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Data-Science-For-Beginners/translations/zh-CN/2-Working-With-Data/08-data-preparation/notebook.ipynb

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{
"cells": [
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"cell_type": "markdown",
"metadata": {
"id": "rQ8UhzFpgRra"
},
"source": [
"# 数据准备\n",
"\n",
"[原始笔记本来源于 *Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio by Lee Stott*](https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb)\n",
"\n",
"## 探索 `DataFrame` 信息\n",
"\n",
"> **学习目标：** 在本小节结束时，您应该能够熟练地查找存储在 pandas DataFrame 中的数据的一般信息。\n",
"\n",
"当您将数据加载到 pandas 中时，它很可能会以 `DataFrame` 的形式存在。然而，如果您的 `DataFrame` 数据集有 60,000 行和 400 列，您该如何开始了解您正在处理的数据呢？幸运的是，pandas 提供了一些方便的工具，可以快速查看 `DataFrame` 的整体信息，以及前几行和后几行数据。\n",
"\n",
"为了探索这些功能，我们将导入 Python 的 scikit-learn 库，并使用一个每位数据科学家都见过无数次的经典数据集：英国生物学家 Ronald Fisher 在其 1936 年论文《多重测量在分类问题中的应用》中使用的 *Iris* 数据集：\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"id": "hB1RofhdgRrp",
"trusted": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.datasets import load_iris\n",
"\n",
"iris = load_iris()\n",
"iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AGA0A_Y8hMdz"
},
"source": [
"### `DataFrame.shape`\n",
"我们已经将鸢尾花数据集加载到变量 `iris_df` 中。在深入分析数据之前，了解我们拥有的数据点数量以及数据集的整体规模是非常有价值的。查看我们正在处理的数据量是很有帮助的。\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LOe5jQohhulf",
"outputId": "fb0577ac-3b4a-4623-cb41-20e1b264b3e9"
},
"outputs": [
{
"data": {
"text/plain": [
"(150, 4)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "smE7AGzOhxk2"
},
"source": [
"我们正在处理包含150行和4列的数据。每一行代表一个数据点，每一列代表与数据框相关的一个特征。基本上，这里有150个数据点，每个数据点包含4个特征。\n",
"\n",
"`shape` 是数据框的一个属性，而不是一个函数，这就是为什么它没有以一对括号结尾。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d3AZKs0PinGP"
},
"source": [
"### `DataFrame.columns`\n",
"现在让我们来看数据的4列。每一列具体代表什么？`columns`属性将为我们提供数据框中列的名称。\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YPGh_ziji-CY",
"outputId": "74e7a43a-77cc-4c80-da56-7f50767c37a0"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
" 'petal width (cm)'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TsobcU_VjCC_"
},
"source": [
"正如我们所看到的，有四（4）列。`columns`属性告诉我们列的名称，基本上没有其他信息。当我们想要识别数据集包含的特征时，这个属性显得尤为重要。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2UTlvkjmgRrs"
},
"source": [
"### `DataFrame.info`\n",
"通过 `shape` 属性提供的数据量以及通过 `columns` 属性提供的特征或列名，可以让我们对数据集有一些了解。现在，我们希望更深入地探索数据集。`DataFrame.info()` 函数在这方面非常有用。\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dHHRyG0_gRrt",
"outputId": "d8fb0c40-4f18-4e19-da48-c8db77d1d3a5",
"trusted": false
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 150 entries, 0 to 149\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 sepal length (cm) 150 non-null float64\n",
" 1 sepal width (cm) 150 non-null float64\n",
" 2 petal length (cm) 150 non-null float64\n",
" 3 petal width (cm) 150 non-null float64\n",
"dtypes: float64(4)\n",
"memory usage: 4.8 KB\n"
]
}
],
"source": [
"iris_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1XgVMpvigRru"
},
"source": [
"从这里，我们可以做出以下几点观察：\n",
"1. 每列的数据类型：在这个数据集中，所有数据都存储为64位浮点数。\n",
"2. 非空值的数量：处理空值是数据准备中的重要步骤。这将在后续的笔记本中处理。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IYlyxbpWFEF4"
},
"source": [
"### DataFrame.describe()\n",
"假设我们的数据集中有大量的数值数据。可以对每一列单独进行单变量统计计算，例如均值、中位数、四分位数等。`DataFrame.describe()`函数为我们提供了数据集中数值列的统计摘要。\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"id": "tWV-CMstFIRA",
"outputId": "4fc49941-bc13-4b0c-a412-cb39e7d3f289"
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"outputs": [
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"<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",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.843333</td>\n",
" <td>3.057333</td>\n",
" <td>3.758000</td>\n",
" <td>1.199333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.828066</td>\n",
" <td>0.435866</td>\n",
" <td>1.765298</td>\n",
" <td>0.762238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.300000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.100000</td>\n",
" <td>2.800000</td>\n",
" <td>1.600000</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.800000</td>\n",
" <td>3.000000</td>\n",
" <td>4.350000</td>\n",
" <td>1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.400000</td>\n",
" <td>3.300000</td>\n",
" <td>5.100000</td>\n",
" <td>1.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>7.900000</td>\n",
" <td>4.400000</td>\n",
" <td>6.900000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"count 150.000000 150.000000 150.000000 150.000000\n",
"mean 5.843333 3.057333 3.758000 1.199333\n",
"std 0.828066 0.435866 1.765298 0.762238\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.300000\n",
"50% 5.800000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zjjtW5hPGMuM"
},
"source": [
"上面的输出显示了每列的总数据点数、均值、标准差、最小值、下四分位数(25%)、中位数(50%)、上四分位数(75%)和最大值。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-lviAu99gRrv"
},
"source": [
"### `DataFrame.head`\n",
"通过以上所有函数和属性，我们已经对数据集有了一个总体的了解。我们知道数据点的数量、特征的数量、每个特征的数据类型以及每个特征的非空值数量。\n",
"\n",
"现在是时候查看数据本身了。让我们看看我们的 `DataFrame` 的前几行（前几个数据点）是什么样子的：\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "DZMJZh0OgRrw",
"outputId": "d9393ee5-c106-4797-f815-218f17160e00",
"trusted": false
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"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" </tbody>\n",
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"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EBHEimZuEFQK"
},
"source": [
"在这里的输出中，我们可以看到数据集的五(5)条记录。如果查看左侧的索引，我们会发现这些是前五行。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oj7GkrTdgRry"
},
"source": [
"### 练习：\n",
"\n",
"从上面的例子可以看出，默认情况下，`DataFrame.head` 会返回 `DataFrame` 的前五行。在下面的代码单元中，你能找到一种方法来显示超过五行吗？\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"id": "EKRmRFFegRrz",
"trusted": false
},
"outputs": [],
"source": [
"# Hint: Consult the documentation by using iris_df.head?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BJ_cpZqNgRr1"
},
"source": [
"### `DataFrame.tail`\n",
"另一种查看数据的方法是从末尾开始（而不是开头）。`DataFrame.head` 的反面是 `DataFrame.tail`，它返回 `DataFrame` 的最后五行：\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "heanjfGWgRr2",
