{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "本数据来源于拉勾网， 利用爬虫爬下来的。接下来我们来探讨一下基于拉勾网看数据分析职位的情况" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 导入相应的包" ] }, { "cell_type": "code", "execution_count": 265, "metadata": {}, "outputs": [], "source": [ "#全部行都能输出\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": 267, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn')\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": 268, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['font.sans-serif']=['Simhei'] #显示中文,解决图中无法显示中文的问题\n", "plt.rcParams['axes.unicode_minus']=False #设置显示中文后,负号显示受影响。解决坐标轴上乱码问题 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 数据清洗" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 导入数据" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
0玩吧[东城区]“七险一金,免费午餐,弹性不打卡,季度旅游”社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科数据分析师
1和信集团[大望路]“五险一金、周末双休”金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科数据分析师
2水滴互助[望京]“年底双薪,股票期权,扁平管理,晋升快”移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k 经验3-5年 / 本科数据分析师
3易企秀[西北旺]“七险一金、零食水果、带薪年假、年度旅游等”移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k 经验3-5年 / 本科数据分析
4齐聚科技（原呱呱视频）[东城区]“五险一金,弹性工作,餐补年终奖,节日福利”移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k 经验3-5年 / 大专数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 \\\n", "0 玩吧 [东城区] “七险一金,免费午餐,弹性不打卡,季度旅游” 社交 / B轮 / 150-500人 \n", "1 和信集团 [大望路] “五险一金、周末双休” 金融 / 不需要融资 / 2000人以上 \n", "2 水滴互助 [望京] “年底双薪,股票期权,扁平管理,晋升快” 移动互联网,医疗丨健康 / B轮 / 500-2000人 \n", "3 易企秀 [西北旺] “七险一金、零食水果、带薪年假、年度旅游等” 移动互联网 / B轮 / 150-500人 \n", "4 齐聚科技（原呱呱视频） [东城区] “五险一金,弹性工作,餐补年终奖,节日福利” 移动互联网,游戏 / C轮 / 150-500人 \n", "\n", " 城市 岗位技能 工位要求 标题 \n", "0 北京 数据分析 SPSS SQL 15k-25k 经验3-5年 / 本科 数据分析师 \n", "1 北京 数据分析 10k-20k 经验不限 / 本科 数据分析师 \n", "2 北京 数据分析 MySQL Hive 15k-25k 经验3-5年 / 本科 数据分析师 \n", "3 北京 数据运营 SPSS SQL 15k-30k 经验3-5年 / 本科 数据分析 \n", "4 北京 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 / 大专 数据分析师 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(r\"C:\\Users\\EDZ\\Desktop\\案列\\拉勾网数据分析职位分析\\data\\lagou1.csv\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以大概看一下有哪些列, 有些列里面明显包含了许多信息比如工位要求这一列， 就包含了3个信息， \n", "**薪资 经验 学历**， 所以我们后面就需要对类似的这种进行分列" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 查看数据的信息" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1674 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1673 non-null object\n", "公司地点 1672 non-null object\n", "公司短评 1674 non-null object\n", "公司级别 1674 non-null object\n", "城市 1669 non-null object\n", "岗位技能 1671 non-null object\n", "工位要求 1674 non-null object\n", "标题 1674 non-null object\n", "dtypes: object(8)\n", "memory usage: 104.8+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从这里我们可以看出一下信息：\n", "- 1 数据一共1674行， 8列\n", "- 2 数据是有缺失值的， 比如公司名称这列就缺失1个, 公司地点缺失两个， 城市缺失5个\n", "- 3 所有列的数据类型， 都是object" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据去重" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "df.drop_duplicates(inplace=True) # inplace=True在原表上进行去重， 不用返回新的DataFrame对象" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1593 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1592 non-null object\n", "公司地点 1591 non-null object\n", "公司短评 1593 non-null object\n", "公司级别 1593 non-null object\n", "城市 1588 non-null object\n", "岗位技能 1590 non-null object\n", "工位要求 1593 non-null object\n", "标题 1593 non-null object\n", "dtypes: object(8)\n", "memory usage: 112.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从这里我们可以看出数据变成了1593行， 也就是说原数据的确存在重复" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "81" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1674-1593 # 有81行是完全重复的， 删除即可" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 缺失值处理" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1593 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1592 non-null object\n", "公司地点 1591 non-null object\n", "公司短评 1593 non-null object\n", "公司级别 1593 non-null object\n", "城市 1588 non-null object\n", "岗位技能 1590 non-null object\n", "工位要求 1593 non-null object\n", "标题 1593 non-null object\n", "dtypes: object(8)\n", "memory usage: 112.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "缺失值情况如下：\n", "- 公司名称1个缺失\n", "- 公司地点2个缺失\n", "- 城市5个缺失\n", "- 岗位技能3个缺失" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 公司名称缺失值处理" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
872NaN[珠江路]“扁平管理；弹性工作”教育 / 未融资 / 50-150人南京大数据4k-6k 经验应届毕业生 / 硕士数据分析（python）实习生（南京）
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 岗位技能 工位要求 \\\n", "872 NaN [珠江路] “扁平管理；弹性工作” 教育 / 未融资 / 50-150人 南京 大数据 4k-6k 经验应届毕业生 / 硕士 \n", "\n", " 标题 \n", "872 数据分析（python）实习生（南京） " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取出公司名称有缺失的行\n", "df[df.公司名称.isna()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "像这种缺失， 我们完全是不知道的， 所以可以直接删除该行， 当然我们这里不用带算法， 也可以不用处理， \n", "所以**怎么处理都没有任何关系, 那我们这里选择删除该行。**" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "df.drop(index=872, inplace=True) # 删除该行" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [公司名称, 公司地点, 公司短评, 公司级别, 城市, 岗位技能, 工位要求, 标题]\n", "Index: []" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取出公司名称有缺失的行\n", "df[df.公司名称.isna()] # 公司名称不再有缺失值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 公司地点缺失值处理" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1592 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1592 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1592 non-null object\n", "公司级别 1592 non-null object\n", "城市 1587 non-null object\n", "岗位技能 1589 non-null object\n", "工位要求 1592 non-null object\n", "标题 1592 non-null object\n", "dtypes: object(8)\n", "memory usage: 111.9+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
876南京森马NaN“目前五家店正在急速发展。空间很大。”消费生活 / 不需要融资 / 500-2000人南京产品设计 产品策划3K-5K 经验1年以下 / 大专数据分析助理
1634三藏科技NaN“办公环境优、资深大佬指导、领导超Nice”软件开发 / 未融资 / 15-50人武汉建模 机器学习 算法10k-20k 经验3-5年 / 硕士数据分析算法工程师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "876 南京森马 NaN “目前五家店正在急速发展。空间很大。” 消费生活 / 不需要融资 / 500-2000人 南京 \n", "1634 三藏科技 NaN “办公环境优、资深大佬指导、领导超Nice” 软件开发 / 未融资 / 15-50人 武汉 \n", "\n", " 岗位技能 工位要求 标题 \n", "876 产品设计 产品策划 3K-5K 经验1年以下 / 大专 数据分析助理 \n", "1634 建模 机器学习 算法 10k-20k 经验3-5年 / 硕士 数据分析算法工程师 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取出公司地点有缺失的行\n", "df[df.公司地点.isna()] " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# 我们这里也把这两行删除了\n", "df.drop(index=[876, 1634], inplace=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [公司名称, 公司地点, 公司短评, 公司级别, 城市, 岗位技能, 工位要求, 标题]\n", "Index: []" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取出公司地点有缺失的行\n", "df[df.公司地点.isna()] # 公司地点也不再有缺失的行" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 城市缺失值处理" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1590 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1590 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1590 non-null object\n", "公司级别 1590 non-null object\n", "城市 1585 non-null object\n", "岗位技能 1587 non-null object\n", "工位要求 1590 non-null object\n", "标题 1590 non-null object\n", "dtypes: object(8)\n", "memory usage: 111.8+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
871苏宁易购[玄武区]“500强 平台大 发展好”电商,信息安全 / 上市公司 / 2000人以上NaN数据库 BI 可视化10k-15k 经验3-5年 / 本科数据分析
874苏宁云商集团互联网平台公司[仙林]“内容电商，前景无限。”电商 / 上市公司 / 2000人以上NaN电商10k-20k 经验应届毕业生 / 本科社招：高级数据分析师（非校招）
1617微派[东湖新技术…]“发展空间大,大牛带”社交 / B轮 / 50-150人NaN数据分析4k-8k 经验应届毕业生 / 本科数据分析（校招/实习）
1618小红书[光谷]“六险一金 周末双休 13薪 定期体检等”消费生活 / D轮及以上 / 500-2000人NaN数据分析10k-15k 经验3-5年 / 本科高级业务数据分析师
1619极验[东湖新技术…]“有挑战性,技术先驱,大牛队友”信息安全,数据服务 / B轮 / 50-150人NaN大数据 数据挖掘 数据分析12k-18k 经验应届毕业生 / 本科数据分析工程师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 \\\n", "871 苏宁易购 [玄武区] “500强 平台大 发展好” \n", "874 苏宁云商集团互联网平台公司 [仙林] “内容电商，前景无限。” \n", "1617 微派 [东湖新技术…] “发展空间大,大牛带” \n", "1618 小红书 [光谷] “六险一金 周末双休 13薪 定期体检等” \n", "1619 极验 [东湖新技术…] “有挑战性,技术先驱,大牛队友” \n", "\n", " 公司级别 城市 岗位技能 工位要求 \\\n", "871 电商,信息安全 / 上市公司 / 2000人以上 NaN 数据库 BI 可视化 10k-15k 经验3-5年 / 本科 \n", "874 电商 / 上市公司 / 2000人以上 NaN 电商 10k-20k 经验应届毕业生 / 本科 \n", "1617 社交 / B轮 / 50-150人 NaN 数据分析 4k-8k 经验应届毕业生 / 本科 \n", "1618 消费生活 / D轮及以上 / 500-2000人 NaN 数据分析 10k-15k 经验3-5年 / 本科 \n", "1619 信息安全,数据服务 / B轮 / 50-150人 NaN 大数据 数据挖掘 数据分析 12k-18k 经验应届毕业生 / 本科 \n", "\n", " 标题 \n", "871 数据分析 \n", "874 社招：高级数据分析师（非校招） \n", "1617 数据分析（校招/实习） \n", "1618 高级业务数据分析师 \n", "1619 数据分析工程师 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.城市.isna()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这里我们就不要直接删除了， 因为明显这里是可以填充缺失值的， 城市虽然缺失， 但是我们可以通过公司地点这一列来判断城市应该是什么， \n", "比如1617 1618 1619 **公司地点出现'东湖'， '光谷'则可以判断出这些行的城市就应该是武汉。** \n", "**另外稍微查下就知道， 玄武区 仙林在南京市。** " ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# 把871 874 两行的城市这一列填充成 ---南京\n", "df.loc[[871, 874], '城市'] = '南京' # 填充一个标量会自动进行广播" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "871 南京\n", "874 南京\n", "Name: 城市, dtype: object" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[[871, 874], '城市']" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# 把1617 1618 1619三行的城市这一列的填充成---武汉\n", "df.loc[1617:1619, '城市'] = '武汉'" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1617 武汉\n", "1618 武汉\n", "1619 武汉\n", "Name: 城市, dtype: object" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[1617:1619, '城市']" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [公司名称, 公司地点, 公司短评, 公司级别, 城市, 岗位技能, 工位要求, 标题]\n", "Index: []" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.城市.isna()] # 城市这列也不在有缺失值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 岗位技能缺失值处理" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1590 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1590 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1590 non-null object\n", "公司级别 1590 non-null object\n", "城市 1590 non-null object\n", "岗位技能 1587 non-null object\n", "工位要求 1590 non-null object\n", "标题 1590 non-null object\n", "dtypes: object(8)\n", "memory usage: 191.8+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
328北京盛业恒泰投资有限公司[朝外]“双休 五险一金 朝九晚五”移动互联网,金融 / 不需要融资 / 150-500人北京NaN6k-12k 经验不限 / 大专数据分析
475XGATE讯格得[武侯区]“五险一金、国定假期、每周水果、休息室”移动互联网,企业服务 / 不需要融资 / 50-150人成都NaN12k-20k 经验不限 / 硕士数据分析
1025旺旺集团[虹桥]“有班车、食堂；零食福利”电商,移动互联网 / 上市公司 / 2000人以上上海NaN6k-8k 经验不限 / 不限数据分析
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 \\\n", "328 北京盛业恒泰投资有限公司 [朝外] “双休 五险一金 朝九晚五” 移动互联网,金融 / 不需要融资 / 150-500人 \n", "475 XGATE讯格得 [武侯区] “五险一金、国定假期、每周水果、休息室” 移动互联网,企业服务 / 不需要融资 / 50-150人 \n", "1025 旺旺集团 [虹桥] “有班车、食堂；零食福利” 电商,移动互联网 / 上市公司 / 2000人以上 \n", "\n", " 城市 岗位技能 工位要求 标题 \n", "328 北京 NaN 6k-12k 经验不限 / 大专 数据分析 \n", "475 成都 NaN 12k-20k 经验不限 / 硕士 数据分析 \n", "1025 上海 NaN 6k-8k 经验不限 / 不限 数据分析 " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取出岗位技能有缺失的行\n", "df[df.岗位技能.isna()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "岗位技能我们很难获取， 那我们就用户一个空字符串来填充" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "df['岗位技能'] = df.岗位技能.fillna('')" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [公司名称, 公司地点, 公司短评, 公司级别, 城市, 岗位技能, 工位要求, 标题]\n", "Index: []" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提取出岗位技能有缺失的行\n", "df[df.岗位技能.isna()] # 岗位技能也不在有缺失" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
