n",
              "0       0.167652                   0.881686         0.414297         0.258195  \n",
              "1       0.190809                   0.850035         0.481421         0.237092  \n",
              "2       0.121316                   0.706766         0.516907         0.275324  \n",
              "3       0.163571                   0.731109         0.479835         0.267175  \n",
              "4       0.237588                   0.775620         0.613513         0.267919  \n",
              "...          ...                        ...              ...              ...  \n",
              "2194   -0.051219                   0.844209         0.657524         0.211726  \n",
              "2195   -0.047464                   0.830652         0.658434         0.235354  \n",
              "2196    0.006313                   0.788523         0.660658         0.345736  \n",
              "2197   -0.075575                   0.756945         0.609917         0.241682  \n",
              "2198   -0.069513                   0.752632         0.640609         0.191350  \n",
              "\n",
              "[2183 rows x 11 columns]"
            ]
          },
          "execution_count": 174,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df\n",
        " .dropna(subset='Negative affect')\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1658e1cc-18fe-45db-8a2a-62050550afaf",
      "metadata": {
        "id": "1658e1cc-18fe-45db-8a2a-62050550afaf"
      },
      "source": [
        "Let's redo the plot with the cleaned up data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0a5e5b2-ff56-4387-8040-ef3f72009de0",
      "metadata": {
        "scrolled": true,
        "id": "c0a5e5b2-ff56-4387-8040-ef3f72009de0",
        "outputId": "9d40647d-2c6f-4dd7-e061-8933be72aa14"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x178364820>"
            ]
          },
          "execution_count": 182,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "(df\n",
        " .dropna(subset='Negative affect')\n",
        " .assign(Continent=df['Country name'].apply(get_continent))\n",
        " .groupby(['year', 'Continent'])\n",
        " ['Negative affect']\n",
        " .median()\n",
        " .unstack()\n",
        " .plot()\n",
        " .legend(bbox_to_anchor=(1,1))\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc4b8e94-12db-4f18-8e34-1ebff1161f19",
      "metadata": {
        "id": "fc4b8e94-12db-4f18-8e34-1ebff1161f19"
      },
      "source": [
        "There is still a gap in the *Oceania* line. Let's explore that and see the rows with missing values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8acc370f-446b-4f14-bf1d-2ee690901b9f",
      "metadata": {
        "id": "8acc370f-446b-4f14-bf1d-2ee690901b9f",
        "outputId": "ba3eaf3f-878b-4faa-f58d-aba7f36179ae"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>Continent</th>\n",
              "      <th>Africa</th>\n",
              "      <th>Asia</th>\n",
              "      <th>Europe</th>\n",
              "      <th>North America</th>\n",
              "      <th>Oceania</th>\n",
              "      <th>South America</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>0.345555</td>\n",
              "      <td>0.241056</td>\n",
              "      <td>0.259164</td>\n",
              "      <td>0.226111</td>\n",
              "      <td>0.238012</td>\n",
              "      <td>0.267397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>0.225375</td>\n",
              "      <td>0.239390</td>\n",
              "      <td>0.241685</td>\n",
              "      <td>0.248030</td>\n",
              "      <td>0.218773</td>\n",
              "      <td>0.328230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>0.227878</td>\n",
              "      <td>0.233697</td>\n",
              "      <td>0.239325</td>\n",
              "      <td>0.240080</td>\n",
              "      <td>0.226674</td>\n",
              "      <td>0.291755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0.239573</td>\n",
              "      <td>0.237846</td>\n",
              "      <td>0.217813</td>\n",
              "      <td>0.229474</td>\n",
              "      <td>0.225154</td>\n",
              "      <td>0.295163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>0.191887</td>\n",
              "      <td>0.237092</td>\n",
              "      <td>0.249777</td>\n",
              "      <td>0.245432</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.264550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>0.184129</td>\n",
              "      <td>0.233014</td>\n",
              "      <td>0.240364</td>\n",
              "      <td>0.235618</td>\n",
              "      <td>0.227416</td>\n",
              "      <td>0.240530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>0.229093</td>\n",
              "      <td>0.237561</td>\n",
              "      <td>0.256577</td>\n",
              "      <td>0.269225</td>\n",
              "      <td>0.202737</td>\n",
