{ "cells": [ { "cell_type": "markdown", "id": "8d489687-27b4-4e9a-952e-7a012c306abe", "metadata": {}, "source": [ "## 缺失值的检测" ] }, { "cell_type": "code", "execution_count": 2, "id": "da4a95cf-d6e6-40a9-b66a-97a4e43dc762", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Num1Num2Num3Num4
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数量占比(%)
Num4250.0
Num1125.0
Num200.0
Num300.0
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" ], "text/plain": [ " 数量 占比(%)\n", "Num4 2 50.0\n", "Num1 1 25.0\n", "Num2 0 0.0\n", "Num3 0 0.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def missing_values_table(df):\n", " # 计算每列包含缺失值的数量\n", " mis_val = df.isnull().sum()\n", " # 计算所有列缺失值⽐例\n", " mis_val_percent = df.isnull().sum() / len(df) * 100\n", " # 将数量和比例组合成DataFrame类的对象\n", " mis_val_table = pd.DataFrame({'数量':mis_val,\n", " '占比(%)':mis_val_percent})\n", " # 按照缺失值的数量降序排列\n", " mis_val_table_sorted = mis_val_table.sort_values('数量',\n", " ascending=False)\n", " return mis_val_table_sorted\n", "missing_values_table(df_na) # 统计缺失值的数量和占比情况" ] }, { "cell_type": "markdown", "id": "0ba91590-a075-4b67-a2b8-f128cec4e1d4", "metadata": {}, "source": [ "## 缺失值的处理" ] }, { "cell_type": "markdown", "id": "93d58f98-dabb-4881-a186-4d7228263131", "metadata": {}, "source": [ "### 1.\t删除缺失值" ] }, { "cell_type": "code", "execution_count": 6, "id": "8ae6f230-359e-4f92-90a7-619ce6e5b815", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Num1 Num2 Num3 Num4\n", "0 1.0 5 9 13.0\n", "1 2.0 6 10 14.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_na.dropna() # 删除包含缺失值的行" ] }, { "cell_type": "code", "execution_count": 7, "id": "fa058331-0029-4502-ad94-006c33a4f598", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Num1 Num2 Num3 Num4\n", "0 1.0 5 9 13.0\n", "1 2.0 6 10 14.0\n", "3 4.0 8 12 NaN" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_na.dropna(thresh=3) # 删除包含缺失值的行，并指定非缺失值的数量" ] }, { "cell_type": "markdown", "id": "80531b9d-8e56-4e2e-bff7-b38abfd05722", "metadata": {}, "source": [ "### 2.\t填充缺失值" ] }, { "cell_type": "code", "execution_count": 8, "id": "930e534f-7567-4f3a-87d3-9a19efd876b9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Num1Num2Num3Num4
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" ], "text/plain": [ " Num1 Num2 Num3 Num4\n", "0 1.0 5 9 13.0\n", "1 2.0 6 10 14.0\n", "2 NaN 7 11 NaN\n", "3 4.0 8 12 NaN" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "df_na = pd.DataFrame({'Num1':[1, 2, None, 4],\n", " 'Num2':[5, 6, 7, 8],\n", " 'Num3':[9, 10, 11, 12],\n", " 'Num4':[13, 14, np.NaN, np.NaN]})\n", "df_na" ] }, { "cell_type": "code", "execution_count": 9, "id": "dae9c89c-db23-4609-ad39-f289f17fc0a8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Num1Num2Num3Num4
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" ], "text/plain": [ " Num1 Num2 Num3 Num4\n", "0 1.0 5 9 13.0\n", "1 2.0 6 10 14.0\n", "2 66.0 7 11 66.0\n", "3 4.0 8 12 66.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_na.fillna(value=66.0) # 使用标量66.0填充缺失值" ] }, { "cell_type": "code", "execution_count": 10, "id": "55b765fb-acff-40d9-b678-2f85489fb837", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.3" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 计算Num1列的平均数，并保留一位小数\n", "mean_num1 = round(df_na['Num1'].mean(), 1)\n", "mean_num1" ] }, { "cell_type": "code", "execution_count": 11, "id": "68ca54fc-5001-4bbd-9005-e5493cafcdd5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 计算Num4列的平均数，并保留一位小数\n", "mean_num4 = round(df_na['Num4'].mean(), 1)\n", "mean_num4" ] }, { "cell_type": "code", "execution_count": 12, "id": "8b972bef-eb14-4022-ace0-5a27aca41971", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Num1Num2Num3Num4
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" ], "text/plain": [ " Num1 Num2 Num3 Num4\n", "0 1.0 5 9 13.0\n", "1 2.0 6 10 14.0\n", "2 2.3 7 11 13.5\n", "3 4.0 8 12 13.5" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 给指定的列填充平均数\n", "df_na.fillna(value={'Num1':mean_num1, 'Num4':mean_num4})" ] }, { "cell_type": "code", "execution_count": 13, "id": "fa5d9ed6-9ab1-4c79-845f-e496a9cb3cc5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\itcast\\AppData\\Local\\Temp\\ipykernel_13000\\3329080356.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df_na.fillna(method='ffill') # 采用前向填充的方式填充缺失值\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " Num1 Num2 Num3 Num4\n", "0 1.0 5 9 13.0\n", "1 2.0 6 10 14.0\n", "2 4.0 7 11 NaN\n", "3 4.0 8 12 NaN" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_na.fillna(method='bfill') # 采用后向填充的方式填充缺失值" ] }, { "cell_type": "markdown", "id": "fa2fb2ee-4343-4423-8c08-3da82e82126e", "metadata": {}, "source": [ "## 重复值的检测" ] }, { "cell_type": "code", "execution_count": 3, "id": "be3dd33d-abd0-429b-942a-47f519fcd029", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameageheight(cm)gender