328北京盛业恒泰投资有限公司[朝外]“双休 五险一金 朝九晚五”移动互联网,金融 / 不需要融资 / 150-500人北京6k-12k 经验不限 / 大专数据分析
475XGATE讯格得[武侯区]“五险一金、国定假期、每周水果、休息室”移动互联网,企业服务 / 不需要融资 / 50-150人成都12k-20k 经验不限 / 硕士数据分析
1025旺旺集团[虹桥]“有班车、食堂；零食福利”电商,移动互联网 / 上市公司 / 2000人以上上海6k-8k 经验不限 / 不限数据分析
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 \\\n", "328 北京盛业恒泰投资有限公司 [朝外] “双休 五险一金 朝九晚五” 移动互联网,金融 / 不需要融资 / 150-500人 \n", "475 XGATE讯格得 [武侯区] “五险一金、国定假期、每周水果、休息室” 移动互联网,企业服务 / 不需要融资 / 50-150人 \n", "1025 旺旺集团 [虹桥] “有班车、食堂；零食福利” 电商,移动互联网 / 上市公司 / 2000人以上 \n", "\n", " 城市 岗位技能 工位要求 标题 \n", "328 北京 6k-12k 经验不限 / 大专 数据分析 \n", "475 成都 12k-20k 经验不限 / 硕士 数据分析 \n", "1025 上海 6k-8k 经验不限 / 不限 数据分析 " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.岗位技能==''] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注意缺失值是NaN, 现在这里是空字符串， 两者是不一样的" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1590 entries, 0 to 1673\n", "Data columns (total 8 columns):\n", "公司名称 1590 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1590 non-null object\n", "公司级别 1590 non-null object\n", "城市 1590 non-null object\n", "岗位技能 1590 non-null object\n", "工位要求 1590 non-null object\n", "标题 1590 non-null object\n", "dtypes: object(8)\n", "memory usage: 191.8+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "到这里我们就把缺失值处理完了" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 去掉 公司地点 这列两边的中括号" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
0玩吧[东城区]“七险一金,免费午餐,弹性不打卡,季度旅游”社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科数据分析师
1和信集团[大望路]“五险一金、周末双休”金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科数据分析师
2水滴互助[望京]“年底双薪,股票期权,扁平管理,晋升快”移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k 经验3-5年 / 本科数据分析师
3易企秀[西北旺]“七险一金、零食水果、带薪年假、年度旅游等”移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k 经验3-5年 / 本科数据分析
4齐聚科技（原呱呱视频）[东城区]“五险一金,弹性工作,餐补年终奖,节日福利”移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k 经验3-5年 / 大专数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 \\\n", "0 玩吧 [东城区] “七险一金,免费午餐,弹性不打卡,季度旅游” 社交 / B轮 / 150-500人 \n", "1 和信集团 [大望路] “五险一金、周末双休” 金融 / 不需要融资 / 2000人以上 \n", "2 水滴互助 [望京] “年底双薪,股票期权,扁平管理,晋升快” 移动互联网,医疗丨健康 / B轮 / 500-2000人 \n", "3 易企秀 [西北旺] “七险一金、零食水果、带薪年假、年度旅游等” 移动互联网 / B轮 / 150-500人 \n", "4 齐聚科技（原呱呱视频） [东城区] “五险一金,弹性工作,餐补年终奖,节日福利” 移动互联网,游戏 / C轮 / 150-500人 \n", "\n", " 城市 岗位技能 工位要求 标题 \n", "0 北京 数据分析 SPSS SQL 15k-25k 经验3-5年 / 本科 数据分析师 \n", "1 北京 数据分析 10k-20k 经验不限 / 本科 数据分析师 \n", "2 北京 数据分析 MySQL Hive 15k-25k 经验3-5年 / 本科 数据分析师 \n", "3 北京 数据运营 SPSS SQL 15k-30k 经验3-5年 / 本科 数据分析 \n", "4 北京 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 / 大专 数据分析师 " ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "df['公司地点'] = df.公司地点.map(lambda s:s[1:-1]) # map一个匿名函数搞定" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
0玩吧东城区“七险一金,免费午餐,弹性不打卡,季度旅游”社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科数据分析师
1和信集团大望路“五险一金、周末双休”金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科数据分析师
2水滴互助望京“年底双薪,股票期权,扁平管理,晋升快”移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k 经验3-5年 / 本科数据分析师
3易企秀西北旺“七险一金、零食水果、带薪年假、年度旅游等”移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k 经验3-5年 / 本科数据分析
4齐聚科技（原呱呱视频）东城区“五险一金,弹性工作,餐补年终奖,节日福利”移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k 经验3-5年 / 大专数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "0 玩吧 东城区 “七险一金,免费午餐,弹性不打卡,季度旅游” 社交 / B轮 / 150-500人 北京 \n", "1 和信集团 大望路 “五险一金、周末双休” 金融 / 不需要融资 / 2000人以上 北京 \n", "2 水滴互助 望京 “年底双薪,股票期权,扁平管理,晋升快” 移动互联网,医疗丨健康 / B轮 / 500-2000人 北京 \n", "3 易企秀 西北旺 “七险一金、零食水果、带薪年假、年度旅游等” 移动互联网 / B轮 / 150-500人 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 “五险一金,弹性工作,餐补年终奖,节日福利” 移动互联网,游戏 / C轮 / 150-500人 北京 \n", "\n", " 岗位技能 工位要求 标题 \n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 / 本科 数据分析师 \n", "1 数据分析 10k-20k 经验不限 / 本科 数据分析师 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 / 本科 数据分析师 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 / 本科 数据分析 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 / 大专 数据分析师 " ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 去掉公司短评两边的引号" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "df['公司短评'] = df.公司短评.map(lambda s:s[1:-1])" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求标题
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科数据分析师
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科数据分析师
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k 经验3-5年 / 本科数据分析师
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k 经验3-5年 / 本科数据分析
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k 经验3-5年 / 大专数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 移动互联网,医疗丨健康 / B轮 / 500-2000人 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 移动互联网 / B轮 / 150-500人 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 移动互联网,游戏 / C轮 / 150-500人 北京 \n", "\n", " 岗位技能 工位要求 标题 \n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 / 本科 数据分析师 \n", "1 数据分析 10k-20k 经验不限 / 本科 数据分析师 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 / 本科 数据分析师 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 / 本科 数据分析 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 / 大专 数据分析师 " ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 拆分 工位要求" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 拆分出 薪资 \n", "位置放在工位要求后面" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' 15k-25k 经验3-5年 / 本科'" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = ' 15k-25k 经验3-5年 / 本科'\n", "s" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'15k-25k'" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.split()[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 15k-25k\n", "1 10k-20k\n", "2 15k-25k\n", "3 15k-30k\n", "4 15k-20k\n", " ... \n", "1669 30k-40k\n", "1670 6k-10k\n", "1671 4k-6k\n", "1672 15k-25k\n", "1673 10k-20k\n", "Name: 工位要求, Length: 1590, dtype: object" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.工位要求.map(lambda s : s.split()[0])" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "df.insert(7, '薪资', df.工位要求.map(lambda s : s.split()[0]))" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科15k-25k数据分析师
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科10k-20k数据分析师
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k 经验3-5年 / 本科15k-25k数据分析师
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k 经验3-5年 / 本科15k-30k数据分析
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k 经验3-5年 / 大专15k-20k数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 移动互联网,医疗丨健康 / B轮 / 500-2000人 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 移动互联网 / B轮 / 150-500人 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 移动互联网,游戏 / C轮 / 150-500人 北京 \n", "\n", " 岗位技能 工位要求 薪资 标题 \n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 / 本科 15k-25k 数据分析师 \n", "1 数据分析 10k-20k 经验不限 / 本科 10k-20k 数据分析师 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 / 本科 15k-25k 数据分析师 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 / 本科 15k-30k 数据分析 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 / 大专 15k-20k 数据分析师 " ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [公司名称, 公司地点, 公司短评, 公司级别, 城市, 岗位技能, 工位要求, 薪资, 标题]\n", "Index: []" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看薪资这列缺失值情况\n", "df[df.薪资.isna()] # 薪资这一列没有缺失值" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['15k-25k', '15k-30k', '10k-20k', '20k-40k', '10k-15k', '8k-15k', '20k-30k', '15k-20k', '20k-35k', '25k-50k', '6k-8k', '12k-20k', '8k-10k', '8k-12k', '6k-10k', '25k-40k', '10k-18k', '25k-35k', '30k-50k', '18k-35k', '8k-16k', '4k-6k', '3k-5k', '12k-24k', '4k-8k', '5k-10k', '6k-12k', '7k-10k', '5k-8k', '2k-4k', '30k-40k', '18k-30k', '18k-25k', '25k-30k', '12k-18k', '4k-7k', '13k-25k', '18k-36k', '25k-45k', '30k-60k', '13k-20k', '2k-3k', '7k-14k', '8k-13k', '8k-14k', '6k-9k', '15k-22k', '14k-20k', '9k-18k', '7k-12k', '3k-4k', '20k-25k', '5k-9k', '4k-5k', '1k-2k', '5k-7k', '7k-9k', '10k-16k', '11k-18k', '18k-28k', '10k-13k', '16k-25k', '50k-80k', '12k-17k', '7k-13k', '30k-45k', '9k-12k', '40k-70k', '10k-14k', '17k-30k', '12k-16k', '18k-32k', '50k-100k', '3k-6k', '14k-25k', '12k-15k', '13k-26k', '16k-24k', '17k-34k', '19k-28k', '50k-70k', '35k-45k', '5k-6k', '12k-22k', '14k-28k', '11k-22k', '经验1-3年', '30k-55k', '45k-60k', '13k-18k', '15k-18k', '10k-17k', '17k-25k', '21k-35k', '17k-23k', '14k-18k', '35k-60k', '20k-28k', '11k-16k', '9k-11k', '7k-8k', '9k-15k', '40k-50k', '28k-40k', '20k-23k', '6k-7k', '8k-11k', '14k-24k', '经验3-5年', '11k-17k', '14k-22k', '16k-23k', '60k-100k', '40k-80k', '1k-1k', '60k-80k', '28k-55k', '16k-32k', '15k-27k', '20k-26k', '23k-46k', '9k-16k', '55k-80k', '13k-22k', '50k-65k', '11k-21k', '15k-26k', '13k-15k', '35k-50k', '21k-29k', '6k-11k', '28k-45k', '24k-25k', '40k-60k', '15k-28k', '11k-20k', '28k-38k', '26k-40k', '16k-30k', '21k-26k', '35k-55k', '9k-14k', '20K-40K', '15k-19k', '38k-65k', '30k-35k', '15k-24k', '20k-38k', '4K-5K', '25k-49k', '200k-300k', '17k-28k', '35k-70k', '35k-65k']\n" ] } ], "source": [ "# 查看薪资这列异常值情况\n", "print(list(df.薪资.value_counts().index))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "仔细观察上面的输出结果， **有出现 '经验1-3年' , '经验3-5年'这些明显的异常**， 我们把这些有异常的行提出来看一下" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
868SHEIN安德门五险一金，绩效奖金，年终奖，节日福利电商,移动互联网 / C轮 / 2000人以上南京跨境电商 数据分析经验1-3年 / 本科经验1-3年数据分析师 (MJ000443)
875南京德胜智业珠江路年轻、提薪快、五险一金、不加班、做五休二电商,消费生活 / 未融资 / 15-50人南京移动互联网 广告营销 策划 创意经验1-3年 / 不限经验1-3年推广/SEO/数据分析/运营分析/策划/创意
1624字节跳动洪山区六险一金，餐补，带薪休假，扁平管理文娱丨内容 / C轮 / 2000人以上武汉项目管理经验3-5年 / 本科经验3-5年商业化-高级数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "868 SHEIN 安德门 五险一金，绩效奖金，年终奖，节日福利 电商,移动互联网 / C轮 / 2000人以上 南京 \n", "875 南京德胜智业 珠江路 年轻、提薪快、五险一金、不加班、做五休二 电商,消费生活 / 未融资 / 15-50人 南京 \n", "1624 字节跳动 洪山区 六险一金，餐补，带薪休假，扁平管理 文娱丨内容 / C轮 / 2000人以上 武汉 \n", "\n", " 岗位技能 工位要求 薪资 标题 \n", "868 跨境电商 数据分析 经验1-3年 / 本科 经验1-3年 数据分析师 (MJ000443) \n", "875 移动互联网 广告营销 策划 创意 经验1-3年 / 不限 经验1-3年 推广/SEO/数据分析/运营分析/策划/创意 \n", "1624 项目管理 经验3-5年 / 本科 经验3-5年 商业化-高级数据分析师 " ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.薪资.map(lambda s: '经验' in s)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看出这里明显是由于工位要求没有薪资信息导致的，所以我们必须要处理， 这里的处理方式可以这样， 868 875 都是南京， 那我们看看南京其他公司工资如何， 为了更精准一点， 我们还可以利用其他信息进行定位， 比如， 868就是南京的2000人以上的公司经验是1-3年的工资， 其他类似。" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "1669 False\n", "1670 False\n", "1671 False\n", "1672 False\n", "1673 False\n", "Name: 城市, Length: 1590, dtype: bool" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.城市=='南京' # 第一个条件" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 True\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "1669 True\n", "1670 False\n", "1671 False\n", "1672 True\n", "1673 True\n", "Name: 公司级别, Length: 1590, dtype: bool" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.公司级别.map(lambda s : '2000人以上' in s) # 第二个条件" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "1669 False\n", "1670 False\n", "1671 False\n", "1672 False\n", "1673 False\n", "Name: 工位要求, Length: 1590, dtype: bool" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.工位要求.map(lambda s : '经验1-3年' in s) # 第三个条件" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