              "      <td>0.269106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>0.221627</td>\n",
              "      <td>0.231071</td>\n",
              "      <td>0.245632</td>\n",
              "      <td>0.278111</td>\n",
              "      <td>0.210638</td>\n",
              "      <td>0.287592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>0.241231</td>\n",
              "      <td>0.269507</td>\n",
              "      <td>0.252374</td>\n",
              "      <td>0.283281</td>\n",
              "      <td>0.16427</td>\n",
              "      <td>0.271086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>0.268049</td>\n",
              "      <td>0.260893</td>\n",
              "      <td>0.237620</td>\n",
              "      <td>0.299388</td>\n",
              "      <td>0.222162</td>\n",
              "      <td>0.274822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>0.266510</td>\n",
              "      <td>0.298386</td>\n",
              "      <td>0.234048</td>\n",
              "      <td>0.295253</td>\n",
              "      <td>0.184733</td>\n",
              "      <td>0.314151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>0.324739</td>\n",
              "      <td>0.263090</td>\n",
              "      <td>0.230485</td>\n",
              "      <td>0.281422</td>\n",
              "      <td>0.22175</td>\n",
              "      <td>0.306865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>0.358310</td>\n",
              "      <td>0.286023</td>\n",
              "      <td>0.219717</td>\n",
              "      <td>0.268269</td>\n",
              "      <td>0.198539</td>\n",
              "      <td>0.303513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>0.358828</td>\n",
              "      <td>0.292183</td>\n",
              "      <td>0.228276</td>\n",
              "      <td>0.287358</td>\n",
              "      <td>0.177704</td>\n",
              "      <td>0.328171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>0.342163</td>\n",
              "      <td>0.284670</td>\n",
              "      <td>0.227838</td>\n",
              "      <td>0.271475</td>\n",
              "      <td>0.196683</td>\n",
              "      <td>0.337148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>0.327903</td>\n",
              "      <td>0.294512</td>\n",
              "      <td>0.251109</td>\n",
              "      <td>0.310301</td>\n",
              "      <td>0.206809</td>\n",
              "      <td>0.362144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>0.340848</td>\n",
              "      <td>0.273718</td>\n",
              "      <td>0.252777</td>\n",
              "      <td>0.283584</td>\n",
              "      <td>0.220745</td>\n",
              "      <td>0.358491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022</th>\n",
              "      <td>0.362231</td>\n",
              "      <td>0.241539</td>\n",
              "      <td>0.226395</td>\n",
              "      <td>0.271906</td>\n",
              "      <td>0.227067</td>\n",
              "      <td>0.298878</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Continent    Africa      Asia    Europe  North America   Oceania  \\\n",
              "year                                                               \n",
              "2005       0.345555  0.241056  0.259164       0.226111  0.238012   \n",
              "2006       0.225375  0.239390  0.241685       0.248030  0.218773   \n",
              "2007       0.227878  0.233697  0.239325       0.240080  0.226674   \n",
              "2008       0.239573  0.237846  0.217813       0.229474  0.225154   \n",
              "2009       0.191887  0.237092  0.249777       0.245432      <NA>   \n",
              "2010       0.184129  0.233014  0.240364       0.235618  0.227416   \n",
              "2011       0.229093  0.237561  0.256577       0.269225  0.202737   \n",
              "2012       0.221627  0.231071  0.245632       0.278111  0.210638   \n",
              "2013       0.241231  0.269507  0.252374       0.283281   0.16427   \n",
              "2014       0.268049  0.260893  0.237620       0.299388  0.222162   \n",
              "2015       0.266510  0.298386  0.234048       0.295253  0.184733   \n",
              "2016       0.324739  0.263090  0.230485       0.281422   0.22175   \n",
              "2017       0.358310  0.286023  0.219717       0.268269  0.198539   \n",
              "2018       0.358828  0.292183  0.228276       0.287358  0.177704   \n",
              "2019       0.342163  0.284670  0.227838       0.271475  0.196683   \n",
              "2020       0.327903  0.294512  0.251109       0.310301  0.206809   \n",
              "2021       0.340848  0.273718  0.252777       0.283584  0.220745   \n",
              "2022       0.362231  0.241539  0.226395       0.271906  0.227067   \n",
              "\n",
              "Continent  South America  \n",
              "year                      \n",
              "2005            0.267397  \n",