"outputId": "6ae09a21-fe09-4110-b0d7-1a1fbf34d7f3",
"trusted": false
},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
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" <th>145</th>\n",
" <td>6.7</td>\n",
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" <td>5.2</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"145 6.7 3.0 5.2 2.3\n",
"146 6.3 2.5 5.0 1.9\n",
"147 6.5 3.0 5.2 2.0\n",
"148 6.2 3.4 5.4 2.3\n",
"149 5.9 3.0 5.1 1.8"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "31kBWfyLgRr3"
},
"source": [
"在实际操作中，能够轻松查看 `DataFrame` 的前几行或后几行非常有用，尤其是在检查有序数据集中的异常值时。\n",
"\n",
"上面通过代码示例展示的所有函数和属性，都能帮助我们了解数据的整体情况。\n",
"\n",
"> **要点:** 仅仅通过查看 `DataFrame` 的元数据或其中的前几行和后几行数据，就可以快速了解所处理数据的大小、形状和内容。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TvurZyLSDxq_"
},
"source": [
"### 缺失数据\n",
"让我们深入了解缺失数据。缺失数据是指某些列中没有存储任何值。\n",
"\n",
"举个例子：假设某人对自己的体重很在意，因此在调查中没有填写体重字段。那么，这个人的体重值就会缺失。\n",
"\n",
"在现实世界的数据集中，缺失值是非常常见的。\n",
"\n",
"**Pandas如何处理缺失数据**\n",
"\n",
"Pandas处理缺失值有两种方式。第一种方式你在之前的章节中已经见过：`NaN`，即“非数字”（Not a Number）。这是一个特殊值，属于IEEE浮点数规范的一部分，仅用于表示缺失的浮点值。\n",
"\n",
"对于非浮点类型的缺失值，Pandas使用Python的`None`对象。虽然遇到两种不同类型的值来表示同样的含义可能会让人感到困惑，但这种设计选择有其合理的编程原因。在实际应用中，这种方式使Pandas能够在绝大多数情况下提供一个良好的折中方案。尽管如此，`None`和`NaN`都存在一些限制，需要注意它们的使用方式。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lOHqUlZFgRr5"
},
"source": [
"### `None`：非浮点型缺失数据\n",
"由于 `None` 来源于 Python，它不能用于数据类型不是 `'object'` 的 NumPy 和 pandas 数组。请记住，NumPy 数组（以及 pandas 中的数据结构）只能包含一种数据类型。这赋予了它们在大规模数据和计算工作中的强大能力，但也限制了它们的灵活性。这类数组必须提升为“最低公分母”，即能够包含数组中所有内容的数据类型。当数组中包含 `None` 时，这意味着你正在处理 Python 对象。\n",
"\n",
"为了更直观地理解这一点，请看以下示例数组（注意它的 `dtype`）：\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QIoNdY4ngRr7",
"outputId": "92779f18-62f4-4a03-eca2-e9a101604336",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, None, 6, 8], dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"example1 = np.array([2, None, 6, 8])\n",
"example1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pdlgPNbhgRr7"
},
"source": [
"数据类型向上转换的现实带来了两个副作用。首先，操作将在解释型 Python 代码的层面上进行，而不是编译型 NumPy 代码的层面上。基本上，这意味着任何涉及包含 `None` 的 `Series` 或 `DataFrame` 的操作都会变得更慢。虽然你可能不会注意到这种性能下降，但对于大型数据集来说，这可能会成为一个问题。\n",
"\n",
"第二个副作用源于第一个副作用。由于 `None` 本质上将 `Series` 或 `DataFrame` 拉回到原生 Python 的世界，因此在包含 `None` 值的数组上使用 NumPy/pandas 的聚合函数（如 `sum()` 或 `min()`）通常会产生错误：\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
},
"id": "gWbx-KB9gRr8",
"outputId": "ecba710a-22ec-41d5-a39c-11f67e645b50",
"trusted": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-ce9901ad18bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexample1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 45\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m 46\u001b[0m initial=_NoValue, where=True):\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
]
}
],
"source": [
"example1.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LcEwO8UogRr9"
},
"source": [
"**关键点**：整数与`None`值之间的加法（以及其他操作）是未定义的，这可能会限制您对包含它们的数据集的操作。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pWvVHvETgRr9"
},
"source": [
"### `NaN`：缺失的浮点值\n",
"\n",
"与 `None` 不同，NumPy（因此也包括 pandas）支持 `NaN`，以便进行快速的矢量化操作和 ufuncs。坏消息是，任何对 `NaN` 进行的算术运算结果都会是 `NaN`。例如：\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rcFYfMG9gRr9",
"outputId": "699e81b7-5c11-4b46-df1d-06071768690f",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nan + 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BW3zQD2-gRr-",
"outputId": "4525b6c4-495d-4f7b-a979-efce1dae9bd0",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nan * 0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fU5IPRcCgRr-"
},
"source": [
"好消息：在包含 `NaN` 的数组上运行聚合不会出现错误。坏消息：结果并不总是有用：\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LCInVgSSgRr_",
"outputId": "fa06495a-0930-4867-87c5-6023031ea8b5",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"(nan, nan, nan)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example2 = np.array([2, np.nan, 6, 8]) \n",
"example2.sum(), example2.min(), example2.max()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nhlnNJT7gRr_"
},
"source": [
"### 练习:\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
"id": "yan3QRaOgRr_",
"trusted": false
},
"outputs": [],
"source": [
"# What happens if you add np.nan and None together?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_iDvIRC8gRsA"
},
"source": [
"记住：`NaN`仅用于表示缺失的浮点值；整数、字符串或布尔值没有对应的`NaN`。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kj6EKdsAgRsA"
},
"source": [
"### `NaN` 和 `None`：pandas 中的空值\n",
"\n",
"尽管 `NaN` 和 `None` 的行为可能略有不同，但 pandas 仍然能够将它们互换处理。为了更好地理解这一点，来看一个整数的 `Series`：\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Nji-KGdNgRsA",
"outputId": "36aa14d2-8efa-4bfd-c0ed-682991288822",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int_series = pd.Series([1, 2, 3], dtype=int)\n",
"int_series"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WklCzqb8gRsB"
},
"source": [
"### 练习:\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true,
"id": "Cy-gqX5-gRsB",
"trusted": false
},
"outputs": [],
"source": [
"# Now set an element of int_series equal to None.\n",
"# How does that element show up in the Series?\n",
"# What is the dtype of the Series?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WjMQwltNgRsB"
},
"source": [
"在将数据类型向上转换以实现 `Series` 和 `DataFrame` 数据同质化的过程中，pandas 会主动在 `None` 和 `NaN` 之间切换缺失值。由于这一设计特性，将 `None` 和 `NaN` 视为 pandas 中两种不同形式的“空值”是很有帮助的。事实上，pandas 中一些处理缺失值的核心方法名称也反映了这一点：\n",
"\n",
"- `isnull()`：生成一个布尔掩码，用于标识缺失值\n",
"- `notnull()`：与 `isnull()` 相反\n",
"- `dropna()`：返回过滤后的数据版本\n",
"- `fillna()`：返回一个填充或插补缺失值后的数据副本\n",
"\n",
"这些方法非常重要，掌握并熟悉它们是关键。接下来我们将逐一深入了解这些方法。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yh5ifd9FgRsB"
},
"source": [
"### 检测空值\n",
"\n",
"既然我们已经了解了缺失值的重要性，那么在处理它们之前，我们需要在数据集中检测它们。 \n",
"`isnull()` 和 `notnull()` 是检测空数据的主要方法。两者都会返回数据的布尔掩码。\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true,
"id": "e-vFp5lvgRsC",
"trusted": false
},
"outputs": [],
"source": [
"example3 = pd.Series([0, np.nan, '', None])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1XdaJJ7PgRsC",
"outputId": "92fc363a-1874-471f-846d-f4f9ce1f51d0",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 True\n",