861vivo小行弹性工作,试用期全薪，丰富年终奖硬件 / 未融资 / 2000人以上南京数据分析7k-10k 经验1-3年 / 本科7k-10k数据分析专员
862卓尔智联集团有限公司安德门上市公司 500强 五险一金移动互联网,电商 / 上市公司 / 2000人以上南京电商 数据挖掘6k-12k 经验1-3年 / 本科6k-12kHB-数据分析师-南京
863鼎捷软件双龙大道五险一金全，各项补充福利其他 / 上市公司 / 2000人以上南京数据分析 数据运营 数据库7k-10k 经验1-3年 / 本科7k-10k数据分析工程师(帆软报表）
868SHEIN安德门五险一金，绩效奖金，年终奖，节日福利电商,移动互联网 / C轮 / 2000人以上南京跨境电商 数据分析经验1-3年 / 本科经验1-3年数据分析师 (MJ000443)
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "861 vivo 小行 弹性工作,试用期全薪，丰富年终奖 硬件 / 未融资 / 2000人以上 南京 \n", "862 卓尔智联集团有限公司 安德门 上市公司 500强 五险一金 移动互联网,电商 / 上市公司 / 2000人以上 南京 \n", "863 鼎捷软件 双龙大道 五险一金全，各项补充福利 其他 / 上市公司 / 2000人以上 南京 \n", "868 SHEIN 安德门 五险一金，绩效奖金，年终奖，节日福利 电商,移动互联网 / C轮 / 2000人以上 南京 \n", "\n", " 岗位技能 工位要求 薪资 标题 \n", "861 数据分析 7k-10k 经验1-3年 / 本科 7k-10k 数据分析专员 \n", "862 电商 数据挖掘 6k-12k 经验1-3年 / 本科 6k-12k HB-数据分析师-南京 \n", "863 数据分析 数据运营 数据库 7k-10k 经验1-3年 / 本科 7k-10k 数据分析工程师(帆软报表） \n", "868 跨境电商 数据分析 经验1-3年 / 本科 经验1-3年 数据分析师 (MJ000443) " ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df.城市=='南京')&(df.公司级别.map(lambda s : '2000人以上' in s)&(df.工位要求.map(lambda s : '经验1-3年' in s)))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所以从这里可以看出， 南京2000人以上的要求经验1-3年的数据分析师工资也就是7k-10k， 当然这里只是参考" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
868SHEIN安德门五险一金，绩效奖金，年终奖，节日福利电商,移动互联网 / C轮 / 2000人以上南京跨境电商 数据分析经验1-3年 / 本科经验1-3年数据分析师 (MJ000443)
875南京德胜智业珠江路年轻、提薪快、五险一金、不加班、做五休二电商,消费生活 / 未融资 / 15-50人南京移动互联网 广告营销 策划 创意经验1-3年 / 不限经验1-3年推广/SEO/数据分析/运营分析/策划/创意
1624字节跳动洪山区六险一金，餐补，带薪休假，扁平管理文娱丨内容 / C轮 / 2000人以上武汉项目管理经验3-5年 / 本科经验3-5年商业化-高级数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "868 SHEIN 安德门 五险一金，绩效奖金，年终奖，节日福利 电商,移动互联网 / C轮 / 2000人以上 南京 \n", "875 南京德胜智业 珠江路 年轻、提薪快、五险一金、不加班、做五休二 电商,消费生活 / 未融资 / 15-50人 南京 \n", "1624 字节跳动 洪山区 六险一金，餐补，带薪休假，扁平管理 文娱丨内容 / C轮 / 2000人以上 武汉 \n", "\n", " 岗位技能 工位要求 薪资 标题 \n", "868 跨境电商 数据分析 经验1-3年 / 本科 经验1-3年 数据分析师 (MJ000443) \n", "875 移动互联网 广告营销 策划 创意 经验1-3年 / 不限 经验1-3年 推广/SEO/数据分析/运营分析/策划/创意 \n", "1624 项目管理 经验3-5年 / 本科 经验3-5年 商业化-高级数据分析师 " ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.薪资.map(lambda s: '经验' in s)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "把868行的薪资改为7k-10k" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "df.loc[868, '薪资'] = '7k-10k'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "以同样的方式处理其他行" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
875南京德胜智业珠江路年轻、提薪快、五险一金、不加班、做五休二电商,消费生活 / 未融资 / 15-50人南京移动互联网 广告营销 策划 创意经验1-3年 / 不限经验1-3年推广/SEO/数据分析/运营分析/策划/创意
880南京邮数通山西路项目奖金,带薪休年假,五险一金移动互联网,信息安全 / 不需要融资 / 15-50人南京大数据 数据挖掘8k-15k 经验1-3年 / 本科8k-15k大数据分析开发工程师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "875 南京德胜智业 珠江路 年轻、提薪快、五险一金、不加班、做五休二 电商,消费生活 / 未融资 / 15-50人 南京 \n", "880 南京邮数通 山西路 项目奖金,带薪休年假,五险一金 移动互联网,信息安全 / 不需要融资 / 15-50人 南京 \n", "\n", " 岗位技能 工位要求 薪资 标题 \n", "875 移动互联网 广告营销 策划 创意 经验1-3年 / 不限 经验1-3年 推广/SEO/数据分析/运营分析/策划/创意 \n", "880 大数据 数据挖掘 8k-15k 经验1-3年 / 本科 8k-15k 大数据分析开发工程师 " ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df.城市=='南京')&(df.公司级别.map(lambda s : '15-50人' in s))&df.工位要求.map(lambda s : '经验1-3年' in s)]" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "df.loc[875, '薪资'] = '8k-15k'" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'8k-15k'" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[875, '薪资']" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
1624字节跳动洪山区六险一金，餐补，带薪休假，扁平管理文娱丨内容 / C轮 / 2000人以上武汉项目管理经验3-5年 / 本科经验3-5年商业化-高级数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 岗位技能 \\\n", "1624 字节跳动 洪山区 六险一金，餐补，带薪休假，扁平管理 文娱丨内容 / C轮 / 2000人以上 武汉 项目管理 \n", "\n", " 工位要求 薪资 标题 \n", "1624 经验3-5年 / 本科 经验3-5年 商业化-高级数据分析师 " ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.薪资.map(lambda s: '经验' in s)]" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
1597字节跳动洪山区弹性工作，免费三餐，晋升空间，团队氛围好文娱丨内容 / C轮 / 2000人以上武汉数据分析20k-40k 经验3-5年 / 本科20k-40k数据分析师
1603斗鱼直播关山下午茶；五险一金；弹性工作；前景好；文娱丨内容 / D轮及以上 / 2000人以上武汉分析8k-12k 经验3-5年 / 本科8k-12k数据分析师
1610字节跳动东湖新技术…六险一金，餐补，租房补贴，带薪休假文娱丨内容 / C轮 / 2000人以上武汉数据分析15k-30k 经验3-5年 / 本科15k-30k数据分析经理（光谷）
1612时时同云科技（成都）有限责任公司丁字桥六险一金 阿里背景移动互联网,消费生活 / C轮 / 2000人以上武汉本地生活 数据分析 数据处理8k-12k 经验3-5年 / 大专8k-12k数据分析主管
1623海康威视武汉研发中心关山六险一金；大平台信息安全,数据服务 / 上市公司 / 2000人以上武汉大数据10k-15k 经验3-5年 / 本科10k-15k数据分析工程师
1624字节跳动洪山区六险一金，餐补，带薪休假，扁平管理文娱丨内容 / C轮 / 2000人以上武汉项目管理经验3-5年 / 本科经验3-5年商业化-高级数据分析师
1636字节跳动洪山区六险一金、团队氛围好、餐补、扁平化管理文娱丨内容 / C轮 / 2000人以上武汉移动互联网 数据分析 行业分析 市场竞争分析6k-12k 经验3-5年 / 本科6k-12k商业化-高级数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 \\\n", "1597 字节跳动 洪山区 弹性工作，免费三餐，晋升空间，团队氛围好 \n", "1603 斗鱼直播 关山 下午茶；五险一金；弹性工作；前景好； \n", "1610 字节跳动 东湖新技术… 六险一金，餐补，租房补贴，带薪休假 \n", "1612 时时同云科技（成都）有限责任公司 丁字桥 六险一金 阿里背景 \n", "1623 海康威视武汉研发中心 关山 六险一金；大平台 \n", "1624 字节跳动 洪山区 六险一金，餐补，带薪休假，扁平管理 \n", "1636 字节跳动 洪山区 六险一金、团队氛围好、餐补、扁平化管理 \n", "\n", " 公司级别 城市 岗位技能 \\\n", "1597 文娱丨内容 / C轮 / 2000人以上 武汉 数据分析 \n", "1603 文娱丨内容 / D轮及以上 / 2000人以上 武汉 分析 \n", "1610 文娱丨内容 / C轮 / 2000人以上 武汉 数据分析 \n", "1612 移动互联网,消费生活 / C轮 / 2000人以上 武汉 本地生活 数据分析 数据处理 \n", "1623 信息安全,数据服务 / 上市公司 / 2000人以上 武汉 大数据 \n", "1624 文娱丨内容 / C轮 / 2000人以上 武汉 项目管理 \n", "1636 文娱丨内容 / C轮 / 2000人以上 武汉 移动互联网 数据分析 行业分析 市场竞争分析 \n", "\n", " 工位要求 薪资 标题 \n", "1597 20k-40k 经验3-5年 / 本科 20k-40k 数据分析师 \n", "1603 8k-12k 经验3-5年 / 本科 8k-12k 数据分析师 \n", "1610 15k-30k 经验3-5年 / 本科 15k-30k 数据分析经理（光谷） \n", "1612 8k-12k 经验3-5年 / 大专 8k-12k 数据分析主管 \n", "1623 10k-15k 经验3-5年 / 本科 10k-15k 数据分析工程师 \n", "1624 经验3-5年 / 本科 经验3-5年 商业化-高级数据分析师 \n", "1636 6k-12k 经验3-5年 / 本科 6k-12k 商业化-高级数据分析师 " ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['城市']=='武汉')&(df.公司级别.map(lambda s : '2000人以上' in s))&df.工位要求.map(lambda s : '经验3-5年' in s)]" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "df.loc[1624, '薪资'] = '20k-40k'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "到这里薪资的异常值就处理完了" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['15k-25k', '15k-30k', '10k-20k', '20k-40k', '10k-15k', '8k-15k', '20k-30k', '15k-20k', '20k-35k', '25k-50k', '6k-8k', '12k-20k', '8k-12k', '8k-10k', '6k-10k', '25k-40k', '25k-35k', '10k-18k', '4k-6k', '8k-16k', '30k-50k', '18k-35k', '3k-5k', '12k-24k', '4k-8k', '7k-10k', '5k-10k', '6k-12k', '5k-8k', '2k-4k', '30k-40k', '18k-30k', '18k-25k', '13k-25k', '4k-7k', '12k-18k', '18k-36k', '25k-30k', '25k-45k', '13k-20k', '2k-3k', '30k-60k', '8k-13k', '7k-14k', '6k-9k', '15k-22k', '8k-14k', '3k-4k', '14k-20k', '9k-18k', '7k-12k', '4k-5k', '20k-25k', '5k-7k', '1k-2k', '5k-9k', '10k-13k', '7k-9k', '10k-16k', '11k-18k', '18k-28k', '12k-17k', '7k-13k', '17k-30k', '30k-45k', '16k-25k', '12k-16k', '9k-12k', '50k-80k', '10k-14k', '40k-70k', '12k-15k', '13k-26k', '13k-18k', '35k-45k', '11k-22k', '12k-22k', '14k-25k', '18k-32k', '50k-100k', '30k-55k', '10k-17k', '14k-28k', '5k-6k', '50k-70k', '19k-28k', '17k-34k', '16k-24k', '3k-6k', '15k-18k', '45k-60k', '17k-25k', '14k-24k', '20k-23k', '7k-8k', '9k-15k', '11k-16k', '40k-50k', '35k-60k', '14k-18k', '9k-11k', '20k-28k', '21k-35k', '28k-40k', '17k-23k', '6k-7k', '8k-11k', '11k-17k', '14k-22k', '16k-23k', '60k-100k', '40k-80k', '1k-1k', '60k-80k', '28k-55k', '16k-32k', '15k-27k', '20k-26k', '23k-46k', '9k-16k', '55k-80k', '13k-22k', '50k-65k', '11k-21k', '15k-26k', '13k-15k', '35k-50k', '21k-29k', '6k-11k', '28k-45k', '24k-25k', '40k-60k', '15k-28k', '11k-20k', '28k-38k', '26k-40k', '16k-30k', '21k-26k', '35k-55k', '9k-14k', '20K-40K', '15k-19k', '38k-65k', '30k-35k', '15k-24k', '20k-38k', '4K-5K', '25k-49k', '200k-300k', '17k-28k', '35k-70k', '35k-65k']\n" ] } ], "source": [ "print(df.薪资.value_counts().index.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 拆分出 经验要求 \n", "位置放在薪资后面" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资标题
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科15k-25k数据分析师
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科10k-20k数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 岗位技能 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 数据分析 SPSS SQL \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 数据分析 \n", "\n", " 工位要求 薪资 标题 \n", "0 15k-25k 经验3-5年 / 本科 15k-25k 数据分析师 \n", "1 10k-20k 经验不限 / 本科 10k-20k 数据分析师 " ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(2)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'15k-25k 经验3-5年 / 本科'" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = df.工位要求[0]\n", "s" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'经验3-5年'" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.split()[1]" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 经验3-5年\n", "1 经验不限\n", "2 经验3-5年\n", "3 经验3-5年\n", "4 经验3-5年\n", " ... \n", "1669 经验5-10年\n", "1670 经验不限\n", "1671 经验不限\n", "1672 经验3-5年\n", "1673 经验应届毕业生\n", "Name: 工位要求, Length: 1590, dtype: object" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.工位要求.map(lambda s:s.split()[1])" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [], "source": [ "df.insert(8, '经验要求', df.工位要求.map(lambda s:s.split()[1]))" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资经验要求标题
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k 经验3-5年 / 本科15k-25k经验3-5年数据分析师
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k 经验不限 / 本科10k-20k经验不限数据分析师
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k 经验3-5年 / 本科15k-25k经验3-5年数据分析师
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k 经验3-5年 / 本科15k-30k经验3-5年数据分析
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k 经验3-5年 / 大专15k-20k经验3-5年数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 移动互联网,医疗丨健康 / B轮 / 500-2000人 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 移动互联网 / B轮 / 150-500人 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 移动互联网,游戏 / C轮 / 150-500人 北京 \n", "\n", " 岗位技能 工位要求 薪资 经验要求 标题 \n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 / 本科 15k-25k 经验3-5年 数据分析师 \n", "1 数据分析 10k-20k 经验不限 / 本科 10k-20k 经验不限 数据分析师 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 / 本科 15k-25k 经验3-5年 数据分析师 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 / 本科 15k-30k 经验3-5年 数据分析 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 / 大专 15k-20k 经验3-5年 数据分析师 " ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资经验要求标题
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [公司名称, 公司地点, 公司短评, 公司级别, 城市, 岗位技能, 工位要求, 薪资, 经验要求, 标题]\n", "Index: []" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看经验要求的缺失值情况\n", "df[df.经验要求.isna()] # 经验要求这列没有缺失值" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "经验3-5年 655\n", "经验1-3年 422\n", "经验5-10年 240\n", "经验不限 167\n", "经验应届毕业生 68\n", "经验1年以下 31\n", "经验10年以上 4\n", "/ 3\n", "Name: 经验要求, dtype: int64" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看经验要求这列的异常值情况\n", "df.经验要求.value_counts() # 有3个异常值/" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资经验要求标题
868SHEIN安德门五险一金，绩效奖金，年终奖，节日福利电商,移动互联网 / C轮 / 2000人以上南京跨境电商 数据分析经验1-3年 / 本科7k-10k/数据分析师 (MJ000443)
875南京德胜智业珠江路年轻、提薪快、五险一金、不加班、做五休二电商,消费生活 / 未融资 / 15-50人南京移动互联网 广告营销 策划 创意经验1-3年 / 不限8k-15k/推广/SEO/数据分析/运营分析/策划/创意
1624字节跳动洪山区六险一金，餐补，带薪休假，扁平管理文娱丨内容 / C轮 / 2000人以上武汉项目管理经验3-5年 / 本科20k-40k/商业化-高级数据分析师