              "2006            0.328230  \n",
              "2007            0.291755  \n",
              "2008            0.295163  \n",
              "2009            0.264550  \n",
              "2010            0.240530  \n",
              "2011            0.269106  \n",
              "2012            0.287592  \n",
              "2013            0.271086  \n",
              "2014            0.274822  \n",
              "2015            0.314151  \n",
              "2016            0.306865  \n",
              "2017            0.303513  \n",
              "2018            0.328171  \n",
              "2019            0.337148  \n",
              "2020            0.362144  \n",
              "2021            0.358491  \n",
              "2022            0.298878  "
            ]
          },
          "execution_count": 189,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df\n",
        " .dropna(subset='Negative affect')\n",
        " .assign(Continent=df['Country name'].apply(get_continent))\n",
        " .groupby(['year', 'Continent'])\n",
        " ['Negative affect']\n",
        " .median()\n",
        " .unstack()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "faaeed87-04af-4ee9-acdc-9d1c3b428a3d",
      "metadata": {
        "id": "faaeed87-04af-4ee9-acdc-9d1c3b428a3d"
      },
      "source": [
        "It looks like data is missing from 2009. Let's look at the non-aggregated values from Oceania from 2009:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d14ad500-8bd5-4763-a434-68b5cbf76270",
      "metadata": {
        "id": "d14ad500-8bd5-4763-a434-68b5cbf76270",
        "outputId": "b1c033af-1e96-4399-f644-2de3cb9d0a4e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/qn/r8_0pgj1645dn1w69vqls6cw0000gn/T/ipykernel_54500/2886390756.py:4: RuntimeWarning: Engine has switched to 'python' because numexpr does not support extension array dtypes. Please set your engine to python manually.\n",
            "  .query('Continent == \"Oceania\" and 2008 <= year <= 2010')\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>Country name</th>\n",
              "      <th>year</th>\n",
              "      <th>Life Ladder</th>\n",
              "      <th>Log GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy at birth</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "      <th>Positive affect</th>\n",
              "      <th>Negative affect</th>\n",
              "      <th>Continent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Australia</td>\n",
              "      <td>2008</td>\n",
              "      <td>7.253757</td>\n",
              "      <td>10.709456</td>\n",
              "      <td>0.946635</td>\n",
              "      <td>70.040001</td>\n",
              "      <td>0.915733</td>\n",
              "      <td>0.301722</td>\n",
              "      <td>0.430811</td>\n",
              "      <td>0.728992</td>\n",
              "      <td>0.218427</td>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Australia</td>\n",
              "      <td>2010</td>\n",
              "      <td>7.450047</td>\n",
              "      <td>10.713649</td>\n",
              "      <td>0.954520</td>\n",
              "      <td>70.199997</td>\n",
              "      <td>0.932059</td>\n",
              "      <td>0.313121</td>\n",
              "      <td>0.366127</td>\n",
              "      <td>0.761716</td>\n",
              "      <td>0.220073</td>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1398</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>2008</td>\n",
              "      <td>7.381171</td>\n",
              "      <td>10.541654</td>\n",
              "      <td>0.944275</td>\n",
              "      <td>69.760002</td>\n",
              "      <td>0.893072</td>\n",
              "      <td>0.292630</td>\n",
              "      <td>0.333751</td>\n",
              "      <td>0.784192</td>\n",
              "      <td>0.231881</td>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1399</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>2010</td>\n",
              "      <td>7.223756</td>\n",
              "      <td>10.534310</td>\n",
              "      <td>0.975642</td>\n",
              "      <td>69.800003</td>\n",
              "      <td>0.917753</td>\n",
              "      <td>0.249289</td>\n",
              "      <td>0.320748</td>\n",
              "      <td>0.782821</td>\n",
              "      <td>0.234758</td>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Country name  year  Life Ladder  Log GDP per capita  Social support  \\\n",
              "78      Australia  2008     7.253757           10.709456        0.946635   \n",
              "79      Australia  2010     7.450047           10.713649        0.954520   \n",
              "1398  New Zealand  2008     7.381171           10.541654        0.944275   \n",
              "1399  New Zealand  2010     7.223756           10.534310        0.975642   \n",
              "\n",
              "      Healthy life expectancy at birth  Freedom to make life choices  \\\n",