"dtype: bool"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example3.isnull()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PaSZ0SQygRsC"
},
"source": [
"仔细观察输出结果，有什么让你感到意外吗？虽然 `0` 是一个算术上的空值，但它仍然是一个完全合法的整数，pandas 也将其视为整数。而 `''` 则稍微复杂一些。虽然我们在第 1 节中使用它来表示空字符串值，但它本质上仍然是一个字符串对象，而不是 pandas 所认为的空值。\n",
"\n",
"现在，让我们换个角度，以更贴近实际使用的方法来应用这些方法。你可以直接将布尔掩码用作 ``Series`` 或 ``DataFrame`` 的索引，这在处理孤立的缺失值（或存在值）时非常有用。\n",
"\n",
"如果我们想要统计缺失值的总数，只需对 `isnull()` 方法生成的掩码进行求和即可。\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JCcQVoPkHDUv",
"outputId": "001daa72-54f8-4bd5-842a-4df627a79d4d"
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example3.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PlBqEo3mgRsC"
},
"source": [
"### 练习:\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true,
"id": "ggDVf5uygRsD",
"trusted": false
},
"outputs": [],
"source": [
"# Try running example3[example3.notnull()].\n",
"# Before you do so, what do you expect to see?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D_jWN7mHgRsD"
},
"source": [
"**关键点**：当在数据框中使用 `isnull()` 和 `notnull()` 方法时，它们会产生类似的结果：显示结果及其索引，这将在处理数据时极大地帮助您。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BvnoojWsgRr4"
},
"source": [
"### 处理缺失数据\n",
"\n",
"> **学习目标：** 在本小节结束时，您应该了解如何以及何时替换或删除 DataFrame 中的空值。\n",
"\n",
"机器学习模型本身无法处理缺失数据。因此，在将数据传入模型之前，我们需要处理这些缺失值。\n",
"\n",
"处理缺失数据的方法会带来微妙的权衡，并可能影响您的最终分析结果以及实际应用效果。\n",
"\n",
"处理缺失数据主要有两种方法：\n",
"\n",
"1. 删除包含缺失值的行\n",
"2. 用其他值替换缺失值\n",
"\n",
"我们将详细讨论这两种方法及其优缺点。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3VaYC1TvgRsD"
},
"source": [
"### 删除空值\n",
"\n",
"我们传递给模型的数据量直接影响其性能。删除空值意味着减少数据点的数量，从而缩小数据集的规模。因此，当数据集较大时，建议删除包含空值的行。\n",
"\n",
"另一种情况可能是某一行或列有大量缺失值。在这种情况下，可以删除这些行或列，因为它们的大部分数据都缺失，对我们的分析贡献不大。\n",
"\n",
"除了识别缺失值之外，pandas 还提供了一种方便的方法来从 `Series` 和 `DataFrame` 中移除空值。为了更好地理解这一点，我们可以回到 `example3`。`DataFrame.dropna()` 函数可以帮助我们删除包含空值的行。\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7uIvS097gRsD",
"outputId": "c13fc117-4ca1-4145-a0aa-42ac89e6e218",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"2 \n",
"dtype: object"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example3 = example3.dropna()\n",
"example3"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hil2cr64gRsD"
},
"source": [
"请注意，这应该看起来像您从 `example3[example3.notnull()]` 的输出。这里的区别在于，`dropna` 不仅仅是基于掩码值进行索引，而是从 `Series` `example3` 中移除了那些缺失值。\n",
"\n",
"由于 DataFrame 是二维的，因此它提供了更多删除数据的选项。\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "an-l74sPgRsE",
"outputId": "340876a0-63ad-40f6-bd54-6240cdae50ab",
"trusted": false
},
"outputs": [
{
"data": {
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"metadata": {},
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],
"source": [
"example4 = pd.DataFrame([[1, np.nan, 7], \n",
" [2, 5, 8], \n",
" [np.nan, 6, 9]])\n",
"example4"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "66wwdHZrgRsE"
},
"source": [
"（你是否注意到，为了容纳 `NaN` 值，pandas 将两列数据类型提升为浮点型？）\n",
"\n",
"你无法从 `DataFrame` 中删除单个值，因此必须删除整行或整列。根据你的需求，你可能需要选择其中一种方式，因此 pandas 提供了两种选项。在数据科学中，列通常表示变量，行表示观测值，因此你更可能删除数据的行；`dropna()` 的默认设置是删除所有包含任何空值的行：\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
},
"id": "jAVU24RXgRsE",
"outputId": "0b5e5aee-7187-4d3f-b583-a44136ae5f80",
"trusted": false
},
"outputs": [
{
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"source": [
"example4.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TrQRBuTDgRsE"
},
"source": [
"如果需要，您可以从列中删除NA值。使用`axis=1`来实现：\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "GrBhxu9GgRsE",
"outputId": "ff4001f3-2e61-4509-d60e-0093d1068437",
"trusted": false
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"outputs": [
{
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"metadata": {},
"output_type": "execute_result"
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"source": [
"example4.dropna(axis='columns')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KWXiKTfMgRsF"
},
"source": [
"请注意，这可能会丢弃许多您可能希望保留的数据，尤其是在较小的数据集中。如果您只想删除包含多个甚至全部空值的行或列，该怎么办？您可以在 `dropna` 中通过 `how` 和 `thresh` 参数来指定这些设置。\n",
"\n",
"默认情况下，`how='any'`（如果您想自己检查或查看该方法的其他参数，可以在代码单元中运行 `example4.dropna?`）。您也可以选择指定 `how='all'`，这样只会删除包含所有空值的行或列。让我们扩展示例 `DataFrame`，在接下来的练习中看看它的实际效果。\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "Bcf_JWTsgRsF",
"outputId": "72e0b1b8-52fa-4923-98ce-b6fbed6e44b1",
"trusted": false
},
"outputs": [
{
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"metadata": {},
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],
"source": [
"example4[3] = np.nan\n",
"example4"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pNZer7q9JPNC"
},
"source": [
"> 关键要点：\n",
"1. 只有在数据集足够大的情况下，删除空值才是一个好主意。\n",
"2. 如果某行或某列的大部分数据缺失，可以直接删除整行或整列。\n",
"3. `DataFrame.dropna(axis=)` 方法可以用来删除空值。`axis` 参数表示是删除行还是列。\n",
"4. 还可以使用 `how` 参数。默认情况下，它设置为 `any`，因此只会删除包含任意空值的行或列。可以将其设置为 `all`，以指定仅删除所有值均为空的行或列。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oXXSfQFHgRsF"
},
"source": [
"### 练习:\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true,
"id": "ExUwQRxpgRsF",
"trusted": false
},
"outputs": [],
"source": [
"# How might you go about dropping just column 3?\n",
"# Hint: remember that you will need to supply both the axis parameter and the how parameter.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "38kwAihWgRsG"
},
"source": [
"`thresh` 参数为您提供了更细粒度的控制：您可以设置一行或一列需要具有的 *非空* 值的数量，以便保留该行或列：\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
},
"id": "M9dCNMaagRsG",
"outputId": "8093713a-54d2-4e54-c73f-4eea315cb6f2",
"trusted": false
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"outputs": [
{
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"source": [
"example4.dropna(axis='rows', thresh=3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fmSFnzZegRsG"
},
"source": [
"这里，第一行和最后一行已被删除，因为它们仅包含两个非空值。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mCcxLGyUgRsG"
},
"source": [
"### 填充空值\n",
"\n",
"有时，用可能有效的值填充缺失值是合理的。填充空值有几种方法。第一种是使用领域知识（即数据集所基于主题的知识）来近似缺失值。\n",
"\n",
"你可以使用 `isnull` 来直接处理，但这可能会很繁琐，尤其是当你有很多值需要填充时。由于这是数据科学中非常常见的任务，pandas 提供了 `fillna` 方法，它会返回一个 `Series` 或 `DataFrame` 的副本，其中的缺失值被替换为你选择的值。让我们创建另一个示例 `Series` 来看看它在实际中的工作方式。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CE8S7louLezV"