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "868 SHEIN 安德门 五险一金，绩效奖金，年终奖，节日福利 电商,移动互联网 / C轮 / 2000人以上 南京 \n", "875 南京德胜智业 珠江路 年轻、提薪快、五险一金、不加班、做五休二 电商,消费生活 / 未融资 / 15-50人 南京 \n", "1624 字节跳动 洪山区 六险一金，餐补，带薪休假，扁平管理 文娱丨内容 / C轮 / 2000人以上 武汉 \n", "\n", " 岗位技能 工位要求 薪资 经验要求 标题 \n", "868 跨境电商 数据分析 经验1-3年 / 本科 7k-10k / 数据分析师 (MJ000443) \n", "875 移动互联网 广告营销 策划 创意 经验1-3年 / 不限 8k-15k / 推广/SEO/数据分析/运营分析/策划/创意 \n", "1624 项目管理 经验3-5年 / 本科 20k-40k / 商业化-高级数据分析师 " ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 提出经验要去异常值的行\n", "df[df.经验要求=='/'] # 原因就是没有薪资造成的" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [], "source": [ "df.loc[[868, 875], '经验要求'] = '经验1-3年'" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "868 经验1-3年\n", "875 经验1-3年\n", "Name: 经验要求, dtype: object" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[[868, 875], '经验要求'] " ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [], "source": [ "df.loc[1624, '经验要求'] = '经验3-5年'" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'经验3-5年'" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[1624, '经验要求']" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "经验3-5年 656\n", "经验1-3年 424\n", "经验5-10年 240\n", "经验不限 167\n", "经验应届毕业生 68\n", "经验1年以下 31\n", "经验10年以上 4\n", "Name: 经验要求, dtype: int64" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.经验要求.value_counts() # 经验要求不再有异常值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 拆分出 学历要求 \n", "位置放到 经验要求 后面" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1590 entries, 0 to 1673\n", "Data columns (total 10 columns):\n", "公司名称 1590 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1590 non-null object\n", "公司级别 1590 non-null object\n", "城市 1590 non-null object\n", "岗位技能 1590 non-null object\n", "工位要求 1590 non-null object\n", "薪资 1590 non-null object\n", "经验要求 1590 non-null object\n", "标题 1590 non-null object\n", "dtypes: object(10)\n", "memory usage: 216.6+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'15k-25k 经验3-5年 / 本科'" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = df.工位要求[0]\n", "s" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'本科'" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.split()[-1]" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 本科\n", "1 本科\n", "2 本科\n", "3 本科\n", "4 大专\n", " ..\n", "1669 不限\n", "1670 大专\n", "1671 大专\n", "1672 本科\n", "1673 本科\n", "Name: 工位要求, Length: 1590, dtype: object" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.工位要求.map(lambda s : s.split()[-1])" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "df['学历要求'] = df.工位要求.map(lambda s : s.split()[-1])" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看学历要求的缺失值\n", "df.学历要求.isna().sum() # 没有缺失" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "本科 1324\n", "不限 99\n", "大专 94\n", "硕士 70\n", "博士 2\n", "经验3-5年 1\n", "Name: 学历要求, dtype: int64" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看学历要求的异常值情况\n", "df.学历要求.value_counts() # 有一个异常值" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能工位要求薪资经验要求标题学历要求
1628烽火科技·虹旭信息技术有限公司江夏区班车,提供食宿,奖金,薪酬增长,沟通畅通信息安全,数据服务 / 不需要融资 / 150-500人武汉数据分析 数据运营7k-14k 经验3-5年7k-14k经验3-5年数据分析师经验3-5年
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 \\\n", "1628 烽火科技·虹旭信息技术有限公司 江夏区 班车,提供食宿,奖金,薪酬增长,沟通畅通 \n", "\n", " 公司级别 城市 岗位技能 工位要求 薪资 \\\n", "1628 信息安全,数据服务 / 不需要融资 / 150-500人 武汉 数据分析 数据运营 7k-14k 经验3-5年 7k-14k \n", "\n", " 经验要求 标题 学历要求 \n", "1628 经验3-5年 数据分析师 经验3-5年 " ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.学历要求=='经验3-5年'] # 该行没有学历" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "学历要求 明显是一个分类型变量， 给他改成一个众数" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 本科\n", "dtype: object" ] }, "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.学历要求.mode() # 查看学历要求的众数， 由于众数不止一个， 则返回一个Series对象" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [], "source": [ "df.loc[1628, '学历要求'] = '本科'" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "本科 1325\n", "不限 99\n", "大专 94\n", "硕士 70\n", "博士 2\n", "Name: 学历要求, dtype: int64" ] }, "execution_count": 212, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.学历要求.value_counts() # 这里处理完了异常值" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1590 entries, 0 to 1673\n", "Data columns (total 11 columns):\n", "公司名称 1590 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1590 non-null object\n", "公司级别 1590 non-null object\n", "城市 1590 non-null object\n", "岗位技能 1590 non-null object\n", "工位要求 1590 non-null object\n", "薪资 1590 non-null object\n", "经验要求 1590 non-null object\n", "标题 1590 non-null object\n", "学历要求 1590 non-null object\n", "dtypes: object(11)\n", "memory usage: 229.1+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 删除 工位要求 这一列" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [], "source": [ "del df['工位要求']" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能薪资经验要求标题学历要求
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k经验3-5年数据分析师本科
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k经验不限数据分析师本科
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k经验3-5年数据分析师本科
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k经验3-5年数据分析本科
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k经验3-5年数据分析师大专
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 移动互联网,医疗丨健康 / B轮 / 500-2000人 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 移动互联网 / B轮 / 150-500人 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 移动互联网,游戏 / C轮 / 150-500人 北京 \n", "\n", " 岗位技能 薪资 经验要求 标题 学历要求 \n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 数据分析师 本科 \n", "1 数据分析 10k-20k 经验不限 数据分析师 本科 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 数据分析师 本科 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 数据分析 本科 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 数据分析师 大专 " ] }, "execution_count": 217, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 拆分 公司级别 成三列\n", "- 公司领域\n", "- 融资阶段\n", "- 公司规模" ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司领域融资阶段公司规模
0社交B轮150-500人
1金融不需要融资2000人以上
2移动互联网,医疗丨健康B轮500-2000人
3移动互联网B轮150-500人
4移动互联网,游戏C轮150-500人
............
1669企业服务上市公司2000人以上
1670金融未融资50-150人
1671移动互联网,人工智能上市公司500-2000人
1672移动互联网不需要融资2000人以上
1673移动互联网不需要融资2000人以上
\n", "

1590 rows × 3 columns

\n", "
" ], "text/plain": [ " 公司领域 融资阶段 公司规模\n", "0 社交 B轮 150-500人\n", "1 金融 不需要融资 2000人以上\n", "2 移动互联网,医疗丨健康 B轮 500-2000人\n", "3 移动互联网 B轮 150-500人\n", "4 移动互联网,游戏 C轮 150-500人\n", "... ... ... ...\n", "1669 企业服务 上市公司 2000人以上\n", "1670 金融 未融资 50-150人\n", "1671 移动互联网,人工智能 上市公司 500-2000人\n", "1672 移动互联网 不需要融资 2000人以上\n", "1673 移动互联网 不需要融资 2000人以上\n", "\n", "[1590 rows x 3 columns]" ] }, "execution_count": 222, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = df.公司级别.str.split('/', expand=True)\n", "a.columns = ['公司领域', '融资阶段', '公司规模']\n", "a" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [], "source": [ "# 将上表拼接到df上\n", "df = pd.concat([df, a], axis=1)" ] }, { "cell_type": "code", "execution_count": 227, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k经验不限数据分析师本科金融不需要融资2000人以上
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快移动互联网,医疗丨健康 / B轮 / 500-2000人北京数据分析 MySQL Hive15k-25k经验3-5年数据分析师本科移动互联网,医疗丨健康B轮500-2000人
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等移动互联网 / B轮 / 150-500人北京数据运营 SPSS SQL15k-30k经验3-5年数据分析本科移动互联网B轮150-500人
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利移动互联网,游戏 / C轮 / 150-500人北京游戏 社交 SQL 数据分析 商业15k-20k经验3-5年数据分析师大专移动互联网,游戏C轮150-500人
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 移动互联网,医疗丨健康 / B轮 / 500-2000人 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 移动互联网 / B轮 / 150-500人 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 移动互联网,游戏 / C轮 / 150-500人 北京 \n", "\n", " 岗位技能 薪资 经验要求 标题 学历要求 公司领域 融资阶段 \\\n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 数据分析师 本科 社交 B轮 \n", "1 数据分析 10k-20k 经验不限 数据分析师 本科 金融 不需要融资 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 数据分析师 本科 移动互联网,医疗丨健康 B轮 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 数据分析 本科 移动互联网 B轮 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 数据分析师 大专 移动互联网,游戏 C轮 \n", "\n", " 公司规模 \n", "0 150-500人 \n", "1 2000人以上 \n", "2 500-2000人 \n", "3 150-500人 \n", "4 150-500人 " ] }, "execution_count": 227, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1590 entries, 0 to 1673\n", "Data columns (total 13 columns):\n", "公司名称 1590 non-null object\n", "公司地点 1590 non-null object\n", "公司短评 1590 non-null object\n", "公司级别 1590 non-null object\n", "城市 1590 non-null object\n", "岗位技能 1590 non-null object\n", "薪资 1590 non-null object\n", "经验要求 1590 non-null object\n", "标题 1590 non-null object\n", "学历要求 1590 non-null object\n", "公司领域 1590 non-null object\n", "融资阶段 1590 non-null object\n", "公司规模 1590 non-null object\n", "dtypes: object(13)\n", "memory usage: 253.9+ KB\n" ] } ], "source": [ "# 查看缺失值情况\n", "df.info() # 没有缺失值" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " 上市公司 392\n", " 不需要融资 370\n", " D轮及以上 172\n", " C轮 172\n", " 未融资 156\n", " A轮 151\n", " B轮 150\n", " 天使轮 27\n", "Name: 融资阶段, dtype: int64" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看 融资阶段有无异常值\n", "df.融资阶段.value_counts() # 融资阶段也没有异常值" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " 2000人以上 577\n", " 150-500人 366\n", " 500-2000人 330\n", " 50-150人 205\n", " 15-50人 88\n", " 少于15人 24\n", "Name: 公司规模, dtype: int64" ] }, "execution_count": 233, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查看公司规模有无异常值\n", "df.公司规模.value_counts() # 公司规模没有异常值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 删除 公司级别" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评公司级别城市岗位技能薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游社交 / B轮 / 150-500人北京数据分析 SPSS SQL15k-25k经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休金融 / 不需要融资 / 2000人以上北京数据分析10k-20k经验不限数据分析师本科金融不需要融资2000人以上
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 公司级别 城市 岗位技能 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 社交 / B轮 / 150-500人 北京 数据分析 SPSS SQL \n", "1 和信集团 大望路 五险一金、周末双休 金融 / 不需要融资 / 2000人以上 北京 数据分析 \n", "\n", " 薪资 经验要求 标题 学历要求 公司领域 融资阶段 公司规模 \n", "0 15k-25k 经验3-5年 数据分析师 本科 社交 B轮 150-500人 \n", "1 10k-20k 经验不限 数据分析师 本科 金融 不需要融资 2000人以上 " ] }, "execution_count": 235, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(2)" ] }, { "cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [], "source": [ "del df['公司级别']" ] }, { "cell_type": "code", "execution_count": 237, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评城市岗位技能薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游北京数据分析 SPSS SQL15k-25k经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休北京数据分析10k-20k经验不限数据分析师本科金融不需要融资2000人以上
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快北京数据分析 MySQL Hive15k-25k经验3-5年数据分析师本科移动互联网,医疗丨健康B轮500-2000人
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等北京数据运营 SPSS SQL15k-30k经验3-5年数据分析本科移动互联网B轮150-500人
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利北京游戏 社交 SQL 数据分析 商业15k-20k经验3-5年数据分析师大专移动互联网,游戏C轮150-500人
.......................................