              "78                           70.040001                      0.915733   \n",
              "79                           70.199997                      0.932059   \n",
              "1398                         69.760002                      0.893072   \n",
              "1399                         69.800003                      0.917753   \n",
              "\n",
              "      Generosity  Perceptions of corruption  Positive affect  Negative affect  \\\n",
              "78      0.301722                   0.430811         0.728992         0.218427   \n",
              "79      0.313121                   0.366127         0.761716         0.220073   \n",
              "1398    0.292630                   0.333751         0.784192         0.231881   \n",
              "1399    0.249289                   0.320748         0.782821         0.234758   \n",
              "\n",
              "     Continent  \n",
              "78     Oceania  \n",
              "79     Oceania  \n",
              "1398   Oceania  \n",
              "1399   Oceania  "
            ]
          },
          "execution_count": 191,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df\n",
        " .dropna(subset='Negative affect')\n",
        " .assign(Continent=df['Country name'].apply(get_continent))\n",
        " .query('Continent == \"Oceania\" and 2008 <= year <= 2010')\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5f9f7859-e3bb-490f-866a-4518f62e920f",
      "metadata": {
        "id": "5f9f7859-e3bb-490f-866a-4518f62e920f"
      },
      "source": [
        "The rows for 2009 are completely missing for Australia and New Zealand. I'm not a subject matter expert on happiness, but since this is my blog post, I will make an executive decision and use the value from 2008 for 2009. The values surrounding it look close enough. We call this a *forward fill* because we will push the last known value forward. (A quick aside: if you are doing machine learning with time series, this is a popular choice for dealing with missing values because it doesn't leak information from the future.)\n",
        "\n",
        "Here's the plot with the values filled in:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "114b4edb-a343-4be7-aa73-6d9add55ad4f",
      "metadata": {
        "scrolled": true,
        "id": "114b4edb-a343-4be7-aa73-6d9add55ad4f",
        "outputId": "a81888fd-6bb6-4df7-f0a8-9dd8485639e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x17a952470>"
            ]
          },
          "execution_count": 195,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "(df\n",
        " .dropna(subset='Negative affect')\n",
        " .assign(Continent=df['Country name'].apply(get_continent))\n",
        " .groupby(['year', 'Continent'])\n",
        " ['Negative affect']\n",
        " .median()\n",
        " .unstack()\n",
        " .assign(Oceania=lambda df_:df_.Oceania.ffill())\n",
        " .plot()\n",
        " .legend(bbox_to_anchor=(1,1))\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9023f66-8271-4ebb-8791-160f383e3d16",
      "metadata": {
        "id": "c9023f66-8271-4ebb-8791-160f383e3d16"
      },
      "source": [
        "This plot is ok. However, I want to fix the labels, and draw focus to the Europe values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2552d296-9334-4106-ad14-6007727b5143",
      "metadata": {
        "id": "2552d296-9334-4106-ad14-6007727b5143",
        "outputId": "ed9ca5d3-3d97-482e-93a8-c21ae6490198"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'negative affect')"
            ]
          },
          "execution_count": 196,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "\n",
        "column_colors = []\n",
        "\n",
        "def get_column_names_and_set_colors(df):\n",
        "    global column_colors\n",
        "    for col in df.columns:\n",
        "        if col == 'Europe':\n",
        "            column_colors.append('red')\n",
        "        else:\n",
        "            column_colors.append('grey')\n",
        "\n",
        "    return df\n",
        "\n",
        "ax = (df\n",
        "      .dropna(subset=['Negative affect'])\n",
        "      .assign(Continent=df['Country name'].apply(get_continent))\n",
        "      .groupby(['year', 'Continent'])\n",
        "      ['Negative affect']\n",
        "      .median()\n",
        "      .unstack()\n",
        "      .pipe(get_column_names_and_set_colors)\n",
        "      .assign(Oceania=lambda df_:df_.Oceania.ffill())\n",
        "      .plot(linewidth=2, color=column_colors)\n",
        ")\n",
        "\n",
        "# Set x-axis to display whole numbers only\n",
        "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "\n",
        "# Move the legend box outside of the plot area\n",
        "ax.legend(bbox_to_anchor=(1, 1))\n",
        "ax.set_title('Median Negative affect in European Countries')\n",
        "ax.set_ylabel('negative affect')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "71fde198-c60e-4438-9cc0-fb687f5f0b86",