0张三24162女
1李四23165女
2王五29175男
3赵六22175男
4赵六22175男
5孙七27180男
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" ], "text/plain": [ " name age height(cm) gender\n", "0 张三 24 162 女\n", "1 李四 23 165 女\n", "2 王五 29 175 男\n", "3 赵六 22 175 男\n", "4 赵六 22 175 男\n", "5 孙七 27 180 男" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "person_info = pd.DataFrame({'name':['张三', '李四', '王五', '赵六', '赵六', '孙七'],\n", " 'age': [24, 23, 29, 22, 22, 27],\n", " 'height(cm)': [162, 165, 175, 175, 175, 180],\n", " 'gender': ['女', '女', '男', '男', '男', '男']})\n", "person_info" ] }, { "cell_type": "code", "execution_count": 16, "id": "92c812d9-babb-40bb-80b1-4b375f69fb30", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 True\n", "5 False\n", "dtype: bool" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 检测重复值，保留第一次出现的行，标记再次出现的其他行为重复值\n", "person_info.duplicated()" ] }, { "cell_type": "markdown", "id": "88b30f67-9c63-4fc5-a121-b36e8b6070ba", "metadata": {}, "source": [ "## 重复值的处理" ] }, { "cell_type": "code", "execution_count": 17, "id": "53048d29-0b0a-47a1-b5a8-8d5954cce150", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameageheightgender
0张三24162女
1李四23165女
2王五29175男
3赵六22175男
5孙七27180男
\n", "
" ], "text/plain": [ " name age height gender\n", "0 张三 24 162 女\n", "1 李四 23 165 女\n", "2 王五 29 175 男\n", "3 赵六 22 175 男\n", "5 孙七 27 180 男" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person_info.drop_duplicates() # 删除重复值" ] }, { "cell_type": "markdown", "id": "76649ae8-0e54-4032-b993-dfacb7aa25ba", "metadata": {}, "source": [ "## 异常值的检测" ] }, { "cell_type": "markdown", "id": "2cbebbdd-1002-46ca-b474-3f0ecc307556", "metadata": {}, "source": [ "### 1.\t通过3σ原则检测异常值" ] }, { "cell_type": "code", "execution_count": 19, "id": "98dc5e74-7a9a-4790-b3c2-e5556b5e88a2", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "def three_sigma(ser):\n", " \"\"\"\n", " :param ser: 被检测的数据，接收DataFrame类的对象的一列数据\n", " :return: 异常值及其对应的行索引\n", " \"\"\"\n", " # 计算平均值和标准差\n", " mean_data = ser.mean()\n", " std_data = ser.std()\n", " # 根据3σ原则组合检测条件，即小于μ-3σ或大于μ+3σ\n", " rule = (mean_data - 3 * std_data > ser) | (mean_data +\n", " 3 * std_data < ser)\n", " # 获取异常值的行索引\n", " index = np.arange(ser.shape[0])[rule]\n", " # 根据行索引获取异常值\n", " outliers = ser.iloc[index]\n", " return outliers" ] }, { "cell_type": "code", "execution_count": 20, "id": "23de793d-f0cf-4197-800b-4150256c41ae", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " A B\n", "0 1 2\n", "1 2 3\n", "2 3 8\n", "3 4 5\n", "4 5 6\n", "5 6 7\n", "6 30 8\n", "7 3 9\n", "8 3 0\n", "9 4 3\n", "10 5 4\n", "11 3 5\n", "12 2 6\n", "13 4 7\n", "14 5 2\n", "15 23 4\n", "16 2 5\n", "17 5 6\n", "18 3 4" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df_obj = pd.read_csv(r'data\\outlier_data.csv')\n", "df_obj" ] }, { "cell_type": "code", "execution_count": 21, "id": "4c021ab2-02c8-43ea-84ba-f9ccbb76152d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6 30\n", "Name: A, dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "three_sigma(df_obj['A'])" ] }, { "cell_type": "code", "execution_count": 22, "id": "af7ac4c6-e8a8-41af-99a3-65e64deddef2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Series([], Name: B, dtype: int64)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "three_sigma(df_obj['B'])" ] }, { "cell_type": "markdown", "id": "2cb12709-132a-4ab6-b0a9-dc430487bacb", "metadata": {}, "source": [ "### 2.\t通过箱形图检测异常值" ] }, { "cell_type": "code", "execution_count": 23, "id": "e420249c-60b2-4add-80b5-cec5af7388f8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_obj = pd.read_csv(r'data\\example_data.csv')\n", "df_obj.boxplot(column=['A', 'B']) # 绘制箱形图" ] }, { "cell_type": "code", "execution_count": 24, "id": "fc29fac3-d0c1-450e-94d6-abcee88b20a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([50, 23], dtype=object)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df_obj = pd.read_csv(r'data\\example_data.csv')\n", "# 绘制箱形图，指定返回值的类型为字典\n", "box_dict = df_obj.boxplot(column=['A', 'B'], return_type='dict') \n", "# 获取箱形中左侧图形中空心圆圈的y值\n", "box_dict['fliers'][0].get_ydata()" ] }, { "cell_type": "code", "execution_count": 25, "id": "c7d1b7da-a185-452a-971b-1b727a3309f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5 50\n", "15 23\n", "Name: A, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "def box_outliers(ser):\n", " # 对待检测的数据集进行排序\n", " new_ser = ser.sort_values()\n", " # 判断数据的总数量是奇数还是偶数\n", " if new_ser.count() % 2 == 0:\n", " # 计算Q3、Q1、IQR\n", " Q3 = new_ser[int(len(new_ser) / 2):].median()\n", " Q1 = new_ser[:int(len(new_ser) / 2)].median()\n", " elif new_ser.count() % 2 != 0:\n", " Q3 = new_ser[int((len(new_ser)-1) / 2):].median()\n", " Q1 = new_ser[:int((len(new_ser)-1) / 2)].median()\n", " IQR = round(Q3 - Q1, 1)\n", " rule = (round(Q3+1.5*IQR, 1) < ser)|(round(Q1-1.5*IQR, 1) > ser)\n", " index = np.arange(ser.shape[0])[rule]\n", " # 获取异常值及其对应的索引\n", " outliers = ser.iloc[index]\n", " return outliers\n", "box_outliers(df_obj['A'])" ] }, { "cell_type": "markdown", "id": "838d9ae3-1dc5-45ce-a48c-d3e7f77be692", "metadata": {}, "source": [ "## 异常值的处理" ] }, { "cell_type": "code", "execution_count": 26, "id": "fafd90f3-0bd4-47f0-aca9-1f90759cce89", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " A B\n", "0 1 2\n", "1 2 3\n", "2 3 8\n", "3 4 5\n", "4 5 6\n", "5 2 7\n", "6 2 8\n", "7 3 9\n", "8 3 0\n", "9 4 3\n", "10 5 4\n", "11 3 5\n", "12 2 6\n", "13 4 7\n", "14 5 2\n", "15 3 4\n", "16 2 5\n", "17 5 6\n", "18 3 4" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_obj.replace(to_replace=[23, 50], value=[3, 2]) # 替换多个异常值" ] }, { "cell_type": "markdown", "id": "29954467-dfe4-41d3-98c0-a3d276b5098a", "metadata": {}, "source": [ "## 转换数据类型" ] }, { "cell_type": "markdown", "id": "daa7e8be-a090-461e-9383-aae15a0041bf", "metadata": {}, "source": [ "### 1.\t通过astype()方法转换数据的类型" ] }, { "cell_type": "code", "execution_count": 28, "id": "3e223533-0297-466d-abf9-8bd692bf72b0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A object\n", "B object\n", "C object\n", "dtype: object" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.DataFrame({'A': ['1', '1.2', '4.2'], 'B': ['-9', '70', '88'],\n", " 'C': ['x', '5.0', '0']})\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 29, "id": "ec88d100-fd1a-4951-9f32-bfe72966c303", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 -9\n", "1 70\n", "2 88\n", "Name: B, dtype: int32" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['B'].astype(dtype='int') # 转换为整数类型" ] }, { "cell_type": "markdown", "id": "1e182566-4e73-4597-a1e2-7a18842fe79b", "metadata": {}, "source": [ "### 2.\t通过to_numeric()函数转换数据类型" ] }, { "cell_type": "code", "execution_count": 30, "id": "0d005fe8-a1a0-4a20-9af3-ebe4dfbee91c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.5\n", "1 2.2\n", "2 3.8\n", "dtype: object" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "ser_obj = pd.Series(['1.5', '2.2', '3.8'])\n", "ser_obj" ] }, { "cell_type": "code", "execution_count": 31, "id": "08219081-5e6c-457c-802a-27beacc96afe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.5\n", "1 2.2\n", "2 3.8\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 将object类型转换浮点型\n", "pd.to_numeric(ser_obj)" ] }, { "cell_type": "code", "execution_count": 32, "id": "8581fc78-8ab4-4111-ad64-cbee8d4e62df", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Unable to parse string \"problem\" at position 2", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32mlib.pyx:2374\u001b[0m, in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Unable to parse string \"problem\"", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[32], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m ser_obj[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mproblem\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 2\u001b[0m pd\u001b[38;5;241m.\u001b[39mto_numeric(ser_obj)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\tools\\numeric.py:222\u001b[0m, in \u001b[0;36mto_numeric\u001b[1;34m(arg, errors, downcast, dtype_backend)\u001b[0m\n\u001b[0;32m 220\u001b[0m coerce_numeric \u001b[38;5;241m=\u001b[39m errors \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 222\u001b[0m values, new_mask \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_numeric( \u001b[38;5;66;03m# type: ignore[call-overload] # noqa: E501\u001b[39;00m\n\u001b[0;32m 223\u001b[0m values,\n\u001b[0;32m 224\u001b[0m \u001b[38;5;28mset\u001b[39m(),\n\u001b[0;32m 225\u001b[0m coerce_numeric\u001b[38;5;241m=\u001b[39mcoerce_numeric,\n\u001b[0;32m 226\u001b[0m convert_to_masked_nullable\u001b[38;5;241m=\u001b[39mdtype_backend \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default\n\u001b[0;32m 227\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values_dtype, StringDtype)\n\u001b[0;32m 228\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m values_dtype\u001b[38;5;241m.