1669宇通客车管城回族区高薪、福利待遇、假期多郑州BI SPSS 数据分析30k-40k经验5-10年大数据分析师不限企业服务上市公司2000人以上
1670河南千山教育科技有限公司金水区薪资待遇：8K~10K 双休郑州证券 股票 证券分析6k-10k经验不限金融数据分析师大专金融未融资50-150人
1671缓冲网络科技经开区包住，发展前景大郑州工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法4k-6k经验不限大数据分析开发工程师助理/实习生大专移动互联网,人工智能上市公司500-2000人
1672中移在线服务有限公司高新区海量数据 优质平台 发展空间大郑州数据分析 SQL15k-25k经验3-5年大数据分析师本科移动互联网不需要融资2000人以上
1673中移在线服务有限公司高新区六险两金、扁平化管理郑州分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析10k-20k经验应届毕业生政企大数据分析师本科移动互联网不需要融资2000人以上
\n", "

1590 rows × 12 columns

\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 北京 \n", "1 和信集团 大望路 五险一金、周末双休 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 北京 \n", "... ... ... ... .. \n", "1669 宇通客车 管城回族区 高薪、福利待遇、假期多 郑州 \n", "1670 河南千山教育科技有限公司 金水区 薪资待遇：8K~10K 双休 郑州 \n", "1671 缓冲网络科技 经开区 包住，发展前景大 郑州 \n", "1672 中移在线服务有限公司 高新区 海量数据 优质平台 发展空间大 郑州 \n", "1673 中移在线服务有限公司 高新区 六险两金、扁平化管理 郑州 \n", "\n", " 岗位技能 薪资 经验要求 标题 学历要求 \\\n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 数据分析师 本科 \n", "1 数据分析 10k-20k 经验不限 数据分析师 本科 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 数据分析师 本科 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 数据分析 本科 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 数据分析师 大专 \n", "... ... ... ... ... ... \n", "1669 BI SPSS 数据分析 30k-40k 经验5-10年 大数据分析师 不限 \n", "1670 证券 股票 证券分析 6k-10k 经验不限 金融数据分析师 大专 \n", "1671 工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法 4k-6k 经验不限 大数据分析开发工程师助理/实习生 大专 \n", "1672 数据分析 SQL 15k-25k 经验3-5年 大数据分析师 本科 \n", "1673 分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析 10k-20k 经验应届毕业生 政企大数据分析师 本科 \n", "\n", " 公司领域 融资阶段 公司规模 \n", "0 社交 B轮 150-500人 \n", "1 金融 不需要融资 2000人以上 \n", "2 移动互联网,医疗丨健康 B轮 500-2000人 \n", "3 移动互联网 B轮 150-500人 \n", "4 移动互联网,游戏 C轮 150-500人 \n", "... ... ... ... \n", "1669 企业服务 上市公司 2000人以上 \n", "1670 金融 未融资 50-150人 \n", "1671 移动互联网,人工智能 上市公司 500-2000人 \n", "1672 移动互联网 不需要融资 2000人以上 \n", "1673 移动互联网 不需要融资 2000人以上 \n", "\n", "[1590 rows x 12 columns]" ] }, "execution_count": 237, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "基本上到这里我们就处理好了， 接下来我们来析一下" ] }, { "cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评城市岗位技能薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游北京数据分析 SPSS SQL15k-25k经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休北京数据分析10k-20k经验不限数据分析师本科金融不需要融资2000人以上
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快北京数据分析 MySQL Hive15k-25k经验3-5年数据分析师本科移动互联网,医疗丨健康B轮500-2000人
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等北京数据运营 SPSS SQL15k-30k经验3-5年数据分析本科移动互联网B轮150-500人
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利北京游戏 社交 SQL 数据分析 商业15k-20k经验3-5年数据分析师大专移动互联网,游戏C轮150-500人
.......................................
1669宇通客车管城回族区高薪、福利待遇、假期多郑州BI SPSS 数据分析30k-40k经验5-10年大数据分析师不限企业服务上市公司2000人以上
1670河南千山教育科技有限公司金水区薪资待遇：8K~10K 双休郑州证券 股票 证券分析6k-10k经验不限金融数据分析师大专金融未融资50-150人
1671缓冲网络科技经开区包住，发展前景大郑州工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法4k-6k经验不限大数据分析开发工程师助理/实习生大专移动互联网,人工智能上市公司500-2000人
1672中移在线服务有限公司高新区海量数据 优质平台 发展空间大郑州数据分析 SQL15k-25k经验3-5年大数据分析师本科移动互联网不需要融资2000人以上
1673中移在线服务有限公司高新区六险两金、扁平化管理郑州分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析10k-20k经验应届毕业生政企大数据分析师本科移动互联网不需要融资2000人以上
\n", "

1590 rows × 12 columns

\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 北京 \n", "1 和信集团 大望路 五险一金、周末双休 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 北京 \n", "... ... ... ... .. \n", "1669 宇通客车 管城回族区 高薪、福利待遇、假期多 郑州 \n", "1670 河南千山教育科技有限公司 金水区 薪资待遇：8K~10K 双休 郑州 \n", "1671 缓冲网络科技 经开区 包住，发展前景大 郑州 \n", "1672 中移在线服务有限公司 高新区 海量数据 优质平台 发展空间大 郑州 \n", "1673 中移在线服务有限公司 高新区 六险两金、扁平化管理 郑州 \n", "\n", " 岗位技能 薪资 经验要求 标题 学历要求 \\\n", "0 数据分析 SPSS SQL 15k-25k 经验3-5年 数据分析师 本科 \n", "1 数据分析 10k-20k 经验不限 数据分析师 本科 \n", "2 数据分析 MySQL Hive 15k-25k 经验3-5年 数据分析师 本科 \n", "3 数据运营 SPSS SQL 15k-30k 经验3-5年 数据分析 本科 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 经验3-5年 数据分析师 大专 \n", "... ... ... ... ... ... \n", "1669 BI SPSS 数据分析 30k-40k 经验5-10年 大数据分析师 不限 \n", "1670 证券 股票 证券分析 6k-10k 经验不限 金融数据分析师 大专 \n", "1671 工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法 4k-6k 经验不限 大数据分析开发工程师助理/实习生 大专 \n", "1672 数据分析 SQL 15k-25k 经验3-5年 大数据分析师 本科 \n", "1673 分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析 10k-20k 经验应届毕业生 政企大数据分析师 本科 \n", "\n", " 公司领域 融资阶段 公司规模 \n", "0 社交 B轮 150-500人 \n", "1 金融 不需要融资 2000人以上 \n", "2 移动互联网,医疗丨健康 B轮 500-2000人 \n", "3 移动互联网 B轮 150-500人 \n", "4 移动互联网,游戏 C轮 150-500人 \n", "... ... ... ... \n", "1669 企业服务 上市公司 2000人以上 \n", "1670 金融 未融资 50-150人 \n", "1671 移动互联网,人工智能 上市公司 500-2000人 \n", "1672 移动互联网 不需要融资 2000人以上 \n", "1673 移动互联网 不需要融资 2000人以上 \n", "\n", "[1590 rows x 12 columns]" ] }, "execution_count": 238, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 数据分析" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 薪增一列 平均薪资 \n", "位置在薪资之后" ] }, { "cell_type": "code", "execution_count": 241, "metadata": {}, "outputs": [], "source": [ "import re" ] }, { "cell_type": "code", "execution_count": 248, "metadata": {}, "outputs": [], "source": [ "def slary(x):\n", " L = re.findall('\\d+', x)\n", " return (float(L[0])+float(L[1]))*1000/2" ] }, { "cell_type": "code", "execution_count": 249, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 20000.0\n", "1 15000.0\n", "2 20000.0\n", "3 22500.0\n", "4 17500.0\n", " ... \n", "1669 35000.0\n", "1670 8000.0\n", "1671 5000.0\n", "1672 20000.0\n", "1673 15000.0\n", "Name: 薪资, Length: 1590, dtype: float64" ] }, "execution_count": 249, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.薪资.map(slary)" ] }, { "cell_type": "code", "execution_count": 256, "metadata": {}, "outputs": [], "source": [ "df.insert(6, '平均薪资', df.薪资.map(slary))" ] }, { "cell_type": "code", "execution_count": 257, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评城市岗位技能薪资平均薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游北京数据分析 SPSS SQL15k-25k20000.0经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休北京数据分析10k-20k15000.0经验不限数据分析师本科金融不需要融资2000人以上
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快北京数据分析 MySQL Hive15k-25k20000.0经验3-5年数据分析师本科移动互联网,医疗丨健康B轮500-2000人
3易企秀西北旺七险一金、零食水果、带薪年假、年度旅游等北京数据运营 SPSS SQL15k-30k22500.0经验3-5年数据分析本科移动互联网B轮150-500人
4齐聚科技（原呱呱视频）东城区五险一金,弹性工作,餐补年终奖,节日福利北京游戏 社交 SQL 数据分析 商业15k-20k17500.0经验3-5年数据分析师大专移动互联网,游戏C轮150-500人
..........................................
1669宇通客车管城回族区高薪、福利待遇、假期多郑州BI SPSS 数据分析30k-40k35000.0经验5-10年大数据分析师不限企业服务上市公司2000人以上
1670河南千山教育科技有限公司金水区薪资待遇：8K~10K 双休郑州证券 股票 证券分析6k-10k8000.0经验不限金融数据分析师大专金融未融资50-150人
1671缓冲网络科技经开区包住，发展前景大郑州工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法4k-6k5000.0经验不限大数据分析开发工程师助理/实习生大专移动互联网,人工智能上市公司500-2000人
1672中移在线服务有限公司高新区海量数据 优质平台 发展空间大郑州数据分析 SQL15k-25k20000.0经验3-5年大数据分析师本科移动互联网不需要融资2000人以上
1673中移在线服务有限公司高新区六险两金、扁平化管理郑州分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析10k-20k15000.0经验应届毕业生政企大数据分析师本科移动互联网不需要融资2000人以上
\n", "

1590 rows × 13 columns