      "metadata": {
        "id": "71fde198-c60e-4438-9cc0-fb687f5f0b86"
      },
      "source": [
        "That looks better. The *negative affect* in Europe has held pretty constant while other continents have seen it rising since 2010."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30f01ea1-fed0-4f96-aa37-2a77b1af88b4",
      "metadata": {
        "id": "30f01ea1-fed0-4f96-aa37-2a77b1af88b4"
      },
      "source": [
        "## Dropping Entirely Empty Rows\n",
        "\n",
        "Let me show you another place .dropna comes in useful.",
        "\n",
        "Imagine you've just downloaded an exciting new dataset that you can't wait to dive into. You load it into a DataFrame, only to find several empty rows at the end. All fields are missing or set to a 'null' value in these rows. I find this occasionally happens to me.\n",
        "\n",
        "Here is a toy example:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57fc3677-fbec-4812-ba56-631978b5c08e",
      "metadata": {
        "scrolled": true,
        "id": "57fc3677-fbec-4812-ba56-631978b5c08e",
        "outputId": "5a92bca1-fcfe-4cda-e057-c537675dc3c0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>Age</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alice</td>\n",
              "      <td>34.0</td>\n",
              "      <td>50000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bob</td>\n",
              "      <td>45.0</td>\n",
              "      <td>75000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cathy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dave</td>\n",
              "      <td>31.0</td>\n",
              "      <td>60000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name   Age    Income\n",
              "0  Alice  34.0   50000.0\n",
              "1    Bob  45.0   75000.0\n",
              "2  Cathy   NaN  120000.0\n",
              "3   Dave  31.0   60000.0\n",
              "4    NaN   NaN       NaN\n",
              "5    NaN   NaN       NaN\n",
              "6    NaN   NaN       NaN"
            ]
          },
          "execution_count": 210,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from io import StringIO\n",
        "\n",
        "csv_string = \"\"\"Name,Age,Income\n",
        "Alice,34,50000\n",
        "Bob,45,75000\n",
        "Cathy,,120000\n",
        "Dave,31,60000\n",
        ",,\n",
        ",,\n",
        ",,\"\"\"\n",
        "\n",
        "\n",
        "df_empty = pd.read_csv(StringIO(csv_string))\n",
        "df_empty"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f0d0cef-1b9b-4726-b9cc-1fb9e55e1d08",
      "metadata": {
        "id": "6f0d0cef-1b9b-4726-b9cc-1fb9e55e1d08"
      },
      "source": [
        "If my data had a bunch of missing rows, I would use `.dropna` but with a specific incantation, specifying `how='all'`. This only removes rows if every cell is missing. Note that *Cathy* was missing *Age* but not dropped:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1648915b-c4c9-49c9-bff8-c0bab2ee65b1",
      "metadata": {
        "id": "1648915b-c4c9-49c9-bff8-c0bab2ee65b1",
        "outputId": "f1463d0a-8b07-4328-e48d-ed802f7e2ea0"
      },
      "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>Age</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alice</td>\n",
              "      <td>34.0</td>\n",
              "      <td>50000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bob</td>\n",
              "      <td>45.0</td>\n",
              "      <td>75000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cathy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dave</td>\n",
              "      <td>31.0</td>\n",
              "      <td>60000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name   Age    Income\n",
              "0  Alice  34.0   50000.0\n",
              "1    Bob  45.0   75000.0\n",
              "2  Cathy   NaN  120000.0\n",
              "3   Dave  31.0   60000.0"
            ]
          },
          "execution_count": 211,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_empty.dropna(how='all')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2975799e-63a9-458e-9199-0ca72f09a436",
      "metadata": {
        "id": "2975799e-63a9-458e-9199-0ca72f09a436"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "Handling missing data is a crucial part of the data preprocessing pipeline, and Pandas's `.dropna` method offers a flexible way to manage such data. Understanding the nature of your missing values is essential for making informed decisions. The `.dropna` method is a blunt tool; you must be careful before dropping values. Remember, each dataset has challenges, and there's no one-size-fits-all solution. Best of luck in keeping around data that sparks joy!\n",
        "\n",
        "If you enjoyed this article, please share it on social media. Also, check out the other posts from this series!"
      ]
    }
  ],
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