},
"source": [
"### 分类数据（非数值型）\n",
"首先让我们来看非数值型数据。在数据集中，我们会有一些包含分类数据的列。例如：性别、True 或 False 等。\n",
"\n",
"在大多数情况下，我们会用该列的`众数`来替换缺失值。比如说，我们有100个数据点，其中90个是True，8个是False，还有2个未填写。那么我们可以将这2个未填写的值填充为True，基于整个列的情况。\n",
"\n",
"当然，在这里我们也可以使用领域知识。让我们来看一个用众数填充的例子。\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
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"height": 204
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"id": "MY5faq4yLdpQ",
"outputId": "19ab472e-1eed-4de8-f8a7-db2a3af3cb1a"
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"outputs": [
{
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"source": [
"fill_with_mode = pd.DataFrame([[1,2,\"True\"],\n",
" [3,4,None],\n",
" [5,6,\"False\"],\n",
" [7,8,\"True\"],\n",
" [9,10,\"True\"]])\n",
"\n",
"fill_with_mode"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MLAoMQOfNPlA"
},
"source": [
"现在，让我们先找到众数，然后用众数填充 `None` 值。\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WKy-9Y2tN5jv",
"outputId": "8da9fa16-e08c-447e-dea1-d4b1db2feebf"
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"outputs": [
{
"data": {
"text/plain": [
"True 3\n",
"False 1\n",
"Name: 2, dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fill_with_mode[2].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6iNz_zG_OKrx"
},
"source": [
"所以，我们将用True替换None\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "TxPKteRvNPOs"
},
"outputs": [],
"source": [
"fill_with_mode[2].fillna('True',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "tvas7c9_OPWE",
"outputId": "ec3c8e44-d644-475e-9e22-c65101965850"
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"outputs": [
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"source": [
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{
"cell_type": "markdown",
"metadata": {
"id": "SktitLxxOR16"
},
"source": [
"正如我们所见，空值已被替换。不言而喻，我们本可以在 `'True'` 的位置写任何内容，它都会被替换。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "heYe1I0dOmQ_"
},
"source": [
"### 数值数据\n",
"现在来说说数值数据。这里有两种常见的方法来替换缺失值：\n",
"\n",
"1. 用行的中位数替换 \n",
"2. 用行的平均值替换 \n",
"\n",
"当数据存在偏斜且有异常值时，我们使用中位数替换。这是因为中位数对异常值具有鲁棒性。\n",
"\n",
"当数据是标准化的，我们可以使用平均值，因为在这种情况下，平均值和中位数会非常接近。\n",
"\n",
"首先，让我们选取一个正态分布的列，并用该列的平均值填充缺失值。\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "09HM_2feOj5Y",
"outputId": "7e309013-9acb-411c-9b06-4de795bbeeff"
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"outputs": [
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"fill_with_mean = pd.DataFrame([[-2,0,1],\n",
" [-1,2,3],\n",
" [np.nan,4,5],\n",
" [1,6,7],\n",
" [2,8,9]])\n",
"\n",
"fill_with_mean"
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{
"cell_type": "markdown",
"metadata": {
"id": "ka7-wNfzSxbx"
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"source": [
"列的平均值是\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XYtYEf5BSxFL",
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"outputs": [
{
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"text/plain": [
"0.0"
]
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"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(fill_with_mean[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oBSRGxKRS39K"
},
"source": [
"用均值填充\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
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"id": "FzncQLmuS5jh",
"outputId": "00f74fff-01f4-4024-c261-796f50f01d2e"
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"source": [
"fill_with_mean[0].fillna(np.mean(fill_with_mean[0]),inplace=True)\n",
"fill_with_mean"
]
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{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"正如我们所见，缺失值已被其平均值替换。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jIvF13a1i00Z"
},
"source": [
"现在让我们尝试另一个数据框，这次我们将用列的中位数替换None值。\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "DA59Bqo3jBYZ",
"outputId": "85dae6ec-7394-4c36-fda0-e04769ec4a32"
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"outputs": [
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"fill_with_median = pd.DataFrame([[-2,0,1],\n",
" [-1,2,3],\n",
" [0,np.nan,5],\n",
" [1,6,7],\n",
" [2,8,9]])\n",
"\n",
"fill_with_median"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mM1GpXYmjHnc"
},
"source": [
"第二列的中位数是\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uiDy5v3xjHHX",
"outputId": "564b6b74-2004-4486-90d4-b39330a64b88"
},
"outputs": [
{
"data": {
"text/plain": [
"4.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fill_with_median[1].median()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z9PLF75Jj_1s"
},
"source": [
"用中位数填充\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "lFKbOxCMkBbg",
"outputId": "a8bd18fb-2765-47d4-e5fe-e965f57ed1f4"
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"outputs": [
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"source": [
"fill_with_median[1].fillna(fill_with_median[1].median(),inplace=True)\n",
"fill_with_median"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8JtQ53GSkKWC"
},
"source": [
"正如我们所见，NaN值已被该列的中位数替换\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0ybtWLDdgRsG",
"outputId": "b8c238ef-6024-4ee2-be2b-aa1f0fcac61d",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b NaN\n",
"c 2.0\n",
"d NaN\n",
"e 3.0\n",
"dtype: float64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example5 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
"example5"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yrsigxRggRsH"
},
"source": [
"您可以使用单个值（例如 `0`）填充所有空条目：\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KXMIPsQdgRsH",
"outputId": "aeedfa0a-a421-4c2f-cb0d-183ce8f0c91d",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b 0.0\n",
"c 2.0\n",
"d 0.0\n",
"e 3.0\n",
"dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example5.fillna(0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RRlI5f_hkfKe"
},
"source": [
"> 关键要点：\n",
"1. 填补缺失值应在数据较少或有明确填补策略时进行。\n",
"2. 可以利用领域知识通过估算来填补缺失值。\n",
"3. 对于分类数据，通常用列的众数替代缺失值。\n",
"4. 对于数值型数据，缺失值通常用列的平均值（针对归一化数据集）或中位数填补。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FI9MmqFJgRsH"