\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 城市 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 北京 \n", "1 和信集团 大望路 五险一金、周末双休 北京 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 北京 \n", "3 易企秀 西北旺 七险一金、零食水果、带薪年假、年度旅游等 北京 \n", "4 齐聚科技（原呱呱视频） 东城区 五险一金,弹性工作,餐补年终奖,节日福利 北京 \n", "... ... ... ... .. \n", "1669 宇通客车 管城回族区 高薪、福利待遇、假期多 郑州 \n", "1670 河南千山教育科技有限公司 金水区 薪资待遇：8K~10K 双休 郑州 \n", "1671 缓冲网络科技 经开区 包住，发展前景大 郑州 \n", "1672 中移在线服务有限公司 高新区 海量数据 优质平台 发展空间大 郑州 \n", "1673 中移在线服务有限公司 高新区 六险两金、扁平化管理 郑州 \n", "\n", " 岗位技能 薪资 平均薪资 经验要求 \\\n", "0 数据分析 SPSS SQL 15k-25k 20000.0 经验3-5年 \n", "1 数据分析 10k-20k 15000.0 经验不限 \n", "2 数据分析 MySQL Hive 15k-25k 20000.0 经验3-5年 \n", "3 数据运营 SPSS SQL 15k-30k 22500.0 经验3-5年 \n", "4 游戏 社交 SQL 数据分析 商业 15k-20k 17500.0 经验3-5年 \n", "... ... ... ... ... \n", "1669 BI SPSS 数据分析 30k-40k 35000.0 经验5-10年 \n", "1670 证券 股票 证券分析 6k-10k 8000.0 经验不限 \n", "1671 工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法 4k-6k 5000.0 经验不限 \n", "1672 数据分析 SQL 15k-25k 20000.0 经验3-5年 \n", "1673 分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析 10k-20k 15000.0 经验应届毕业生 \n", "\n", " 标题 学历要求 公司领域 融资阶段 公司规模 \n", "0 数据分析师 本科 社交 B轮 150-500人 \n", "1 数据分析师 本科 金融 不需要融资 2000人以上 \n", "2 数据分析师 本科 移动互联网,医疗丨健康 B轮 500-2000人 \n", "3 数据分析 本科 移动互联网 B轮 150-500人 \n", "4 数据分析师 大专 移动互联网,游戏 C轮 150-500人 \n", "... ... ... ... ... ... \n", "1669 大数据分析师 不限 企业服务 上市公司 2000人以上 \n", "1670 金融数据分析师 大专 金融 未融资 50-150人 \n", "1671 大数据分析开发工程师助理/实习生 大专 移动互联网,人工智能 上市公司 500-2000人 \n", "1672 大数据分析师 本科 移动互联网 不需要融资 2000人以上 \n", "1673 政企大数据分析师 本科 移动互联网 不需要融资 2000人以上 \n", "\n", "[1590 rows x 13 columns]" ] }, "execution_count": 257, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 城市的平均薪资的关系" ] }, { "cell_type": "code", "execution_count": 301, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 301, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gp = df.groupby('城市')\n", "gp" ] }, { "cell_type": "code", "execution_count": 302, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "城市\n", "北京 22702\n", "杭州 20220\n", "深圳 20026\n", "上海 19538\n", "佛山 18450\n", "广州 15460\n", "郑州 14888\n", "南京 14888\n", "苏州 14687\n", "厦门 14392\n", "武汉 14170\n", "成都 11728\n", "长沙 11125\n", "重庆 10625\n", "天津 8500\n", "西安 6833\n", "Name: 平均薪资, dtype: int64" ] }, "execution_count": 302, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = gp['平均薪资'].mean().sort_values(ascending=False)\n", "a = a.map(lambda x:int(x))\n", "a" ] }, { "cell_type": "code", "execution_count": 276, "metadata": {}, "outputs": [], "source": [ "# 导入直方图的类\n", "from pyecharts.charts import Bar\n", "\n", "# 导入配置项类\n", "from pyecharts import options as opts\n", "\n", "# 导入背景主题类\n", "from pyecharts.globals import ThemeType" ] }, { "cell_type": "code", "execution_count": 303, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 303, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "" ] }, "execution_count": 303, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "" ] }, "execution_count": 303, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 303, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 实例化一个bar图对象\n", "bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))\n", "# 添加横轴的标签\n", "bar.add_xaxis(list(a.index))\n", "bar.add_yaxis(series_name = \"各城市平均薪资直方图\", \n", " yaxis_data = a.values.tolist()) \n", "bar.set_global_opts( title_opts= opts.TitleOpts(title = '各城市平均薪资直方图')\n", " \n", " ,xaxis_opts=opts.AxisOpts(axislabel_opts={'rotate':30}) )\n", " # 使刻度旋转30度，解决横轴显示不完全的问题\n", "\n", "bar.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 将上诉平均薪资的分析过程封装成一个函数 \n", "- 根据传入的列名的不同而进行分组 \n", "- 计算该列名下不同类别的平均薪资的均值， 并按此进行降序输出, 如果类别太多， 只保留平均薪资的均值最高的前8个\n", "- 利用直方图画出上述结果" ] }, { "cell_type": "code", "execution_count": 278, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评城市岗位技能薪资平均薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游北京数据分析 SPSS SQL15k-25k20000.0经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休北京数据分析10k-20k15000.0经验不限数据分析师本科金融不需要融资2000人以上
2水滴互助望京年底双薪,股票期权,扁平管理,晋升快北京数据分析 MySQL Hive15k-25k20000.0经验3-5年数据分析师本科移动互联网,医疗丨健康B轮500-2000人
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 城市 岗位技能 薪资 平均薪资 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 北京 数据分析 SPSS SQL 15k-25k 20000.0 \n", "1 和信集团 大望路 五险一金、周末双休 北京 数据分析 10k-20k 15000.0 \n", "2 水滴互助 望京 年底双薪,股票期权,扁平管理,晋升快 北京 数据分析 MySQL Hive 15k-25k 20000.0 \n", "\n", " 经验要求 标题 学历要求 公司领域 融资阶段 公司规模 \n", "0 经验3-5年 数据分析师 本科 社交 B轮 150-500人 \n", "1 经验不限 数据分析师 本科 金融 不需要融资 2000人以上 \n", "2 经验3-5年 数据分析师 本科 移动互联网,医疗丨健康 B轮 500-2000人 " ] }, "execution_count": 278, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(3)" ] }, { "cell_type": "code", "execution_count": 304, "metadata": {}, "outputs": [], "source": [ "def bar(c): \n", " gp = df.groupby(c)\n", " a = gp['平均薪资'].mean().sort_values(ascending=False)\n", " a = a.map(lambda x:int(x))\n", " if len(a)<=8:\n", " a = a\n", " else:\n", " a = a[0:8]\n", " # 实例化一个bar图对象\n", " bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))\n", " # 添加横轴的标签\n", " bar.add_xaxis(list(a.index))\n", " bar.add_yaxis(series_name = \"各{}与平均薪资直方图\".format(c), \n", " yaxis_data = a.values.tolist()) \n", " bar.set_global_opts( title_opts= opts.TitleOpts(title = \"各{}与平均薪资直方图\".format(c))\n", " ,xaxis_opts=opts.AxisOpts(axislabel_opts={'rotate':30}) )\n", " \n", " return bar.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 学历与平均薪资的关系" ] }, { "cell_type": "code", "execution_count": 305, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
公司名称公司地点公司短评城市岗位技能薪资平均薪资经验要求标题学历要求公司领域融资阶段公司规模
0玩吧东城区七险一金,免费午餐,弹性不打卡,季度旅游北京数据分析 SPSS SQL15k-25k20000.0经验3-5年数据分析师本科社交B轮150-500人
1和信集团大望路五险一金、周末双休北京数据分析10k-20k15000.0经验不限数据分析师本科金融不需要融资2000人以上
\n", "
" ], "text/plain": [ " 公司名称 公司地点 公司短评 城市 岗位技能 薪资 平均薪资 \\\n", "0 玩吧 东城区 七险一金,免费午餐,弹性不打卡,季度旅游 北京 数据分析 SPSS SQL 15k-25k 20000.0 \n", "1 和信集团 大望路 五险一金、周末双休 北京 数据分析 10k-20k 15000.0 \n", "\n", " 经验要求 标题 学历要求 公司领域 融资阶段 公司规模 \n", "0 经验3-5年 数据分析师 本科 社交 B轮 150-500人 \n", "1 经验不限 数据分析师 本科 金融 不需要融资 2000人以上 " ] }, "execution_count": 305, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(2)" ] }, { "cell_type": "code", "execution_count": 306, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 306, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar('学历要求')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 公司领域与平均薪资的关系" ] }, { "cell_type": "code", "execution_count": 307, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['公司名称', '公司地点', '公司短评', '城市', '岗位技能', '薪资', '平均薪资', '经验要求', '标题',\n", " '学历要求', '公司领域', '融资阶段', '公司规模'],\n", " dtype='object')" ] }, "execution_count": 307, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 308, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 308, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar('公司领域')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 融资阶段与平均薪资的关系" ] }, { "cell_type": "code", "execution_count": 309, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['公司名称', '公司地点', '公司短评', '城市', '岗位技能', '薪资', '平均薪资', '经验要求', '标题',\n", " '学历要求', '公司领域', '融资阶段', '公司规模'],\n", " dtype='object')" ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 310, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 310, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar('融资阶段')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 公司规模与平均薪资的关系" ] }, { "cell_type": "code", "execution_count": 311, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['公司名称', '公司地点', '公司短评', '城市', '岗位技能', '薪资', '平均薪资', '经验要求', '标题',\n", " '学历要求', '公司领域', '融资阶段', '公司规模'],\n", " dtype='object')" ] }, "execution_count": 311, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 312, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 312, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar('公司规模')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 经验要求与平均薪资的关系" ] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['公司名称', '公司地点', '公司短评', '城市', '岗位技能', '薪资', '平均薪资', '经验要求', '标题',\n", " '学历要求', '公司领域', '融资阶段', '公司规模'],\n", " dtype='object')" ] }, "execution_count": 314, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 316, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 316, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar('经验要求')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 利用饼图分析数据分析职位岗位技能占比 \n", "统计技能需求里面一些关键字出现的次数， 比如Excel BI SPSS SQL Python 机器学习 数据挖掘 算法等出现的次数， \n", "画出相对应的技能所占的饼图百分比。 \n", "统计规则如下:\n", "- 1 统计 数据库 ， 凡是出现 数据库 SQL Oracle MongoDB都算是数据库的\n", "- 2 统计 Python， 凡是出现 Python 数据挖掘 机器学习 算法 都算是Python的\n", "- 3 统计 BI \n", "- 4 统计 SPSS\n", "- 5 统计 Hive" ] }, { "cell_type": "code", "execution_count": 332, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [数据分析, SPSS, SQL]\n", "1 [数据分析]\n", "2 [数据分析, MySQL, Hive]\n", "3 [数据运营, SPSS, SQL]\n", "4 [游戏, 社交, SQL, 数据分析, 商业]\n", " ... \n", "1669 [BI, SPSS, 数据分析]\n", "1670 [证券, 股票, 证券分析]\n", "1671 [工具软件, 移动互联网, 数据挖掘, 数据架构, 数据仓库, 算法]\n", "1672 [数据分析, SQL]\n", "1673 [分类信息, 大数据, 数据挖掘, 数据架构, 数据仓库, 数据分析]\n", "Name: 岗位技能, Length: 1590, dtype: object" ] }, "execution_count": 332, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = df.