\u001b[39mstorage \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow_numpy\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 229\u001b[0m )\n\u001b[0;32m 230\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m 231\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32mlib.pyx:2416\u001b[0m, in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Unable to parse string \"problem\" at position 2" ] } ], "source": [ "ser_obj[2] = 'problem'\n", "pd.to_numeric(ser_obj)" ] }, { "cell_type": "code", "execution_count": 33, "id": "ec66e97e-324b-4241-944f-8b63187b8c86", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.5\n", "1 2.2\n", "2 problem\n", "dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 忽略非数字的值\n", "pd.to_numeric(ser_obj, errors='ignore')" ] }, { "cell_type": "code", "execution_count": 34, "id": "1ae224c9-3d85-4b7b-bc40-e69a0a8cb4b0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.5\n", "1 2.2\n", "2 NaN\n", "dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 将非数字的值设置成NaN\n", "pd.to_numeric(ser_obj, errors ='coerce')" ] }, { "cell_type": "markdown", "id": "7f1b467f-668e-452a-85c6-4094eaa48cec", "metadata": {}, "source": [ "## 查看摘要信息" ] }, { "cell_type": "code", "execution_count": 36, "id": "18ef0db3-6918-4443-a1ce-bee395d4d495", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " No1 No2 No3 No4 No5\n", "0 1 2 3.0 4 5\n", "1 6 7 8.0 9 10\n", "2 11 12 NaN 14 15\n", "3 16 17 NaN 19 20\n", "4 21 22 23.0 24 25\n", "5 26 27 28.0 29 30" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "arr_2d = np.arange(1, 31).reshape((6, 5)) \n", "df_obj = pd.DataFrame(data=arr_2d, columns=['No1', 'No2', 'No3', 'No4', 'No5'])\n", "# 将指定位置的值修改为NaN\n", "df_obj.iloc[2, 2] = np.NaN\n", "df_obj.iloc[3, 2] = np.NaN\n", "df_obj" ] }, { "cell_type": "code", "execution_count": 37, "id": "5ba620d3-31b2-4fcb-97df-81394e2245db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6 entries, 0 to 5\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 No1 6 non-null int32 \n", " 1 No2 6 non-null int32 \n", " 2 No3 4 non-null float64\n", " 3 No4 6 non-null int32 \n", " 4 No5 6 non-null int32 \n", "dtypes: float64(1), int32(4)\n", "memory usage: 276.0 bytes\n" ] } ], "source": [ "df_obj.info() # 查看df_obj对象的摘要信息" ] }, { "cell_type": "markdown", "id": "99fa9dc2-6a9b-4190-9814-b20d472571a2", "metadata": {}, "source": [ "## 【任务4-1】清洗二手房数据" ] }, { "cell_type": "markdown", "id": "d53e1360-e097-42f8-9659-209a4980f36c", "metadata": {}, "source": [ "### 1.\t获取二手房数据" ] }, { "cell_type": "code", "execution_count": 38, "id": "ed740571-bdee-4f33-a835-fe5a0da20722", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 500 entries, 0 to 499\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 区 500 non-null object\n", " 1 小区名称 499 non-null object\n", " 2 标题 500 non-null object\n", " 3 房屋信息 500 non-null object\n", " 4 关注 500 non-null object\n", " 5 地铁 211 non-null object\n", " 6 单价(元/平米) 500 non-null object\n", "dtypes: object(7)\n", "memory usage: 27.5+ KB\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "houses_one = pd.read_excel(r'data\\secondhandhouse_one.xlsx')\n", "houses_one.info()" ] }, { "cell_type": "code", "execution_count": 39, "id": "c27f8e65-54f6-403e-8457-0421401c8666", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 500 entries, 0 to 499\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 区 500 non-null object \n", " 1 小区名称 499 non-null object \n", " 2 标题 500 non-null object \n", " 3 房屋信息 500 non-null object \n", " 4 关注 500 non-null object \n", " 5 地铁 211 non-null object \n", " 6 单价(元/平米) 500 non-null float64\n", "dtypes: float64(1), object(6)\n", "memory usage: 27.5+ KB\n" ] } ], "source": [ "# 转换单价(元/平方米)一列的数据类型\n", "import re\n", "old_values = []\n", "new_values = []\n", "for price_text in houses_one['单价(元/平方米)']:\n", " result = re.findall('\\d+.\\d元$', str(price_text))\n", " if len(result) != 0:\n", " old_values.append(result[0])\n", " new_result = result[0][:-1]\n", " new_values.append(new_result)\n", "new_house_one = houses_one.replace(to_replace=old_values, value=new_values)\n", "# 将数据类型由object转换为float64\n", "new_house_one['单价(元/平方米)'] = new_house_one['单价(元/平方米)'].astype(dtype='float')\n", "new_house_one.info()" ] }, { "cell_type": "markdown", "id": "f91d2508-65c6-4bff-8dce-63ca2c0fddd9", "metadata": {}, "source": [ "### 2.\t检测与处理缺失值" ] }, { "cell_type": "code", "execution_count": 41, "id": "6d30cc82-2b38-4c16-b2ba-6113d3207ebf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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数量占比(%)
地铁28957.8
小区名称10.2
区00.0
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房屋信息00.0