},
"source": [
"### 练习:\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true,
"id": "af-ezpXdgRsH",
"trusted": false
},
"outputs": [],
"source": [
"# What happens if you try to fill null values with a string, like ''?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kq3hw1kLgRsI"
},
"source": [
"您可以使用**前向填充**空值，即使用最后一个有效值填充空值：\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vO3BuNrggRsI",
"outputId": "e2bc591b-0b48-4e88-ee65-754f2737c196",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b 1.0\n",
"c 2.0\n",
"d 2.0\n",
"e 3.0\n",
"dtype: float64"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example5.fillna(method='ffill')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nDXeYuHzgRsI"
},
"source": [
"您还可以**回填**，将下一个有效值向后传播以填充空值：\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4M5onHcEgRsI",
"outputId": "8f32b185-40dd-4a9f-bd85-54d6b6a414fe",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b 2.0\n",
"c 2.0\n",
"d 3.0\n",
"e 3.0\n",
"dtype: float64"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example5.fillna(method='bfill')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"id": "MbBzTom5gRsI"
},
"source": [
"正如您可能猜到的，这与DataFrame的操作方式相同，但您也可以指定一个`axis`来填充空值：\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "aRpIvo4ZgRsI",
"outputId": "905a980a-a808-4eca-d0ba-224bd7d85955",
"trusted": false
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{
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"source": [
"example4"
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{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
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"id": "VM1qtACAgRsI",
"outputId": "71f2ad28-9b4e-4ff4-f5c3-e731eb489ade",
"trusted": false
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"outputs": [
{
"data": {
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"source": [
"example4.fillna(method='ffill', axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZeMc-I1EgRsI"
},
"source": [
"请注意，当没有可用于向前填充的先前值时，空值将保留。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eeAoOU0RgRsJ"
},
"source": [
"### 练习:\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true,
"id": "e8S-CjW8gRsJ",
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"outputs": [],
"source": [
"# What output does example4.fillna(method='bfill', axis=1) produce?\n",
"# What about example4.fillna(method='ffill') or example4.fillna(method='bfill')?\n",
"# Can you think of a longer code snippet to write that can fill all of the null values in example4?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YHgy0lIrgRsJ"
},
"source": [
"你可以创造性地使用 `fillna`。例如，让我们再次查看 `example4`，但这次我们用 `DataFrame` 中所有值的平均值来填充缺失值：\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "OtYVErEygRsJ",
"outputId": "708b1e67-45ca-44bf-a5ee-8b2de09ece73",
"trusted": false
},
"outputs": [
{
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" 0 1 2 3\n",
"0 1.0 5.5 7 NaN\n",
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"execution_count": 46,
"metadata": {},
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"source": [
"example4.fillna(example4.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zpMvCkLSgRsJ"
},
"source": [
"注意，第3列仍然没有值：默认方向是按行填充值。\n",
"\n",
"> **要点：** 处理数据集中的缺失值有多种方法。具体采用哪种策略（删除、替换，甚至如何替换）应根据数据的具体情况来决定。随着你处理和接触更多的数据集，你会对如何处理缺失值有更好的理解。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bauDnESIl9FH"
},
"source": [
"### 编码分类数据\n",
"\n",
"机器学习模型只能处理数字和任何形式的数值数据。它无法区分“是”和“否”，但可以区分0和1。因此，在填充缺失值之后，我们需要将分类数据编码为某种数值形式，以便模型能够理解。\n",
"\n",
"编码可以通过两种方式完成。接下来我们将讨论这两种方法。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uDq9SxB7mu5i"
},
"source": [
"**标签编码**\n",
"\n",
"标签编码基本上是将每个类别转换为一个数字。例如，假设我们有一个航空乘客的数据集，其中有一列包含他们的舱位类别，类别包括 ['商务舱', '经济舱', '头等舱']。如果对其进行标签编码，它将被转换为 [0,1,2]。让我们通过代码示例来看看。由于我们将在后续笔记本中学习 `scikit-learn`，这里不会使用它。\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"id": "1vGz7uZyoWHL",
"outputId": "9e252855-d193-4103-a54d-028ea7787b34"
},
"outputs": [
{
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" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>economy class</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>50</td>\n",
" <td>economy class</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>60</td>\n",
" <td>business class</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID class\n",
"0 10 business class\n",
"1 20 first class\n",
"2 30 economy class\n",
"3 40 economy class\n",
"4 50 economy class\n",
"5 60 business class"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label = pd.DataFrame([\n",
" [10,'business class'],\n",
" [20,'first class'],\n",
" [30, 'economy class'],\n",
" [40, 'economy class'],\n",
" [50, 'economy class'],\n",
" [60, 'business class']\n",
"],columns=['ID','class'])\n",
"label"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IDHnkwTYov-h"
},
"source": [
"要对第一列进行标签编码，我们首先需要描述一个从每个类别到数字的映射，然后进行替换。\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"id": "ZC5URJG3o1ES",
"outputId": "aab0f1e7-e0f3-4c14-8459-9f9168c85437"
},
"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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],
"text/plain": [
" ID class\n",
"0 10 0\n",
"1 20 2\n",
"2 30 1\n",
"3 40 1\n",
"4 50 1\n",
"5 60 0"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_labels = {'business class':0,'economy class':1,'first class':2}\n",
"label['class'] = label['class'].replace(class_labels)\n",
"label"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ftnF-TyapOPt"
},
"source": [
"正如我们所见，输出与我们预期的结果一致。那么，我们什么时候使用标签编码呢？标签编码适用于以下一种或两种情况： \n",
"1. 当类别数量较多时 \n",
"2. 当类别是有序的时 \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eQPAPVwsqWT7"
},
"source": [
"**独热编码**\n",
"\n",
"另一种编码方式是独热编码。在这种编码方式中，每个列的类别都会作为单独的列添加到数据集中，并且每个数据点根据是否包含该类别会被赋值为0或1。因此，如果有n个不同的类别，就会向数据框中添加n列。\n",
"\n",
"例如，我们仍然使用飞机舱位的例子。类别是：['商务舱', '经济舱', '头等舱']。如果我们进行独热编码，数据集中将会添加以下三列：['class_business class', 'class_economy class', 'class_first class']。\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"id": "ZM0eVh0ArKUL",
"outputId": "83238a76-b3a5-418d-c0b6-605b02b6891b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <td>50</td>\n",