岗位技能.str.split()\n", "a" ] }, { "cell_type": "code", "execution_count": 333, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'数据分析 SPSS SQL 数据分析 数据分析 MySQL Hive 数据运营 SPSS SQL 游戏 社交 SQL 数据分析 商业 数据分析 数据运营 SQL 电商 新零售 Hive MySQL 数据处理 数据分析 电商 广告营销 增长黑客 SQL 商业 数据分析 数据运营 数据分析 教育 运营 数据分析 效果跟踪 MySQL SQLServer 数据分析 云计算 大数据 数据分析 SPSS SQL 数据分析 电商 新零售 运营 产品运营 用户运营 电商运营 移动互联网 需求分析 数据分析 数据分析 教育 数据分析 数据分析 数据分析 算法 数据挖掘 大数据 商业 数据分析 云计算 大数据 数据分析 数据库 数据分析 数据分析 数据分析 数据运营 SQL BI BI 数据分析 电商 产品设计 产品策划 需求分析 数据分析 移动互联网 数据分析 数据分析 移动互联网 数据分析 行业分析 数据分析 企业服务 房产服务 BI 数据库 数据分析 数据运营 SQL BI 商业 MySQL 数据分析 电商 汽车 商业 SQL SPSS 数据分析 数据分析 MySQL SQLServer 大数据 数据分析 商业 数据分析 SPSS SQL 商业 SQL 数据分析 数据分析 大数据 金融 数据分析 商业 数据分析 游戏 数据分析 商业 可视化 数据分析 金融 SQL 数据库 数据分析 数据运营 互联网金融 数据分析 SQL 教育 移动互联网 数据分析 用户增长 电商 SQL 数据分析 BI SQL 数据分析 商业 社交 移动互联网 数据分析 策略 用户研究 教育 SQL ETL 数据仓库 数据分析 SQLServer 移动互联网 增长黑客 数据分析 商业 数据运营 移动互联网 可视化 数据运营 SPSS SQL 直播 数据库 SQL 大数据 数据分析 数据分析 SQL 移动互联网 教育 数据分析 大数据 金融 Hadoop 算法 数据挖掘 数据分析 SQL SPSS 电商 新零售 数据分析 数据运营 数据分析 数据库 SQL 可视化 本地生活 数据分析 数据运营 SQL BI 可视化 数据分析 商业 SQL 数据分析 数据分析 SQL 视频 工具软件 数据分析 数据运营 SQL 商业 大数据 数据挖掘 数据分析 教育 移动互联网 数据分析 BI 可视化 SQL 数据库 SQL 数据分析 BI SQL 数据库 大数据 数据挖掘 数据分析 项目管理 数据分析 大数据 征信 数据分析 新零售 数据分析 汽车 大数据 数据挖掘 数据分析 教育 BI 数据分析 电商 新零售 数据仓库 数据分析 数据处理 数据分析 BI 增长黑客 数据分析 SQL 数据库 数据分析 SQL BI 可视化 数据分析 BI SQL 数据库 医疗健康 大数据 互联网金融 MySQL 数据分析 数据分析 数据挖掘 算法 数据分析 SQL 数据库 移动互联网 BI SQL 数据库 数据分析 移动互联网 数据分析 广告营销 移动互联网 数据分析 SQL 运营 BI 数据分析 金融 移动互联网 SPSS 数据分析 SQL 商业 大数据 数据分析 BI 数据分析 SQL 算法 搜索 数据挖掘 数据分析 旅游 数据分析 SPSS 商业 BI 数据分析 数据运营 大数据 数据分析 数据分析 数据挖掘 数据分析 商业 BI 数据分析 数据挖掘 数据分析 数据挖掘 数据仓库 大数据 数据分析 SPSS 数据分析 数据分析 SQL 金融 数据分析 移动互联网 电商 旅游 BI 产品经理 数据分析 汽车 数据分析 电商 商业 BI BI SQL SPSS 商业 数据分析 数据库 SQL 可视化 数据分析 增长黑客 视频 直播 数据分析 数据分析 数据分析 SQL 数据分析 数据运营 MySQL Scala 数据分析 金融 大数据 数据挖掘 BI 数据分析 数据分析 分析师 互联网金融 MySQL 教育 决策能力 业务流程管理 数据分析 移动互联网 金融 数据分析 数据运营 SQL 互联网金融 银行 节日礼物 技能培训 年度旅游 岗位晋升 数据分析 互联网金融 数据分析 数据分析 数据分析 数据分析 SQL 商业 数据库 产品 大数据 SQL 数据分析 商业 大数据 SQLServer Hive 数据分析 商业 可视化 数据分析 数据分析 房产服务 BI 数据分析 商业 移动互联网 数据分析 企业服务 移动互联网 数据分析 数据运营 数据库 SQL 数据分析 SQL 数据库 金融 NLP 算法 数据分析 医疗健康 大数据 数据分析 BI 数据运营 数据库 电商 大数据 数据分析 SPSS 数据分析 SPSS BI 可视化 数据分析 移动互联网 SQL 可视化 数据分析 数据分析 数据分析 增长黑客 数据分析 数据运营 商业 数据分析 MySQL 教育 算法 数据挖掘 数据分析 ETL MySQL 数据分析 数据处理 互联网金融 数据分析 数据分析 商业 数据分析 SPSS SQL 广告营销 大数据 商业 数据分析 SPSS 数据分析 数据分析 SQL 数据库 数据分析 数据分析 数据分析 互联网金融 借贷 数据分析 数据运营 电商 商业 BI 数据分析 SQL 数据分析 教育 数据分析 SQL 大数据 商业 BI 可视化 数据分析 大数据 金融 SQL 数据分析 大数据 数据分析 可视化 BI 商业 数据分析 互联网金融 借贷 数据分析 数据运营 数据运营 增长黑客 数据分析 Hive MySQL Oracle 电商 数据 大数据 数据运营 电商 SQL 互联网金融 Oracle 数据分析 Hive 游戏 数据分析 数据运营 大数据 数据分析 互联网金融 借贷 数据分析 MySQL Spark 电商 SQL 数据分析 数据运营 数据库 后端 音频处理 软件开发 滴滴 本地生活 移动互联网 BI 数据分析 数据运营 大数据 金融 数据挖掘 数据分析 数据分析 数据分析 数据分析 SQL 互联网金融 理财 BI 数据分析 数据库 大数据 金融 BI 数据分析 电商 数据分析 BI SQL 数据库 BI 数据分析 数据分析 风控 分析师 数据分析 SQL 数据分析 数据分析 大数据 数据分析 SQL 移动互联网 企业服务 数据分析 数据运营 SQL 数据库 数据分析 hadoop 大数据 数据挖掘 数据分析 数据分析 金融 数据分析 数据运营 医疗健康 大数据 数据分析 BI 移动互联网 数据分析 SQL 大数据 数据分析 数据分析 移动互联网 数据分析 Hive 数据处理 数据挖掘 电商 数据分析 数据分析 数据处理 移动互联网 数据分析 数据运营 数据挖掘 数据分析 Spark 电商 数据分析 SPSS BI SQL 产品经理 大数据 数据挖掘 数据分析 金融 数据挖掘 数据分析 Hive 数据分析 数据分析 数据运营 数据库 BI 数据分析 SQL 数据库 数据分析 大数据 旅游 数据挖掘 数据分析 数据处理 算法 SQL 增长黑客 数据分析 大数据 数据分析 数据分析 数据运营 SQL 数据库 电商 数据分析 数据分析 教育 产品 SQL SPSS SQL SPSS 数据运营 数据分析 大数据 房产服务 数据分析 MySQL 数据分析 数据分析 互联网金融 数据分析 数据分析 数据挖掘 SQL 商业 借贷 数据分析 数据处理 数据分析 MySQL SQLServer 滴滴 教育 BI SQL 商业 数据运营 数据分析 移动互联网 视频 数据分析 数据分析 数据架构 数据挖掘 数据分析 SQL 电商 新零售 SQL 数据分析 数据运营 大数据 旅游 数据仓库 数据分析 数据处理 Hive 互联网金融 电商 Hive Hadoop 数据分析 医疗健康 移动互联网 数据分析 SQLServer 借贷 数据分析 SQL 数据库 运营 APP推广 大数据 数据分析 数据分析 MySQL Hive 数据分析 SQL 教育 移动互联网 数据分析 商业 BI 数据运营 大数据 金融 数据分析 数据运营 SQL 数据分析 新零售 汽车 BI 可视化 数据分析 SQL 行业研究 分析师 游戏 大数据 数据分析 数据分析 效果跟踪 新媒体运营 数据分析 电商 数据分析 数据分析 保险 Hadoop ETL Hive 数据分析 数据分析 金融 数据分析 数据处理 数据分析 算法 电商 数据分析 SPSS 电商 可视化 商业 数据分析 BI 产品 数据分析 滴滴 数据分析 教育 数据分析 SPSS SQL 大数据 移动互联网 市场分析 数据分析 行业分析 市场 数据分析 电商 移动互联网 可视化 数据分析 数据运营 增长黑客 数据分析 数据运营 SQL 数据库 数据分析 BI 数据分析 大数据 数据分析 电商 ETL 数据分析 地图 大数据 审核 大数据 教育 滴滴 数据挖掘 数据仓库 数据分析 Hadoop BI 可视化 数据分析 数据运营 数据分析 大数据 数据分析 数据库 增长黑客 商业 数据运营 数据分析 商业 BI 数据分析 SQL 大数据 数据挖掘 数据分析 可视化 数据分析 SPSS 数据库 大数据 汽车 Java 数据挖掘 数据处理 算法 产品 滴滴 电商 SQL 数据分析 本地生活 房产服务 数据库 SQL 数据分析 大数据 商业 数据分析 游戏 数据分析 数据运营 汽车 地图 电商 商业 教育 MySQL 数据分析 数据分析 电商 SQLServer 数据分析 数据分析 游戏 数据分析 数据分析 汽车 Hive 数据分析 数据分析 数据分析 数据 互联网金融 保险 数据分析 BI SPSS SQL 游戏 BI 数据分析 商业 SQL 金融 数据分析 数据分析 数据运营 大数据 医疗健康 数据分析 SQL 广告营销 数据分析 数据挖掘 数据分析 Spark Hadoop Hive 数据分析 SQL 分析 财务 游戏 移动互联网 BI SQL 数据分析 SPSS 数据分析 社交 移动互联网 SQL 商业 数据分析 增长黑客（GrowthHacking） 汽车 新零售 数据运营 SQL 数据分析 商业 大数据 数据挖掘 数据分析 数据分析 电商 本地生活 商业 BI 可视化 互联网金融 移动互联网 房产服务 数据分析 MySQL 数据挖掘 数据分析 电商 数据分析 数据运营 可视化 视频 移动互联网 商业 数据分析 数据运营 SQL 审核 数据分析 滴滴 直播 社交 数据分析 互联网金融 借贷 SQL 可视化 数据分析 SPSS 数据挖掘 算法 数据挖掘 数据分析 数据运营 新零售 汽车 可视化 SPSS SQL 数据分析 数据分析 SQL 商业 数据运营 市场 数据分析 大数据 互联网金融 SQL 运营 数据分析 商业 BI 可视化 SQL 电商 电商 新零售 数据分析 数据分析 视频 数据分析 数据处理 数据架构 数据分析 数据挖掘 移动互联网 数据分析 大数据 工具软件 数据分析 增长黑客（GrowthHacking） 商业 增长黑客 数据分析 数据运营 数据分析 数据运营 分类信息 数据分析 可视化 数据分析 数据库 SQL 大数据 云计算 数据挖掘 数据分析 算法 数据处理 SQL 数据库 数据分析 数据分析 电商 广告营销 数据分析 电商 移动互联网 数据挖掘 数据分析 Hive MySQL 移动互联网 数据分析 保险 增长黑客 数据分析 可视化 数据运营 数据分析 数据分析 SQL 电商 广告营销 数据分析 大数据 数据分析 视频 直播 数据分析 行业分析 市场分析 市场竞争分析 商业 数据分析 物流 数据分析 数据处理 SQLServer 大数据 数据分析 商业 BI 可视化 电商 移动互联网 数据分析 SQL 物流 数据分析 电商 新零售 商业 SQL 数据分析 电商 新零售 教育 Hadoop 数据分析 MySQL 数据分析 广告营销 大数据 数据分析 BI 数据运营 数据分析 汽车 数据分析 SPSS SQL 电商 数据分析 数据分析 电商 房产服务 BI 数据分析 SQL 广告营销 SQL 审核 数据分析 数据分析 市场分析 行业分析 物流 SQLServer 电商 新零售 数据分析 效果跟踪 数据分析 免费班车 岗位晋升 五险一金 定期体检 数据分析 增长黑客 SQL 数据分析 数据分析 数据挖掘 数据处理 互联网金融 教育 运营 数据分析 效果跟踪 大数据 数据挖掘 数据分析 数据分析 其他 大数据 银行 数据挖掘 数据分析 数据分析 SQLServer 游戏 移动互联网 数据分析 数据运营 大数据 移动互联网 数据分析 数据运营 SQL SPSS 大数据 数据分析 本地生活 移动互联网 数据分析 数据分析 医疗健康 物流 数据分析 企业服务 大数据 算法 数据挖掘 数据架构 数据分析 大数据 Hadoop 数据分析 大数据 企业服务 数据分析 SPSS 数据库 移动互联网 通信/网络设备 移动互联网 大数据 大数据 数据分析 数据分析 SQL 市场 数据分析 数据挖掘 数据分析 风控 产品 大数据 移动互联网 数据分析 数据分析 智能硬件 数据分析 数据运营 BI 大数据 数据分析 大数据 银行 数据挖掘 数据分析 移动互联网 电商 数据运营 数据分析 数据分析 可视化 商业 MySQL 数据分析 游戏 移动互联网 数据分析 效果跟踪 移动互联网 数据分析 电商 大数据 算法 数据挖掘 数据分析 分析师 大数据 金融 数据分析 数据库 数据分析 教育 移动互联网 电商 运营 数据分析 机器学习 数据挖掘 建模 大数据 数据分析 数据运营 商业 数据分析 大数据 金融 数据分析 数据库 数据运营 数据分析 SPSS SQL 数据库 数据分析 数据分析 数据分析 大数据 信息安全 数据分析 数据运营 大数据 商业 数据分析 数据分析 物流 金融 移动互联网 企业服务 金融 SQL 数据分析 市场竞争分析 消费者分析 市场分析 数字营销 数据分析 Oracle MySQL Hadoop 数据架构 大数据 企业服务 数据分析 大数据 医疗健康 数据分析 数据库 SQL 数据分析 DBA SQL 游戏运营 数据分析 游戏运营 数据分析 电商 物流 MySQL Oracle 数据分析 数据处理 游戏 数据挖掘 数据分析 大数据 移动互联网 数据分析 分析师 数据分析 互联网金融 大数据 数据分析 数据分析 基金 数据分析 数据分析 数据分析 数据运营 电商 大数据 数据分析 数据运营 SPSS 商业 大数据 金融 商业 数据分析 可视化 数据运营 数据挖掘 数据分析 算法 电商 移动互联网 SPSS 大数据 数据分析 数据分析 产品设计 产品策划 交互设计 移动互联网 数据分析 SQL SPSS 数据分析 数据处理 数据分析 SQL 电商 数据运营 数据库 数据分析 SQL 数据分析 直播 视频 数据分析 SQL 数据运营 BI Hive 数据分析 数据挖掘 数据分析 SQLServer 数据分析 数据库 数据运营 移动互联网 教育 数据分析 增长黑客 数据运营 可视化 产品设计 需求分析 数据分析 数据分析 数据挖掘 数据分析 BI 电商 数据分析 电商 大数据 商业 数据分析 广告营销 移动互联网 数据运营 新零售 商业 数据分析 数据运营 数据库 Hadoop 数据处理 数据分析 MySQL 移动互联网 数据分析 SQL 数据分析 数据分析 数据库 SQL BI SQL 数据库 数据分析 电商 大数据 MySQL 数据分析 数据挖掘 DBA 大数据 数据分析 数据分析 数据库 广告营销 新零售 物流 移动互联网 数据分析 数据运营 数据库 电商 大数据 商业 数据分析 SPSS BI 教育 移动互联网 数据分析 电商 数据库 数据运营 数据分析 商业 新零售 大数据 数据分析 数据分析 数据分析 体育 移动互联网 数据分析 数据运营 数据库 电商 数据分析 数据分析 可视化 商业 数据分析 数据挖掘 数据架构 数据仓库 游戏 广告营销 数据分析 电商 新零售 数据分析 SQL 可视化 HR 人力资源 人事 大数据 移动互联网 运营 数据分析 电商 数据分析 电商 商业 BI 数据分析 游戏 市场 数据分析 广告营销 商业 数据分析 移动互联网 汽车 数据分析 运营 用户增长 游戏 产品运营 数据分析 游戏运营 SQL 数据分析 数据库 社交 移动互联网 SPSS SQL 数据分析 数据分析 SQL 电商 大数据 数据分析 数据挖掘 Hadoop MySQL 大数据 广告营销 SQL 数据分析 可视化 数据库 医疗健康 大数据 SQLServer 数据处理 数据分析 媒体 运维 数据分析 SQL 数据分析 数据分析 数据处理 Hive 数据分析 移动互联网 SQLServer 移动互联网 商业 增长黑客 数据分析 BI 数据分析 游戏 移动互联网 数据分析 数据架构 数据挖掘 MySQL 社交 移动互联网 数据分析 数据运营 增长黑客 数据分析 互联网金融 数据挖掘 数据架构 数据分析 数据处理 社交 大数据 客服 售前 售后 电商 BI 数据分析 数据分析 Hive 金融 数据分析 通信/网络设备 移动互联网 游戏 数据分析 数据运营 医疗健康 移动互联网 可视化 数据分析 SPSS SQL 移动互联网 数据分析 移动互联网 数据分析 电商 数据分析 数据分析 数据处理 云计算 大数据 数据分析 数据挖掘 数据架构 数据仓库 数据分析 大数据 算法 数据挖掘 数据分析 电商 数据分析 数据处理 MySQL 大数据 广告营销 游戏 数据分析 电商 网店推广 推广 SEO 大数据 云计算 Hive 数据分析 MySQL Oracle 数据分析 SQL 电商 社交 商业 SQL 数据分析 SPSS 电商 BI 可视化 SQL 数据库 运营 数据分析 策略运营 用户运营 大数据 算法 数据挖掘 数据分析 BI 数据分析 SQL 大数据 Hive MySQL Oracle SQLServer Hadoop 大数据 广告营销 运营 用户增长 数据分析 商业 数据分析 数据运营 SPSS 数据分析 工具软件 新零售 数据库 大数据 数据挖掘 