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" ], "text/plain": [ " 数量 占比(%)\n", "地铁 289 57.8\n", "小区名称 1 0.2\n", "区 0 0.0\n", "标题 0 0.0\n", "房屋信息 0 0.0\n", "关注 0 0.0\n", "单价(元/平米) 0 0.0" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def missing_values_table(df):\n", " # 计算每列包含缺失值的数量\n", " mis_val = df.isnull().sum()\n", " # 计算所有列缺失值⽐例\n", " mis_val_percent = df.isnull().sum() / len(df) * 100\n", " # 将数量和比例组合成DataFrame类的对象\n", " mis_val_table = pd.DataFrame({'数量':mis_val, '占比(%)':mis_val_percent})\n", " # 按照缺失值的数量降序排列\n", " mis_val_table_sorted = mis_val_table.sort_values('数量',\n", " ascending=False)\n", " return mis_val_table_sorted\n", "# 统计缺失值的数量和占比情况\n", "missing_values_table(new_house_one) " ] }, { "cell_type": "code", "execution_count": 42, "id": "f5dc4530-14e1-4745-bd0e-079cb981c5b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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区小区名称标题房屋信息关注地铁单价(元/平米)
0锦江翡翠城四期翡翠城四期跃层 采光视野好 可看沙河 客厅带有阳台高楼层(共29层)| 2009年建 |2室1厅 | 85.21平米 | 东南331人关注/ 5月前发布近地铁176036.0
1锦江时代豪庭一期时代豪庭套三 中间楼层 有装修 业主处理资产出售中楼层(共38层)| 2009年建 |3室1厅 | 155.79平米| 东南137人关注/ 5月前发布NaN26959.4
2锦江卓锦城六期卓锦城六期紫郡房源，套三，进门带入户中楼层(共31层)| 2014年建 |3室1厅 | 89.33平米| 西南36人关注 / 23天前发布NaN22612.8
3锦江星城银座春熙路太古里标准套一出售，现租给民宿。高楼层(共11层) | 2003年建 | 1室0厅 | 51.07平米 | 南29人关注 / 5月前发布近地铁18014.5
4锦江新莲新苑新莲新苑优质套三，诚心出售，近沙河，采光视野好。高楼层(共7层) | 2001年建 | 3室1厅 | 77.7平米 | 东南14人关注 / 5月前发布NaN13513.5
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495锦江东顺城南街51号东顺城南街51号院，无抵押。满五唯一没有个税高楼层(共7层) | 1992年建 | 2室2厅 | 66.1平米 | 南78人关注 / 1年前发布近地铁21028.7
496锦江育才都市家园二期育才套二、有家具家电、房东自住低楼层(共11层) | 2006年建 | 2室1厅 | 80.74平米 | 东南31人关注 / 7月前发布近地铁19816.7
497锦江嘉和园二期多层住宅无电梯，户型结构合理，居家适宜高楼层(共6层) | 2002年建 | 1室1厅 | 55.42平米 | 东南13人关注 / 9月前发布近地铁12630.8
498锦江学道街42号省教育厅单位房 大套三 采光好 适合一家人居住低楼层(共28层) | 2002年建 | 3室2厅 | 146.77平米 | 西南33人关注 / 8月前发布NaN28616.2
499锦江东洪广厦东洪广厦 3室2厅 东南 西北中楼层(共32层) | 2013年建 | 3室2厅 | 90.04平米 | 东南 西北2人关注 / 19天前发布NaN16825.9
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499 rows × 7 columns

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" ], "text/plain": [ " 区 小区名称 标题 \\\n", "0 锦江 翡翠城四期 翡翠城四期跃层 采光视野好 可看沙河 客厅带有阳台 \n", "1 锦江 时代豪庭一期 时代豪庭套三 中间楼层 有装修 业主处理资产出售 \n", "2 锦江 卓锦城六期 卓锦城六期紫郡房源，套三，进门带入户 \n", "3 锦江 星城银座 春熙路太古里标准套一出售，现租给民宿。 \n", "4 锦江 新莲新苑 新莲新苑优质套三，诚心出售，近沙河，采光视野好。 \n", ".. .. ... ... \n", "495 锦江 东顺城南街51号 东顺城南街51号院，无抵押。满五唯一没有个税 \n", "496 锦江 育才都市家园二期 育才套二、有家具家电、房东自住 \n", "497 锦江 嘉和园二期 多层住宅无电梯，户型结构合理，居家适宜 \n", "498 锦江 学道街42号 省教育厅单位房 大套三 采光好 适合一家人居住 \n", "499 锦江 东洪广厦 东洪广厦 3室2厅 东南 西北 \n", "\n", " 房屋信息 关注 地铁 \\\n", "0 高楼层(共29层)| 2009年建 |2室1厅 | 85.21平米 | 东南 331人关注/ 5月前发布 近地铁 \n", "1 中楼层(共38层)| 2009年建 |3室1厅 | 155.79平米| 东南 137人关注/ 5月前发布 NaN \n", "2 中楼层(共31层)| 2014年建 |3室1厅 | 89.33平米| 西南 36人关注 / 23天前发布 NaN \n", "3 高楼层(共11层) | 2003年建 | 1室0厅 | 51.07平米 | 南 29人关注 / 5月前发布 近地铁 \n", "4 高楼层(共7层) | 2001年建 | 3室1厅 | 77.7平米 | 东南 14人关注 / 5月前发布 NaN \n", ".. ... ... ... \n", "495 高楼层(共7层) | 1992年建 | 2室2厅 | 66.1平米 | 南 78人关注 / 1年前发布 近地铁 \n", "496 低楼层(共11层) | 2006年建 | 2室1厅 | 80.74平米 | 东南 31人关注 / 7月前发布 近地铁 \n", "497 高楼层(共6层) | 2002年建 | 1室1厅 | 55.42平米 | 东南 13人关注 / 9月前发布 近地铁 \n", "498 低楼层(共28层) | 2002年建 | 3室2厅 | 146.77平米 | 西南 33人关注 / 8月前发布 NaN \n", "499 中楼层(共32层) | 2013年建 | 3室2厅 | 90.04平米 | 东南 西北 2人关注 / 19天前发布 NaN \n", "\n", " 单价(元/平米) \n", "0 176036.0 \n", "1 26959.4 \n", "2 22612.8 \n", "3 18014.5 \n", "4 13513.5 \n", ".. ... \n", "495 21028.7 \n", "496 19816.7 \n", "497 12630.8 \n", "498 28616.2 \n", "499 16825.9 \n", "\n", "[499 rows x 7 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 删除小区名称一列的缺失值\n", "new_house_one = new_house_one.dropna(subset=['小区名称'])\n", "new_house_one" ] }, { "cell_type": "markdown", "id": "c81b89a4-d439-4321-b95b-792bf2345d04", "metadata": {}, "source": [ "### 3.\t检测与处理重复值" ] }, { "cell_type": "code", "execution_count": 44, "id": "ddf852b9-b30a-4ed1-a7fc-19eb72f3f54d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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区小区名称标题房屋信息关注地铁单价(元/平米)
54锦江柳江新居五期琉璃场柳江新居五期带装修套二，房东诚心出售低楼层(共18层) | 2010年建 | 2室1厅 | 80.97平米 | 南 北58人关注 / 3月前发布近地铁13832.3
55锦江锦洲花园此房是锦洲花园中庭跃层，顶楼带大花园高楼层(共6层) | 2004年建 | 3室2厅 | 124.68平米 | 西北135人关注 / 5月前发布NaN19890.9
97锦江澳龙名城澳龙名城标准套四双卫随时可看房！低楼层(共16层) | 2009年建 | 4室2厅 | 116.58平米 | 东南 西北95人关注 / 8月前发布近地铁25561.8
98锦江嘉和园二期锦江 川师 狮子山 嘉和园 套二出售低楼层(共6层) | 2002年建 | 2室2厅 | 74平米 | 东19人关注 / 8月前发布近地铁12567.6
99锦江钢管厂五区海椒市街15号标准套二，户型方正不临街。高楼层(共7层) | 1988年建 | 2室1厅 | 82平米 | 东南60人关注 / 1年前发布NaN11097.6
100锦江水碾河路南46号水碾河 底层 家带店 标准套二低楼层(共6层) | 1998年建 | 2室1厅 | 56.28平米 | 东南55人关注 / 4月前发布近地铁12970.9
101锦江蓝润锦江春天蓝润锦江春天 标准套二 家具家电全带低楼层(共18层) | 2015年建 | 2室1厅 | 54.29平米 | 西南7人关注 / 3月前发布NaN20261.6
102锦江宏济新路95号装修很好的套二，拎包入住，相当新高楼层(共6层) | 1985年建 | 2室1厅 | 56.76平米 | 西南123人关注 / 7月前发布NaN13037.4
129锦江绿地468公馆三期绿地468公馆三期清水套二，户型方正。高楼层(共33层) | 2015年建 | 2室1厅 | 77平米 | 东南78人关注 / 4月前发布NaN20324.7