" <td>economy class</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>60</td>\n",
" <td>business class</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID class\n",
"0 10 business class\n",
"1 20 first class\n",
"2 30 economy class\n",
"3 40 economy class\n",
"4 50 economy class\n",
"5 60 business class"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_hot = pd.DataFrame([\n",
" [10,'business class'],\n",
" [20,'first class'],\n",
" [30, 'economy class'],\n",
" [40, 'economy class'],\n",
" [50, 'economy class'],\n",
" [60, 'business class']\n",
"],columns=['ID','class'])\n",
"one_hot"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aVnZ7paDrWmb"
},
"source": [
"让我们对第一列进行独热编码\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"id": "RUPxf7egrYKr"
},
"outputs": [],
"source": [
"one_hot_data = pd.get_dummies(one_hot,columns=['class'])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"id": "TM37pHsFr4ge",
"outputId": "7be15f53-79b2-447a-979c-822658339a9e"
},
"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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"</style>\n",
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" <th></th>\n",
" <th>ID</th>\n",
" <th>class_business class</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
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" <th>4</th>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>60</td>\n",
" <td>1</td>\n",
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],
"text/plain": [
" ID class_business class class_economy class class_first class\n",
"0 10 1 0 0\n",
"1 20 0 0 1\n",
"2 30 0 1 0\n",
"3 40 0 1 0\n",
"4 50 0 1 0\n",
"5 60 1 0 0"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_hot_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_zXRLOjXujdA"
},
"source": [
"每个独热编码列包含0或1，指定该数据点是否属于该类别。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bDnC4NQOu0qr"
},
"source": [
"我们什么时候使用独热编码？独热编码在以下一种或两种情况下使用：\n",
"\n",
"1. 当类别数量和数据集规模较小时。\n",
"2. 当类别没有特定顺序时。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XnUmci_4uvyu"
},
"source": [
"> 关键要点：\n",
"1. 编码用于将非数值数据转换为数值数据。\n",
"2. 编码分为两种类型：标签编码和独热编码，可根据数据集的需求进行选择。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K8UXOJYRgRsJ"
},
"source": [
"## 删除重复数据\n",
"\n",
"> **学习目标：** 在本小节结束时，您应该能够熟练识别并删除 DataFrame 中的重复值。\n",
"\n",
"除了缺失数据之外，您在实际数据集中还会经常遇到重复数据。幸运的是，pandas 提供了一种简单的方法来检测和删除重复条目。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qrEG-Wa0gRsJ"
},
"source": [
"### 识别重复项：`duplicated`\n",
"\n",
"您可以使用 pandas 中的 `duplicated` 方法轻松识别重复值。该方法返回一个布尔掩码，指示 `DataFrame` 中的某个条目是否是之前条目的重复项。让我们创建另一个示例 `DataFrame` 来实际查看这一功能。\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "ZLu6FEnZgRsJ",
"outputId": "376512d1-d842-4db1-aea3-71052aeeecaf",
"trusted": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" }\n",
"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>letters</th>\n",
" <th>numbers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>B</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>B</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>B</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" letters numbers\n",
"0 A 1\n",
"1 B 2\n",
"2 A 1\n",
"3 B 3\n",
"4 B 3"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example6 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],\n",
" 'numbers': [1, 2, 1, 3, 3]})\n",
"example6"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cIduB5oBgRsK",
"outputId": "3da27b3d-4d69-4e1d-bb52-0af21bae87f2",
"trusted": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 True\n",
"3 False\n",
"4 True\n",
"dtype: bool"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example6.duplicated()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0eDRJD4SgRsK"
},
"source": [
"### 删除重复项：`drop_duplicates`\n",
"`drop_duplicates` 仅返回数据的副本，其中所有 `duplicated` 值均为 `False`：\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "w_YPpqIqgRsK",
"outputId": "ac66bd2f-8671-4744-87f5-8b8d96553dea",
"trusted": false
},
"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",
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" text-align: right;\n",
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" <thead>\n",
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" <th></th>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" letters numbers\n",
"0 A 1\n",
"1 B 2\n",
"3 B 3"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example6.drop_duplicates()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "69AqoCZAgRsK"
},
"source": [
"`duplicated` 和 `drop_duplicates` 默认会考虑所有列，但您可以指定它们仅检查 `DataFrame` 中的部分列：\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"id": "BILjDs67gRsK",
"outputId": "ef6dcc08-db8b-4352-c44e-5aa9e2bec0d3",
"trusted": false
},
"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>letters</th>\n",
" <th>numbers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>B</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" letters numbers\n",
"0 A 1\n",
"1 B 2"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example6.drop_duplicates(['letters'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GvX4og1EgRsL"
},
"source": [
"> **要点:** 删除重复数据是几乎每个数据科学项目的重要组成部分。重复数据可能会改变分析结果，并导致不准确的结论！\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 真实世界中的数据质量检查\n",
"\n",
"> **学习目标：** 在本节结束时，您应该能够熟练检测和纠正常见的真实世界数据质量问题，包括不一致的分类值、异常的数值（离群值）以及具有变体的重复实体。\n",
"\n",
"虽然缺失值和完全重复是常见问题，但真实世界的数据集通常还包含更为隐蔽的问题：\n",
"\n",
"1. **不一致的分类值**：同一类别的拼写不同（例如，“USA”、“U.S.A”、“United States”）\n",
"2. **异常的数值**：极端的离群值，可能是数据录入错误（例如，年龄 = 999）\n",
"3. **近似重复的行**：表示同一实体但存在细微差异的记录\n",
"\n",
"让我们来探讨检测和处理这些问题的技术。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 创建一个示例“脏”数据集\n",
"\n",
"首先，让我们创建一个包含我们在实际数据中常见问题类型的示例数据集：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Create a sample dataset with quality issues\n",
"dirty_data = pd.DataFrame({\n",
" 'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
" 'name': ['John Smith', 'Jane Doe', 'John Smith', 'Bob Johnson', \n",
" 'Alice Williams', 'Charlie Brown', 'John Smith', 'Eva Martinez',\n",
" 'Bob Johnson', 'Diana Prince', 'Frank Castle', 'Alice Williams'],\n",
" 'age': [25, 32, 25, 45, 28, 199, 25, 31, 45, 27, -5, 28],\n",