254锦江沙河壹号一期沙河壹号，房东诚心卖，高楼层，采光好，视野开阔！！高楼层(共34层) | 2011年建 | 2室1厅 | 72.89平米 | 西123人关注 / 8月前发布近地铁18521.1
255锦江嘉和园一期房东诚心出售，交通便利 配套齐全。中楼层(共6层) | 2002年建 | 2室2厅 | 78.29平米 | 东南 西北20人关注 / 6月前发布近地铁12517.6
256锦江鑫苑名家二期鑫苑名家套三住宅，居家装修，业主自住保养好。中楼层(共40层) | 2012年建 | 3室1厅 | 86.03平米 | 北24人关注 / 3月前发布NaN20341.7
257锦江鑫苑名家一期鑫苑名家一期，套二带装修，中间楼层，高楼层(共34层) | 2011年建 | 2室1厅 | 79平米 | 东南48人关注 / 4月前发布NaN15569.6
258锦江紫东梵谷二期紫东丽景套二，户型方正，低楼层低楼层(共6层) | 2008年建 | 2室2厅 | 83.25平米 | 东南51人关注 / 1年前发布NaN13813.8
259锦江世纪朝阳九眼桥一环边世纪朝阳对中庭套三高楼层(共26层) | 2005年建 | 3室1厅 | 126.65平米 | 东北14人关注 / 3月前发布NaN22108.2
260锦江天涯南苑带屋顶花园 标准套三双卫 带阳台高楼层(共7层) | 2000年建 | 3室2厅 | 154.13平米 | 东39人关注 / 3月前发布NaN20437.3
261锦江中粮鸿云此房是标准套二，户型方正，精装修，未住人高楼层(共40层) | 2017年建 | 2室1厅 | 66平米 | 西南90人关注 / 8月前发布NaN31515.2
262锦江创意山创意山公寓，单价便宜，交通方便出行低楼层(共22层) | 2017年建 | 2室1厅 | 70.24平米 | 南9人关注 / 5月前发布NaN10962.4
263锦江总府花园总府花园 有电梯 看房需要提前预约低楼层(共30层) | 2000年建 | 3室1厅 | 98.55平米 | 西南62人关注 / 9月前发布近地铁20801.6
349锦江蓝润锦江春天蓝润锦江春天套三，东北朝向，看房预约。中楼层(共18层) | 2016年建 | 3室2厅 | 71.76平米 | 东北137人关注 / 1年前发布NaN18812.7
350锦江西部国际金融中心西部国际高品质小区，开发商统一装修，保养好，采光好高楼层(共47层) 2室1厅 | 82.64平米 | 东 东北2人关注 / 28天前发布近地铁37512.1
477锦江海桐二期海桐二期居家套一，客厅带阳台，卧室带飘窗中楼层(共18层) | 2010年建 | 1室1厅 | 50.18平米 | 东南35人关注 / 1年前发布NaN13949.8
478锦江翡翠城四期翡翠城四期套二双卫，中间楼层，视野好。高楼层(共29层) | 2009年建 | 2室1厅 | 96.57平米 | 东南12人关注 / 9月前发布近地铁21331.7
479锦江锦江城市花园二期锦江区 三圣乡 锦江城市花园二期，房东诚心出售！高楼层(共34层) | 2010年建 | 3室1厅 | 81.82平米 | 东南20人关注 / 4月前发布NaN18235.2
\n", "
" ], "text/plain": [ " 区 小区名称 标题 \\\n", "54 锦江 柳江新居五期 琉璃场柳江新居五期带装修套二，房东诚心出售 \n", "55 锦江 锦洲花园 此房是锦洲花园中庭跃层，顶楼带大花园 \n", "97 锦江 澳龙名城 澳龙名城标准套四双卫随时可看房！ \n", "98 锦江 嘉和园二期 锦江 川师 狮子山 嘉和园 套二出售 \n", "99 锦江 钢管厂五区 海椒市街15号标准套二，户型方正不临街。 \n", "100 锦江 水碾河路南46号 水碾河 底层 家带店 标准套二 \n", "101 锦江 蓝润锦江春天 蓝润锦江春天 标准套二 家具家电全带 \n", "102 锦江 宏济新路95号 装修很好的套二，拎包入住，相当新 \n", "129 锦江 绿地468公馆三期 绿地468公馆三期清水套二，户型方正。 \n", "254 锦江 沙河壹号一期 沙河壹号，房东诚心卖，高楼层，采光好，视野开阔！！ \n", "255 锦江 嘉和园一期 房东诚心出售，交通便利 配套齐全。 \n", "256 锦江 鑫苑名家二期 鑫苑名家套三住宅，居家装修，业主自住保养好。 \n", "257 锦江 鑫苑名家一期 鑫苑名家一期，套二带装修，中间楼层， \n", "258 锦江 紫东梵谷二期 紫东丽景套二，户型方正，低楼层 \n", "259 锦江 世纪朝阳 九眼桥一环边世纪朝阳对中庭套三 \n", "260 锦江 天涯南苑 带屋顶花园 标准套三双卫 带阳台 \n", "261 锦江 中粮鸿云 此房是标准套二，户型方正，精装修，未住人 \n", "262 锦江 创意山 创意山公寓，单价便宜，交通方便出行 \n", "263 锦江 总府花园 总府花园 有电梯 看房需要提前预约 \n", "349 锦江 蓝润锦江春天 蓝润锦江春天套三，东北朝向，看房预约。 \n", "350 锦江 西部国际金融中心 西部国际高品质小区，开发商统一装修，保养好，采光好 \n", "477 锦江 海桐二期 海桐二期居家套一，客厅带阳台，卧室带飘窗 \n", "478 锦江 翡翠城四期 翡翠城四期套二双卫，中间楼层，视野好。 \n", "479 锦江 锦江城市花园二期 锦江区 三圣乡 锦江城市花园二期，房东诚心出售！ \n", "\n", " 房屋信息 关注 地铁 \\\n", "54 低楼层(共18层) | 2010年建 | 2室1厅 | 80.97平米 | 南 北 58人关注 / 3月前发布 近地铁 \n", "55 高楼层(共6层) | 2004年建 | 3室2厅 | 124.68平米 | 西北 135人关注 / 5月前发布 NaN \n", "97 低楼层(共16层) | 2009年建 | 4室2厅 | 116.58平米 | 东南 西北 95人关注 / 8月前发布 近地铁 \n", "98 低楼层(共6层) | 2002年建 | 2室2厅 | 74平米 | 东 19人关注 / 8月前发布 近地铁 \n", "99 高楼层(共7层) | 1988年建 | 2室1厅 | 82平米 | 东南 60人关注 / 1年前发布 NaN \n", "100 低楼层(共6层) | 1998年建 | 2室1厅 | 56.28平米 | 东南 55人关注 / 4月前发布 近地铁 \n", "101 低楼层(共18层) | 2015年建 | 2室1厅 | 54.29平米 | 西南 7人关注 / 3月前发布 NaN \n", "102 高楼层(共6层) | 1985年建 | 2室1厅 | 56.76平米 | 西南 123人关注 / 7月前发布 NaN \n", "129 高楼层(共33层) | 2015年建 | 2室1厅 | 77平米 | 东南 78人关注 / 4月前发布 NaN \n", "254 高楼层(共34层) | 2011年建 | 2室1厅 | 72.89平米 | 西 123人关注 / 8月前发布 近地铁 \n", "255 中楼层(共6层) | 2002年建 | 2室2厅 | 78.29平米 | 东南 西北 20人关注 / 6月前发布 近地铁 \n", "256 中楼层(共40层) | 2012年建 | 3室1厅 | 86.03平米 | 北 24人关注 / 3月前发布 NaN \n", "257 高楼层(共34层) | 2011年建 | 2室1厅 | 79平米 | 东南 48人关注 / 4月前发布 NaN \n", "258 低楼层(共6层) | 2008年建 | 2室2厅 | 83.25平米 | 东南 51人关注 / 1年前发布 NaN \n", "259 高楼层(共26层) | 2005年建 | 3室1厅 | 126.65平米 | 东北 14人关注 / 3月前发布 NaN \n", "260 高楼层(共7层) | 2000年建 | 3室2厅 | 154.13平米 | 东 39人关注 / 3月前发布 NaN \n", "261 高楼层(共40层) | 2017年建 | 2室1厅 | 66平米 | 西南 90人关注 / 8月前发布 NaN \n", "262 低楼层(共22层) | 2017年建 | 2室1厅 | 70.24平米 | 南 9人关注 / 5月前发布 NaN \n", "263 低楼层(共30层) | 2000年建 | 3室1厅 | 98.55平米 | 西南 62人关注 / 9月前发布 近地铁 \n", "349 中楼层(共18层) | 2016年建 | 3室2厅 | 71.76平米 | 东北 137人关注 / 1年前发布 NaN \n", "350 高楼层(共47层) 2室1厅 | 82.64平米 | 东 东北 2人关注 / 28天前发布 近地铁 \n", "477 中楼层(共18层) | 2010年建 | 1室1厅 | 50.18平米 | 东南 35人关注 / 1年前发布 NaN \n", "478 高楼层(共29层) | 2009年建 | 2室1厅 | 96.57平米 | 东南 12人关注 / 9月前发布 近地铁 \n", "479 高楼层(共34层) | 2010年建 | 3室1厅 | 81.82平米 | 东南 20人关注 / 4月前发布 NaN \n", "\n", " 单价(元/平米) \n", "54 13832.3 \n", "55 19890.9 \n", "97 25561.8 \n", "98 12567.6 \n", "99 11097.6 \n", "100 12970.9 \n", "101 20261.6 \n", "102 13037.4 \n", "129 20324.7 \n", "254 18521.1 \n", "255 12517.6 \n", "256 20341.7 \n", "257 15569.6 \n", "258 13813.8 \n", "259 22108.2 \n", "260 20437.3 \n", "261 31515.2 \n", "262 10962.4 \n", "263 20801.6 \n", "349 18812.7 \n", "350 37512.1 \n", "477 13949.8 \n", "478 21331.7 \n", "479 18235.2 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_house_one[new_house_one.duplicated().values == True]" ] }, { "cell_type": "code", "execution_count": 45, "id": "6d7667d4-cd6e-4a32-ab5e-47c8b457485d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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区小区名称标题房屋信息关注地铁单价(元/平米)
0锦江翡翠城四期翡翠城四期跃层 采光视野好 可看沙河 客厅带有阳台高楼层(共29层)| 2009年建 |2室1厅 | 85.21平米 | 东南331人关注/ 5月前发布近地铁176036.0
1锦江时代豪庭一期时代豪庭套三 中间楼层 有装修 业主处理资产出售中楼层(共38层)| 2009年建 |3室1厅 | 155.79平米| 东南137人关注/ 5月前发布NaN26959.4
2锦江卓锦城六期卓锦城六期紫郡房源，套三，进门带入户中楼层(共31层)| 2014年建 |3室1厅 | 89.33平米| 西南36人关注 / 23天前发布NaN22612.8
3锦江星城银座春熙路太古里标准套一出售，现租给民宿。高楼层(共11层) | 2003年建 | 1室0厅 | 51.07平米 | 南29人关注 / 5月前发布近地铁18014.5
4锦江新莲新苑新莲新苑优质套三，诚心出售，近沙河，采光视野好。高楼层(共7层) | 2001年建 | 3室1厅 | 77.7平米 | 东南14人关注 / 5月前发布NaN13513.5
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470锦江东顺城南街51号东顺城南街51号院，无抵押。满五唯一没有个税高楼层(共7层) | 1992年建 | 2室2厅 | 66.1平米 | 南78人关注 / 1年前发布近地铁21028.7
471锦江育才都市家园二期育才套二、有家具家电、房东自住低楼层(共11层) | 2006年建 | 2室1厅 | 80.74平米 | 东南31人关注 / 7月前发布近地铁19816.7
472锦江嘉和园二期多层住宅无电梯，户型结构合理，居家适宜高楼层(共6层) | 2002年建 | 1室1厅 | 55.42平米 | 东南13人关注 / 9月前发布近地铁12630.8
473锦江学道街42号省教育厅单位房 大套三 采光好 适合一家人居住低楼层(共28层) | 2002年建 | 3室2厅 | 146.77平米 | 西南33人关注 / 8月前发布NaN28616.2
474锦江东洪广厦东洪广厦 3室2厅 东南 西北中楼层(共32层) | 2013年建 | 3室2厅 | 90.04平米 | 东南 西北2人关注 / 19天前发布NaN16825.9
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475 rows × 7 columns

\n", "
" ], "text/plain": [ " 区 小区名称 标题 \\\n", "0 锦江 翡翠城四期 翡翠城四期跃层 采光视野好 可看沙河 客厅带有阳台 \n", "1 锦江 时代豪庭一期 时代豪庭套三 中间楼层 有装修 业主处理资产出售 \n", "2 锦江 卓锦城六期 卓锦城六期紫郡房源，套三，进门带入户 \n", "3 锦江 星城银座 春熙路太古里标准套一出售，现租给民宿。 \n", "4 锦江 新莲新苑 新莲新苑优质套三，诚心出售，近沙河，采光视野好。 \n", ".. .. ... ... \n", "470 锦江 东顺城南街51号 东顺城南街51号院，无抵押。满五唯一没有个税 \n", "471 锦江 育才都市家园二期 育才套二、有家具家电、房东自住 \n", "472 锦江 嘉和园二期 多层住宅无电梯，户型结构合理，居家适宜 \n", "473 锦江 学道街42号 省教育厅单位房 大套三 采光好 适合一家人居住 \n", "474 锦江 东洪广厦 东洪广厦 3室2厅 东南 西北 \n", "\n", " 房屋信息 关注 地铁 \\\n", "0 高楼层(共29层)| 2009年建 |2室1厅 | 85.21平米 | 东南 331人关注/ 5月前发布 近地铁 \n", "1 中楼层(共38层)| 2009年建 |3室1厅 | 155.79平米| 东南 137人关注/ 5月前发布 NaN \n", "2 中楼层(共31层)| 2014年建 |3室1厅 | 89.33平米| 西南 36人关注 / 23天前发布 NaN \n", "3 高楼层(共11层) | 2003年建 | 1室0厅 | 51.07平米 | 南 29人关注 / 5月前发布 近地铁 \n", "4 高楼层(共7层) | 2001年建 | 3室1厅 | 77.7平米 | 东南 14人关注 / 5月前发布 NaN \n", ".. ... ... ... \n", "470 高楼层(共7层) | 1992年建 | 2室2厅 | 66.1平米 | 南 78人关注 / 1年前发布 近地铁 \n", "471 低楼层(共11层) | 2006年建 | 2室1厅 | 80.74平米 | 东南 31人关注 / 7月前发布 近地铁 \n", "472 高楼层(共6层) | 2002年建 | 1室1厅 | 55.42平米 | 东南 13人关注 / 9月前发布 近地铁 \n", "473 低楼层(共28层) | 2002年建 | 3室2厅 | 146.77平米 | 西南 33人关注 / 8月前发布 NaN \n", "474 中楼层(共32层) | 2013年建 | 3室2厅 | 90.04平米 | 东南 西北 2人关注 / 19天前发布 NaN \n", "\n", " 单价(元/平米) \n", "0 176036.0 \n", "1 26959.4 \n", "2 22612.8 \n", "3 18014.5 \n", "4 13513.5 \n", ".. ... \n", "470 21028.7 \n", "471 19816.7 \n", "472 12630.8 \n", "473 28616.2 \n", "474 16825.9 \n", "\n", "[475 rows x 7 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 删除重复值，忽略原来的索引并重新设置索引\n", "new_house_one = new_house_one.drop_duplicates(ignore_index=True)\n", "new_house_one" ] }, { "cell_type": "markdown", "id": "4604fd3d-0ee6-4053-99d1-014546a67ac3", "metadata": {}, "source": [ "### 4.\t检测与处理单价(元/平方米)一列的异常值" ] }, { "cell_type": "code", "execution_count": 49, "id": "19bbbdc0-8652-406a-9d71-182fd27b70cc", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams['font.sans-serif'] = ['SimHei']\n", "estate = new_house_one[new_house_one['小区名称'].values == '翡翠城四期']\n", "estate.boxplot(column='单价(元/平方米)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "id": "8ccab8b6-e7cc-4db9-8a14-20f843612ee3", "metadata": {}, "outputs": [], "source": [ "def box_outliers(ser):\n", " # 对需要检测的数据集进行排序\n", " new_ser = ser.sort_values()\n", " # 判断数据的总数量是奇数还是偶数\n", " if new_ser.count() % 2 == 0:\n", " # 分别计算Q3、Q1、IQR\n", " Q3 = new_ser[int(len(new_ser) / 2):].median()\n", " Q1 = new_ser[:int(len(new_ser) / 2)].median()\n", " elif new_ser.count() % 2 != 0:\n", " Q3 = new_ser[int((len(new_ser)-1) / 2):].median()\n", " Q1 = new_ser[:int((len(new_ser)-1) / 2)].median()\n", " IQR = round(Q3 - Q1, 1)\n", " rule = (round(Q3+1.5 * IQR, 1) ser)\n", " index = np.arange(ser.shape[0])[rule]\n", " # 获取包含异常值的数据\n", " outliers = ser.iloc[index]\n", " return outliers" ] }, { "cell_type": "code", "execution_count": 51, "id": "b7053412-ed2c-4c5d-960c-1766dfd348f0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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区小区名称标题房屋信息关注地铁单价(元/平米)
312锦江锦江城市花园三期锦江城市花园三期 套二户型诚心出售低楼层(共34层) | 2011年建 | 2室1厅 | 58.28平米 | 西10人关注 / 6月前发布NaN16815.4
394锦江锦江城市花园三期房子精装，保养的很好，带家具家电，房主诚心出售高楼层(共34层) | 2011年建 | 3室2厅 | 80.76平米 | 东北4人关注 / 2月前发布NaN18945.0
356锦江沙河壹号二期锦江区 双公园 沙河壹号三房双卫房型 居家精装中楼层(共32层) | 2013年建 | 3室2厅 | 91.1平米 | 东南49人关注 / 4月前发布NaN23051.6
373锦江东湖国际东湖国际跃层带花园，装修保养好，直接入住，满五年！高楼层(共33层) | 2012年建 | 4室2厅 | 142.01平米 | 东南14人关注 / 7月前发布NaN39433.8