" 'country': ['USA', 'UK', 'U.S.A', 'Canada', 'USA', 'United Kingdom',\n",
" 'United States', 'Mexico', 'canada', 'USA', 'UK', 'usa'],\n",
" 'purchase_amount': [100.50, 250.00, 105.00, 320.00, 180.00, 90.00,\n",
" 102.00, 275.00, 325.00, 195.00, 410.00, 185.00]\n",
"})\n",
"\n",
"print(\"Sample 'Dirty' Dataset:\")\n",
"print(dirty_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. 检测不一致的分类值\n",
"\n",
"注意，`country` 列中同一个国家有多种表示方式。让我们来识别这些不一致之处：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check unique values in the country column\n",
"print(\"Unique country values:\")\n",
"print(dirty_data['country'].unique())\n",
"print(f\"\\nTotal unique values: {dirty_data['country'].nunique()}\")\n",
"\n",
"# Count occurrences of each variation\n",
"print(\"\\nValue counts:\")\n",
"print(dirty_data['country'].value_counts())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 标准化分类值\n",
"\n",
"我们可以创建一个映射来标准化这些值。一种简单的方法是将值转换为小写并创建一个映射字典：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a standardization mapping\n",
"country_mapping = {\n",
" 'usa': 'USA',\n",
" 'u.s.a': 'USA',\n",
" 'united states': 'USA',\n",
" 'uk': 'UK',\n",
" 'united kingdom': 'UK',\n",
" 'canada': 'Canada',\n",
" 'mexico': 'Mexico'\n",
"}\n",
"\n",
"# Standardize the country column\n",
"dirty_data['country_clean'] = dirty_data['country'].str.lower().map(country_mapping)\n",
"\n",
"print(\"Before standardization:\")\n",
"print(dirty_data['country'].value_counts())\n",
"print(\"\\nAfter standardization:\")\n",
"print(dirty_data[['country_clean']].value_counts())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**替代方法：使用模糊匹配**\n",
"\n",
"对于更复杂的情况，我们可以使用 `rapidfuzz` 库进行模糊字符串匹配，以自动检测相似的字符串：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from rapidfuzz import process, fuzz\n",
"except ImportError:\n",
" print(\"rapidfuzz is not installed. Please install it with 'pip install rapidfuzz' to use fuzzy matching.\")\n",
" process = None\n",
" fuzz = None\n",
"\n",
"# Get unique countries\n",
"unique_countries = dirty_data['country'].unique()\n",
"\n",
"# For each country, find similar matches\n",
"if process is not None and fuzz is not None:\n",
" print(\"Finding similar country names (similarity > 70%):\")\n",
" for country in unique_countries:\n",
" matches = process.extract(country, unique_countries, scorer=fuzz.ratio, limit=3)\n",
" # Filter matches with similarity > 70 and not identical\n",
" similar = [m for m in matches if m[1] > 70 and m[0] != country]\n",
" if similar:\n",
" print(f\"\\n'{country}' is similar to:\")\n",
" for match, score, _ in similar:\n",
" print(f\" - '{match}' (similarity: {score}%)\")\n",
"else:\n",
" print(\"Skipping fuzzy matching because rapidfuzz is not available.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 检测异常数值（离群值）\n",
"\n",
"查看 `age` 列，我们发现一些可疑的值，比如 199 和 -5。让我们使用统计方法来检测这些离群值。\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Display basic statistics\n",
"print(\"Age column statistics:\")\n",
"print(dirty_data['age'].describe())\n",
"\n",
"# Identify impossible values using domain knowledge\n",
"print(\"\\nRows with impossible age values (< 0 or > 120):\")\n",
"impossible_ages = dirty_data[(dirty_data['age'] < 0) | (dirty_data['age'] > 120)]\n",
"print(impossible_ages[['customer_id', 'name', 'age']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 使用 IQR（四分位距）方法\n",
"\n",
"IQR 方法是一种稳健的统计技术，用于检测异常值，对极端值的敏感性较低：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate IQR for age (excluding impossible values)\n",
"valid_ages = dirty_data[(dirty_data['age'] >= 0) & (dirty_data['age'] <= 120)]['age']\n",
"\n",
"Q1 = valid_ages.quantile(0.25)\n",
"Q3 = valid_ages.quantile(0.75)\n",
"IQR = Q3 - Q1\n",
"\n",
"# Define outlier bounds\n",
"lower_bound = Q1 - 1.5 * IQR\n",
"upper_bound = Q3 + 1.5 * IQR\n",
"\n",
"print(f\"IQR-based outlier bounds for age: [{lower_bound:.2f}, {upper_bound:.2f}]\")\n",
"\n",
"# Identify outliers\n",
"age_outliers = dirty_data[(dirty_data['age'] < lower_bound) | (dirty_data['age'] > upper_bound)]\n",
"print(f\"\\nRows with age outliers:\")\n",
"print(age_outliers[['customer_id', 'name', 'age']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 使用 Z-Score 方法\n",
"\n",
"Z-Score 方法通过与平均值的标准差来识别异常值：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from scipy import stats\n",
"except ImportError:\n",
" print(\"scipy is required for Z-score calculation. Please install it with 'pip install scipy' and rerun this cell.\")\n",
"else:\n",
" # Calculate Z-scores for age, handling NaN values\n",
" age_nonan = dirty_data['age'].dropna()\n",
" zscores = np.abs(stats.zscore(age_nonan))\n",
" dirty_data['age_zscore'] = np.nan\n",
" dirty_data.loc[age_nonan.index, 'age_zscore'] = zscores\n",
"\n",
" # Typically, Z-score > 3 indicates an outlier\n",
" print(\"Rows with age Z-score > 3:\")\n",
" zscore_outliers = dirty_data[dirty_data['age_zscore'] > 3]\n",
" print(zscore_outliers[['customer_id', 'name', 'age', 'age_zscore']])\n",
"\n",
" # Clean up the temporary column\n",
" dirty_data = dirty_data.drop('age_zscore', axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 处理异常值\n",
"\n",
"一旦检测到异常值，可以通过以下几种方式处理：\n",
"1. **删除**：删除包含异常值的行（如果它们是错误数据）\n",
"2. **限制**：用边界值替换\n",
"3. **替换为 NaN**：将其视为缺失数据并使用插补技术处理\n",
"4. **保留**：如果它们是合法的极端值\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a cleaned version by replacing impossible ages with NaN\n",
"dirty_data['age_clean'] = dirty_data['age'].apply(\n",
" lambda x: np.nan if (x < 0 or x > 120) else x\n",
")\n",
"\n",
"print(\"Age column before and after cleaning:\")\n",
"print(dirty_data[['customer_id', 'name', 'age', 'age_clean']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. 检测近似重复行\n",
"\n",
"注意，我们的数据集中有多个“John Smith”的条目，且值略有不同。让我们根据名字相似性来识别潜在的重复项。\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# First, let's look at exact name matches (ignoring extra whitespace)\n",
"dirty_data['name_normalized'] = dirty_data['name'].str.strip().str.lower()\n",
"\n",
"print(\"Checking for duplicate names:\")\n",
"duplicate_names = dirty_data[dirty_data.duplicated(['name_normalized'], keep=False)]\n",