11锦江恒大都汇华庭市中 心 太古里 都汇华庭 精装套四 顶跃 诚心出售高楼层(共50层) | 2015年建 | 4室2厅 | 201.56平米 | 西北26人关注 / 8月前发布近地铁39690.4
36锦江华都美林湾华都美林湾套二 对中庭 可看湖高楼层(共33层) | 2010年建 | 2室1厅 | 90平米 | 西南78人关注 / 7月前发布NaN16444.4
0锦江翡翠城四期翡翠城四期跃层 采光视野好 可看沙河 客厅带有阳台高楼层(共29层)| 2009年建 |2室1厅 | 85.21平米 | 东南331人关注/ 5月前发布近地铁176036.0
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" ], "text/plain": [ " 区 小区名称 标题 \\\n", "312 锦江 锦江城市花园三期 锦江城市花园三期 套二户型诚心出售 \n", "394 锦江 锦江城市花园三期 房子精装，保养的很好，带家具家电，房主诚心出售 \n", "356 锦江 沙河壹号二期 锦江区 双公园 沙河壹号三房双卫房型 居家精装 \n", "373 锦江 东湖国际 东湖国际跃层带花园，装修保养好，直接入住，满五年！ \n", "11 锦江 恒大都汇华庭 市中 心 太古里 都汇华庭 精装套四 顶跃 诚心出售 \n", "36 锦江 华都美林湾 华都美林湾套二 对中庭 可看湖 \n", "0 锦江 翡翠城四期 翡翠城四期跃层 采光视野好 可看沙河 客厅带有阳台 \n", "\n", " 房屋信息 关注 地铁 单价(元/平米) \n", "312 低楼层(共34层) | 2011年建 | 2室1厅 | 58.28平米 | 西 10人关注 / 6月前发布 NaN 16815.4 \n", "394 高楼层(共34层) | 2011年建 | 3室2厅 | 80.76平米 | 东北 4人关注 / 2月前发布 NaN 18945.0 \n", "356 中楼层(共32层) | 2013年建 | 3室2厅 | 91.1平米 | 东南 49人关注 / 4月前发布 NaN 23051.6 \n", "373 高楼层(共33层) | 2012年建 | 4室2厅 | 142.01平米 | 东南 14人关注 / 7月前发布 NaN 39433.8 \n", "11 高楼层(共50层) | 2015年建 | 4室2厅 | 201.56平米 | 西北 26人关注 / 8月前发布 近地铁 39690.4 \n", "36 高楼层(共33层) | 2010年建 | 2室1厅 | 90平米 | 西南 78人关注 / 7月前发布 NaN 16444.4 \n", "0 高楼层(共29层)| 2009年建 |2室1厅 | 85.21平米 | 东南 331人关注/ 5月前发布 近地铁 176036.0 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 定义一个列表，用于保存异常值所在的索引\n", "outliers_index_list = []\n", "for i in set(new_house_one['小区名称']):\n", " estate = new_house_one[new_house_one['小区名称'].values == i]\n", " outliers_index = box_outliers(estate['单价(元/平方米)'])\n", " if len(outliers_index) != 0:\n", " # 将异常值的索引添加到列表中\n", " outliers_index_list.append(outliers_index.index.tolist())\n", "# 将utliers_index_list由嵌套列表转换为单个列表\n", "outliers_index_single_li = sum(outliers_index_list, [])\n", "new_house_one.loc[[i for i in outliers_index_single_li]]" ] }, { "cell_type": "code", "execution_count": 52, "id": "4eaf5b79-e638-4836-b03e-fb231a1a4bcf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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区小区名称标题房屋信息关注地铁单价(元/平米)
1锦江时代豪庭一期时代豪庭套三 中间楼层 有装修 业主处理资产出售中楼层(共38层)| 2009年建 |3室1厅 | 155.79平米| 东南137人关注/ 5月前发布NaN26959.4
2锦江卓锦城六期卓锦城六期紫郡房源，套三，进门带入户中楼层(共31层)| 2014年建 |3室1厅 | 89.33平米| 西南36人关注 / 23天前发布NaN22612.8
3锦江星城银座春熙路太古里标准套一出售，现租给民宿。高楼层(共11层) | 2003年建 | 1室0厅 | 51.07平米 | 南29人关注 / 5月前发布近地铁18014.5
4锦江新莲新苑新莲新苑优质套三，诚心出售，近沙河，采光视野好。高楼层(共7层) | 2001年建 | 3室1厅 | 77.7平米 | 东南14人关注 / 5月前发布NaN13513.5
5锦江俊发星雅俊园星雅俊园优质平层公寓，业主精装修，套二带家具家电低楼层(共37层) | 2015年建 | 2室1厅 | 59平米 | 西南80人关注 / 4月前发布NaN13220.3
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470锦江东顺城南街51号东顺城南街51号院，无抵押。满五唯一没有个税高楼层(共7层) | 1992年建 | 2室2厅 | 66.1平米 | 南78人关注 / 1年前发布近地铁21028.7
471锦江育才都市家园二期育才套二、有家具家电、房东自住低楼层(共11层) | 2006年建 | 2室1厅 | 80.74平米 | 东南31人关注 / 7月前发布近地铁19816.7
472锦江嘉和园二期多层住宅无电梯，户型结构合理，居家适宜高楼层(共6层) | 2002年建 | 1室1厅 | 55.42平米 | 东南13人关注 / 9月前发布近地铁12630.8
473锦江学道街42号省教育厅单位房 大套三 采光好 适合一家人居住低楼层(共28层) | 2002年建 | 3室2厅 | 146.77平米 | 西南33人关注 / 8月前发布NaN28616.2
474锦江东洪广厦东洪广厦 3室2厅 东南 西北中楼层(共32层) | 2013年建 | 3室2厅 | 90.04平米 | 东南 西北2人关注 / 19天前发布NaN16825.9
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474 rows × 7 columns

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" ], "text/plain": [ " 区 小区名称 标题 \\\n", "1 锦江 时代豪庭一期 时代豪庭套三 中间楼层 有装修 业主处理资产出售 \n", "2 锦江 卓锦城六期 卓锦城六期紫郡房源，套三，进门带入户 \n", "3 锦江 星城银座 春熙路太古里标准套一出售，现租给民宿。 \n", "4 锦江 新莲新苑 新莲新苑优质套三，诚心出售，近沙河，采光视野好。 \n", "5 锦江 俊发星雅俊园 星雅俊园优质平层公寓，业主精装修，套二带家具家电 \n", ".. .. ... ... \n", "470 锦江 东顺城南街51号 东顺城南街51号院，无抵押。满五唯一没有个税 \n", "471 锦江 育才都市家园二期 育才套二、有家具家电、房东自住 \n", "472 锦江 嘉和园二期 多层住宅无电梯，户型结构合理，居家适宜 \n", "473 锦江 学道街42号 省教育厅单位房 大套三 采光好 适合一家人居住 \n", "474 锦江 东洪广厦 东洪广厦 3室2厅 东南 西北 \n", "\n", " 房屋信息 关注 地铁 \\\n", "1 中楼层(共38层)| 2009年建 |3室1厅 | 155.79平米| 东南 137人关注/ 5月前发布 NaN \n", "2 中楼层(共31层)| 2014年建 |3室1厅 | 89.33平米| 西南 36人关注 / 23天前发布 NaN \n", "3 高楼层(共11层) | 2003年建 | 1室0厅 | 51.07平米 | 南 29人关注 / 5月前发布 近地铁 \n", "4 高楼层(共7层) | 2001年建 | 3室1厅 | 77.7平米 | 东南 14人关注 / 5月前发布 NaN \n", "5 低楼层(共37层) | 2015年建 | 2室1厅 | 59平米 | 西南 80人关注 / 4月前发布 NaN \n", ".. ... ... ... \n", "470 高楼层(共7层) | 1992年建 | 2室2厅 | 66.1平米 | 南 78人关注 / 1年前发布 近地铁 \n", "471 低楼层(共11层) | 2006年建 | 2室1厅 | 80.74平米 | 东南 31人关注 / 7月前发布 近地铁 \n", "472 高楼层(共6层) | 2002年建 | 1室1厅 | 55.42平米 | 东南 13人关注 / 9月前发布 近地铁 \n", "473 低楼层(共28层) | 2002年建 | 3室2厅 | 146.77平米 | 西南 33人关注 / 8月前发布 NaN \n", "474 中楼层(共32层) | 2013年建 | 3室2厅 | 90.04平米 | 东南 西北 2人关注 / 19天前发布 NaN \n", "\n", " 单价(元/平米) \n", "1 26959.4 \n", "2 22612.8 \n", "3 18014.5 \n", "4 13513.5 \n", "5 13220.3 \n", ".. ... \n", "470 21028.7 \n", "471 19816.7 \n", "472 12630.8 \n", "473 28616.2 \n", "474 16825.9 \n", "\n", "[474 rows x 7 columns]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 删除指定的一行数据\n", "new_house_one.drop(0)" ] }, }