"print(duplicate_names.sort_values('name_normalized')[['customer_id', 'name', 'age', 'country']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 使用模糊匹配查找近似重复项\n",
"\n",
"为了进行更复杂的重复检测，我们可以使用模糊匹配来查找相似的名称：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from rapidfuzz import process, fuzz\n",
"\n",
" # Function to find potential duplicates\n",
" def find_near_duplicates(df, column, threshold=90):\n",
" \"\"\"\n",
" Find near-duplicate entries in a column using fuzzy matching.\n",
" \n",
" Parameters:\n",
" - df: DataFrame\n",
" - column: Column name to check for duplicates\n",
" - threshold: Similarity threshold (0-100)\n",
" \n",
" Returns: List of potential duplicate groups\n",
" \"\"\"\n",
" values = df[column].unique()\n",
" duplicate_groups = []\n",
" checked = set()\n",
" \n",
" for value in values:\n",
" if value in checked:\n",
" continue\n",
" \n",
" # Find similar values\n",
" matches = process.extract(value, values, scorer=fuzz.ratio, limit=len(values))\n",
" similar = [m[0] for m in matches if m[1] >= threshold]\n",
" \n",
" if len(similar) > 1:\n",
" duplicate_groups.append(similar)\n",
" checked.update(similar)\n",
" \n",
" return duplicate_groups\n",
"\n",
" # Find near-duplicate names\n",
" duplicate_groups = find_near_duplicates(dirty_data, 'name', threshold=90)\n",
"\n",
" print(\"Potential duplicate groups:\")\n",
" for i, group in enumerate(duplicate_groups, 1):\n",
" print(f\"\\nGroup {i}:\")\n",
" for name in group:\n",
" matching_rows = dirty_data[dirty_data['name'] == name]\n",
" print(f\" '{name}': {len(matching_rows)} occurrence(s)\")\n",
" for _, row in matching_rows.iterrows():\n",
" print(f\" - Customer {row['customer_id']}: age={row['age']}, country={row['country']}\")\n",
"except ImportError:\n",
" print(\"rapidfuzz is not installed. Skipping fuzzy matching for near-duplicates.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 处理重复项\n",
"\n",
"一旦识别出重复项，你需要决定如何处理：\n",
"1. **保留第一次出现**：使用 `drop_duplicates(keep='first')`\n",
"2. **保留最后一次出现**：使用 `drop_duplicates(keep='last')`\n",
"3. **汇总信息**：合并重复行中的信息\n",
"4. **人工审核**：标记以供人工审核\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example: Remove duplicates based on normalized name, keeping first occurrence\n",
"cleaned_data = dirty_data.drop_duplicates(subset=['name_normalized'], keep='first')\n",
"\n",
"print(f\"Original dataset: {len(dirty_data)} rows\")\n",
"print(f\"After removing name duplicates: {len(cleaned_data)} rows\")\n",
"print(f\"Removed: {len(dirty_data) - len(cleaned_data)} duplicate rows\")\n",
"\n",
"print(\"\\nCleaned dataset:\")\n",
"print(cleaned_data[['customer_id', 'name', 'age', 'country_clean']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 总结：完整的数据清洗流程\n",
"\n",
"让我们将所有内容整合成一个全面的清洗流程：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def clean_dataset(df):\n",
" \"\"\"\n",
" Comprehensive data cleaning function.\n",
" \"\"\"\n",
" # Create a copy to avoid modifying the original\n",
" cleaned = df.copy()\n",
" \n",
" # 1. Standardize categorical values (country)\n",
" country_mapping = {\n",
" 'usa': 'USA', 'u.s.a': 'USA', 'united states': 'USA',\n",
" 'uk': 'UK', 'united kingdom': 'UK',\n",
" 'canada': 'Canada', 'mexico': 'Mexico'\n",
" }\n",
" cleaned['country'] = cleaned['country'].str.lower().map(country_mapping)\n",
" \n",
" # 2. Clean abnormal age values\n",
" cleaned['age'] = cleaned['age'].apply(\n",
" lambda x: np.nan if (x < 0 or x > 120) else x\n",
" )\n",
" \n",
" # 3. Remove near-duplicate names (normalize whitespace)\n",
" cleaned['name'] = cleaned['name'].str.strip()\n",
" cleaned = cleaned.drop_duplicates(subset=['name'], keep='first')\n",
" \n",
" return cleaned\n",
"\n",
"# Apply the cleaning pipeline\n",
"final_cleaned_data = clean_dataset(dirty_data)\n",
"\n",
"print(\"Before cleaning:\")\n",
"print(f\" Rows: {len(dirty_data)}\")\n",
"print(f\" Unique countries: {dirty_data['country'].nunique()}\")\n",
"print(f\" Invalid ages: {((dirty_data['age'] < 0) | (dirty_data['age'] > 120)).sum()}\")\n",
"\n",
"print(\"\\nAfter cleaning:\")\n",
"print(f\" Rows: {len(final_cleaned_data)}\")\n",
"print(f\" Unique countries: {final_cleaned_data['country'].nunique()}\")\n",
"print(f\" Invalid ages: {((final_cleaned_data['age'] < 0) | (final_cleaned_data['age'] > 120)).sum()}\")\n",
"\n",
"print(\"\\nCleaned dataset:\")\n",
"print(final_cleaned_data[['customer_id', 'name', 'age', 'country', 'purchase_amount']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🎯 挑战练习\n",
"\n",
"现在轮到你了！下面是一行包含多个质量问题的新数据。你能否：\n",
"\n",
"1. 找出这行数据中的所有问题\n",
"2. 编写代码清理每个问题\n",
"3. 将清理后的数据添加到数据集中\n",
"\n",
"以下是有问题的数据：\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# New problematic row\n",
"new_row = pd.DataFrame({\n",
" 'customer_id': [13],\n",
" 'name': [' Diana Prince '], # Extra whitespace\n",
" 'age': [250], # Impossible age\n",
" 'country': ['U.S.A.'], # Inconsistent format\n",
" 'purchase_amount': [150.00]\n",
"})\n",
"\n",
"print(\"New row to clean:\")\n",
"print(new_row)\n",
"\n",
"# TODO: Your code here to clean this row\n",
"# Hints:\n",
"# 1. Strip whitespace from the name\n",
"# 2. Check if the name is a duplicate (Diana Prince already exists)\n",
"# 3. Handle the impossible age value\n",
"# 4. Standardize the country name\n",
"\n",
"# Example solution (uncomment and modify as needed):\n",
"# new_row_cleaned = new_row.copy()\n",
"# new_row_cleaned['name'] = new_row_cleaned['name'].str.strip()\n",
"# new_row_cleaned['age'] = np.nan # Invalid age\n",
"# new_row_cleaned['country'] = 'USA' # Standardized\n",
"# print(\"\\nCleaned row:\")\n",
"# print(new_row_cleaned)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 关键要点\n",
"\n",
"1. **类别不一致**在实际数据中很常见。务必检查唯一值，并通过映射或模糊匹配来标准化它们。\n",
"\n",
"2. **异常值**会显著影响你的分析。结合领域知识和统计方法（IQR、Z分数）来检测它们。\n",
"\n",
"3. **近似重复项**比完全重复项更难检测。可以考虑使用模糊匹配并规范化数据（如转换为小写、去除空格）来识别它们。\n",
"\n",
"4. **数据清理是一个迭代过程**。可能需要应用多种技术并审查结果，才能最终确定清理后的数据集。\n",
"\n",
"5. **记录你的决策**。记录你应用了哪些清理步骤以及原因，这对于可重复性和透明性非常重要。\n",
"\n",
"> **最佳实践：**始终保留一份原始“脏”数据的副本。切勿覆盖源数据文件——创建清理后的版本，并使用清晰的命名规则，例如`data_cleaned.csv`。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n---\n\n**免责声明**： \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。\n"
]
}
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