数据分析 项目管理 敏捷 解决方案 Hive 数据分析 数据分析 数据分析 电商 可视化 数据分析 数据运营 数据库 大数据 数据挖掘 数据分析 移动互联网 游戏 MySQL 数据处理 数据分析 游戏 数据分析 数据运营 SPSS 大数据 数据分析 数据挖掘 电商 大数据 教育 大数据 BI 商业 可视化 SPSS 数据分析 数据处理 银行 数据挖掘 数据分析 大数据 大数据 数据挖掘 游戏 移动互联网 数据分析 SQL 电商 数据分析 数据运营 游戏 数据分析 游戏 数据库 数据分析 SQL 可视化 大数据 企业服务 BI 可视化 数据分析 数据运营 大数据 云计算 游戏 数据分析 数据分析 市场分析 数字营销 电商 大数据 数据分析 数据运营 SQL 数据库 大数据 移动互联网 商业 数据分析 SPSS SQL 移动互联网 信息安全 数据分析 电商 数据分析 数据运营 大数据 金融 BI 数据分析 数据库 SQL 数据分析 SPSS 教育 数据分析 SPSS SQL BI 风控 风险分析 数据分析 数据库 SQL 大数据 新零售 产品运营 策略运营 数据分析 数据运营 数据分析 SQL 数据库 电商 数据分析 游戏 数据挖掘 数据分析 算法 数据处理 数据分析 SQL 数据运营 游戏 数据分析 数据运营 旅游 工具软件 数据分析 互联网金融 理财 分析师 行业研究 风控 医疗健康 数据分析 电商 推广 SEO 网店推广 大数据 市场 数据分析 电商 大数据 算法 搜索 数据挖掘 PKI 数据分析 游戏 大数据 游戏 数据分析 电商 数据分析 数据运营 SPSS SQL 数据库 SQL SPSS 数据分析 数据分析 产品设计 产品策划 需求分析 大数据 电商 数据分析 数据运营 电商 外汇 理财顾问 汽车 大数据 数据运营 数据分析 互联网金融 Hadoop 数据仓库 数据分析 Oracle 游戏运营 产品运营 数据分析 移动互联网 大数据 电商 财务 分析 大数据 数据分析 MySQL Oracle 游戏 游戏运营 Java NLP MySQL 数据分析 工具软件 信息安全 增长黑客 数据分析 运营 移动互联网 大数据 市场分析 数据分析 产销协调 数据分析 广告营销 电商 市场竞争分析 市场分析 消费者分析 营销策略 企业服务 大数据 数据挖掘 数据分析 算法 数据处理 电商 新零售 数据分析 市场分析 市场竞争分析 大数据 移动互联网 人工智能 建模 算法 数据挖掘 大数据 移动互联网 BI 数据分析 ETL Hadoop MySQL 电商 SQL 电商 Hive 数据分析 MySQL 电商 数据分析 数据运营 SPSS 商业 大数据 云计算 电商 金融 SQL SPSS 商业 数据分析 电商 BI 数据分析 SQL 社交 大数据 数据分析 数据库 SQL 数据运营 电商 广告营销 数据分析 移动互联网 工具软件 可视化 数据库 数据运营 数据分析 电商 移动互联网 数据分析 行业分析 市场分析 商业策划 商业 数据分析 大数据 数据挖掘 数据分析 移动互联网 数据分析 广告营销 数据分析 数据库 工具软件 移动互联网 数据分析 算法 数据挖掘 数据分析 移动互联网 工具软件 数据分析 数据分析 数据运营 数据库 大数据 软件开发 大数据 企业服务 数据分析 电商 数据分析 SQL 数据库 电商 直播 数据分析 网站分析 市场 效果跟踪 商业 BI 数据分析 SQL 数据仓库 数据架构 电商 BI 大数据 BI 数据分析 数据运营 电商 数据分析 数据分析 SQL 电商 大数据 大数据 金融 BI 数据分析 数据分析 电商 BI 数据分析 电商 数据分析 数据挖掘 数据分析 保险 互联网金融 数据库 SQL 数据分析 移动互联网 数据分析 金融 Hadoop Hive 数据挖掘 数据分析 分析师 大数据 数据挖掘 数据分析 信息安全 新零售 电商 数据分析 商业 数据分析 数据分析 运营 游戏 数据分析 数据挖掘 互联网金融 MySQL 数据分析 数据分析 数据分析 Hive 数据分析 电商 大数据 大数据 数据挖掘 数据分析 大数据 BI 可视化 数据分析 大数据 教育 大数据 电商 数据分析 互联网金融 电商 Hive MySQL 数据分析 数据架构 电商 广告营销 营销策略 消费者分析 市场分析 数据分析 数据分析 移动互联网 数据分析 MySQL 数据处理 数据分析 SPSS SQL 数据库 数据分析 电商 Hive MySQL Spark 数据分析 数据分析 数据处理 数据挖掘 数据分析 电商 数据挖掘 数据分析 互联网金融 SQL 数据分析 物流 数据分析 数据运营 BI 可视化 电商 BI 数据分析 电商 教育 数据分析 电商 广告营销 市场分析 消费者分析 营销策略 数据分析 大数据 电商 数据分析 MySQL BI 数据分析 数据分析 BI SQL 数据挖掘 数据分析 搜索 数据分析 策略运营 数据分析 数据分析 数据挖掘 Java Hive SQLServer 电商 BI 数据分析 电商 数据分析 数据挖掘 医疗健康 移动互联网 数据挖掘 数据分析 数据仓库 MySQL 数据挖掘 数据分析 大数据 数据分析 数据分析 医疗健康 企业服务 电商 大数据 数据分析 数据处理 算法 电商 数据分析 数据分析 数据分析 行业分析 企业服务 大数据 SQL 数据库 数据分析 电商 数据分析 数据处理 MySQL SQLServer 数据挖掘 数据分析 移动互联网 电商 商业 数据分析 数据运营 数据库 数据分析 电商 财务 资产/项目评估 分析 风控 滴滴 数据分析 电商 用户运营 数据分析 大数据 医疗健康 SQL SPSS 数据分析 商业 体育 数据分析 大数据 数据分析 数据 数据分析 效果跟踪 跨境电商 大数据 电商 数据挖掘 数据仓库 数据分析 数据架构 工具软件 大数据 大数据 金融 数据分析 电商 数据分析 数据库 企业服务 可视化 数据分析 数据库 数据分析 Hive 数据分析 MySQL 移动互联网 数据分析 企业服务 大数据 电商 金融 大数据 企业服务 MySQL 数据分析 电商 数据分析 移动互联网 电商 数据分析 电商 医疗健康 商业 可视化 数据分析 数据运营 电商 新零售 电商 数据分析 电商 电商 BI SQL 数据分析 商业 MySQL 数据分析 支付 MySQL Java 电商 物流 分析 上市 资产管理 尽职调查 游戏 会计 分析 数据分析 数据分析 数据仓库 数据库开发 其他 通信/网络设备 电商 新零售 算法 数据挖掘 数据分析 大数据 数据分析 信息安全 电商 移动互联网 SQLServer Hive 数据分析 算法 电商 数据分析 行业分析 市场分析 数据分析 数据仓库 数据架构 数据挖掘 电商 SPSS SQL 数据挖掘 数据分析 大数据 征信 算法 数据分析 数据挖掘 大数据 信息安全 SQL 数据分析 电商 大数据 BI 数据分析 电商 数据分析 SQLServer MySQL 数据分析 电商 数据挖掘 数据分析 数据运营 数据库 数据分析 SQL 数据库 通信/网络设备 数据挖掘 算法 数据库开发 金融 风控 分析师 跨境电商 数据分析 移动互联网 大数据 金融 数据分析 数据挖掘 MySQL 数据库 BI 可视化 MySQL 电商 移动互联网 广告营销 策划 创意 Oracle 数据仓库 数据挖掘 数据分析 新零售 BI 数据 数据分析 大数据 数据挖掘 移动互联网 新零售 物流 数据分析 数据分析 SQL 物流 数据分析 数据分析 数据库 大数据 互联网金融 数据分析 医疗健康 数据分析 MySQL 数据分析 Hive SQL 数据运营 数据分析 大数据 数据分析 数据分析 可视化 数据库 SQL 电商 工具软件 SQL 数据分析 数据处理 SQLServer 数据分析 数据分析 数据挖掘 数据分析 SQL 信息安全 数据分析 数据库 SQL 互联网金融 移动互联网 SQL 金融 SQL 数据库 可视化 数据库 大数据 移动互联网 SQL 数据分析 数据分析 互联网金融 SQL 数据分析 游戏 SQL SPSS 数据分析 移动互联网 教育 BI 数据分析 数据运营 SQL 数据分析 数据库 年底双薪 绩效奖金 岗位晋升 定期体检 数据分析 SQL 大数据 数据分析 电商 企业服务 可视化 SQL 数据运营 新零售 风控 数据分析 数据运营 BI SQL 数据库 大数据 金融 商业 数据分析 电商 数据挖掘 数据分析 数据处理 MySQL 数据分析 数据运营 移动互联网 数据分析 商业 数据运营 社交 移动互联网 可视化 数据分析 SQL BI Java 数据分析 数据运营 数据分析 信息安全 机器学习 建模 算法 BI 商业 数据分析 互联网金融 数据分析 MySQL 汽车 移动互联网 数据分析 企业服务 大数据 MySQL 数据挖掘 数据分析 BI 数据分析 数据运营 商业 可视化 BI 数据分析 互联网金融 风控 分析师 信用产品 数据分析 编辑 大数据 商业 BI 可视化 数据分析 电商 新零售 BI SQL 数据库 数据分析 商业 BI 数据分析 社交 移动互联网 数据分析 大数据 数据分析 SQL 互联网金融 数据分析 数据运营 数据库 SQL 数据分析 大数据 互联网金融 数据分析 金融 商业 BI 可视化 数据分析 移动互联网 本地生活 BI 数据分析 SQL 数据库 数据分析 内容 大数据 可视化 SPSS 数据分析 电商 金融 数据挖掘 数据分析 云计算 大数据 数据分析 BI 数据分析 SQL 游戏 SQL 电商 数据库 数据分析 电商 数据分析 大数据 BI 数据分析 信息安全 大数据 数据分析 数据仓库 数据架构 数据挖掘 大数据 征信 数据分析 SPSS 金融 大数据 BI 可视化 数据库 大数据 医疗健康 MySQL ETL 数据分析 数据仓库 数据分析 旅游 大数据 ETL Hive 数据分析 数据处理 大数据 其他 ETL DB2 数据分析 电商 移动互联网 数据分析 BI 数据分析 SQL 年底双薪 节日礼物 技能培训 绩效奖金 电商 大数据 数据分析 数据运营 数据库 BI 可视化 数据分析 SQL 数据分析 游戏 广告营销 企业服务 商业 数据分析 DBA 教育 移动互联网 数据分析 数据运营 SQL 大数据 金融 数据分析 金融 数据分析 电商 移动互联网 数据分析 大数据 数据分析 数据分析 可视化 数据运营 数据分析 数据处理 数据库开发 数据分析 电商 医疗健康 数据分析 数据运营 数据分析 电商 SPSS SQL 数据库 其他 金融 保险 数据分析 SPSS SQL 商业 数据分析 SPSS 数据分析 电商 社交 数据分析 数据处理 SQL 数据分析 大数据 工具软件 旅游 移动互联网 数据分析 电商 数据分析 SQL Spark 互联网金融 互联网金融 SQLServer 数据分析 数据挖掘 MySQL SPSS SQL 数据库 数据分析 大数据 BI 数据分析 移动互联网 数据分析 数据分析 数据分析 数据仓库 数据库开发 大数据 移动互联网 本地生活 数据运营 SQL 数据库 BI 金融 大数据 MySQL SQLServer 数据分析 数据处理 移动互联网 金融 数据分析 SQL 大数据 数据分析 商业 数据分析 增长黑客 BI 数据分析 数据运营 数据分析 电商 本地生活 数据分析 BI 商业 电商 社交 可视化 SQL 数据分析 数据库 电商 数据分析 数据分析 大数据 移动互联网 数据仓库 数据分析 电商 BI SPSS 新零售 数据分析 大数据 数据挖掘 数据分析 数据处理 商业 数据分析 SQL 建模 深度学习 机器学习 数据挖掘 本地生活 BI 数据分析 数据运营 SQL 大数据 BI 数据分析 SPSS 电商 BI 数据分析 数据运营 SQL Hive MySQL 数据分析 数据分析 可视化 增长黑客 数据分析 BI 社交 数据分析 BI 可视化 数据分析 数据运营 电商 移动互联网 数据分析 数据挖掘 电商 数据架构 数据分析 数据处理 大数据 BI 数据分析 大数据 数据分析 MySQL Hadoop 数据仓库 数据分析 数据分析 数据分析 数据分析 SPSS 数据分析 SQL 电商 数据分析 SQLServer Hive SQL 数据分析 数据库 数据分析 数据库 风控 广告营销 数据分析 数字营销 电商 新零售 数据分析 SQL DBA 数据库 移动互联网 大数据 移动互联网 大数据 MySQL 风控 大数据 金融 数据分析 数据分析 数据处理 SQL BI 可视化 数据分析 大数据 移动互联网 数据分析 数据分析 游戏 大数据 数据挖掘 数据分析 信贷风险管理 风控 风险分析 数据分析 移动互联网 SQL BI 可视化 数据分析 大数据 数据挖掘 Hadoop 数据分析 证券/期货 互联网金融 数据分析 金融 大数据 数据仓库 数据分析 数据库开发 移动互联网 MySQL 数据分析 金融 数据挖掘 数据分析 数据挖掘 数据分析 数据处理 旅游 移动互联网 数据分析 消费者分析 市场分析 海外市场 旅游 数据分析 SPSS 数据分析 电商 新零售 hadoop 数据分析 风控 信贷风险管理 大数据 数据分析 大数据 银行 MySQL 数据分析 SQLServer 数据分析 数据分析 移动互联网 MySQL 数据分析 电商 大数据 移动互联网 商业 数据分析 增长黑客 可视化 SQL 数据分析 电商 SQL 商业 数据分析 可视化 数据分析 数据分析 银行 数据挖掘 数据分析 数据分析 数据运营 数据库 SQL 大数据 数据分析 大数据 商业 数据分析 SPSS BI 数据分析 数据库 DBA 电商 数据分析 数据运营 大数据 数据分析 数据分析 移动互联网 数据分析 BI 商业 SQL 数据分析 数据分析 可视化 SQL 数据分析 SQLServer 数据处理 大数据 金融 数据分析 数据分析 Hive MySQL 数据分析 数据库 数据分析 SQL 数据分析 电商 新零售 数据分析 数据运营 SQL 电商 数据分析 其他 数据分析 新零售 电商 数据分析 MySQL 数据仓库 数据分析 社交 大数据 媒体 数据挖掘 数据分析 电商 SQL 数据分析 可视化 HR 薪酬福利 人力资源 数据分析 数据分析 移动互联网 数据分析 数据分析 SPSS 大数据 旅游 数据分析 SQL 汽车 金融 数据分析 数据分析 SQL 数据分析 数据分析 市场分析 市场竞争分析 行业分析 互联网金融 数据分析 数据分析 电商 电商 BI 可视化 数据分析 SQL 本地生活 汽车 数据分析 SQL 商业 移动互联网 数据分析 数据运营 游戏 大数据 数据分析 BI 数据分析 数据分析 数据分析 游戏 数据分析 商业 电商 移动互联网 数据分析 BI 增长黑客（GrowthHacking） 大数据 数据运营 大数据 数据分析 SPSS 数据分析 广告创意 广告协调 数据分析 SPSS 大数据 保险 金融 移动互联网 大数据 数据分析 数据运营 电商 商业 数据运营 数据分析 SQL 物流 大数据 可视化 增长黑客 数据运营 数据库 医疗健康 移动互联网 新零售 本地生活 BI 数据分析 数据分析 增长黑客 BI 可视化 大数据 金融 数据分析 数据分析 游戏 数据分析 SPSS 大数据 数据挖掘 数据分析 风控 风险管理 风险评估 游戏 MySQL 数据处理 数据分析 电商 直播 数据分析 数据分析 银行 SQL 数据库 数据分析 数据分析 数据挖掘 游戏 BI 数据分析 电商 分析 数据分析 数据运营 Oracle SQLServer 数据分析 数据分析 电商 移动互联网 数据运营 SQLServer 移动互联网 风控 新零售 电商 可视化 数据分析 数据分析 商业 教育 大数据 移动互联网 数据分析 数据库 SQL 数据分析 市场 广告营销 新零售 SQL 数据分析 可视化 商业 风控 策略设计 数据分析 电商 大数据 数据分析 运营 数据分析 金融 算法 数据挖掘 数据分析 大数据 数据分析 数据挖掘 教育 数据分析 SPSS 数据分析 商业 数据分析 数据运营 SQL 数据挖掘 数据分析 SQL 教育 大数据 数据挖掘 数据分析 大数据 数据挖掘 数据分析 工具软件 数据运营 SQL BI 可视化 股票期权 年底双薪 带薪年假 弹性工作 电商 运营 银行 数据分析 商业 BI 可视化 数据分析 视频 产品策划 智能硬件 数据分析 BI 数据分析 大数据 MySQL Oracle 数据分析 新零售 大数据 数据分析 大数据 金融 大数据 金融 数据挖掘 数据分析 大数据 房产服务 企业服务 BI 可视化 数据库 大数据 数据分析 数据库 BI 大数据 移动互联网 大数据 广告营销 汽车 营销 数据分析 行业分析 市场分析 SQL 数据库 数据运营 数据分析 SQLServer 数据分析 数据挖掘 大数据 数据挖掘 数据分析 大数据 新零售 数据运营 数据分析 增长黑客 大数据 工具软件 移动互联网 SQLServer 数据处理 大数据 数据挖掘 数据分析 数据分析 大数据 算法 数据挖掘 数据分析 电商 SQLServer 数据分析 医疗健康 数据分析 BI 教育 数据分析 游戏 数据分析 SQL 数据库 数据分析 SQL Hive Flink Spark Hadoop 数据分析 数据分析 数据库开发 数据分析 市场分析 营销策略 运营 SEM SEO 数据分析 地图 分类信息 运营 大数据 企业服务 SQLServer 数据处理 数据分析 新零售 数据分析 电商 新零售 SQL 数据库 数据分析 可视化 大数据 互联网金融 数据分析 SQL 人力资源 电商 数据分析 数据运营 可视化 BI 教育 数据分析 数据运营 数据分析 游戏 SQL 数据库 广告营销 数据分析 数据运营 SQL 数据分析 大数据 其他 SQL 数据库 电商 移动互联网 数据分析 算法 数据架构 数据处理 移动互联网 教育 BI SQL 游戏 数据分析 数据运营 银行 数据分析 BI 数据库 移动互联网 数据分析 BI 数据运营 大数据 数据分析 大数据 金融 云计算 大数据 SPSS 数据运营 数据分析 一手内推信息 猎头专业服务 名企保面试 精准高效对接 教育 移动互联网 SQL SPSS 新零售 大数据 商业 大数据 金融 电商 运营 金融 银行 ETL 数据处理 大数据 互联网金融 互联网金融 数据分析 数据挖掘 互联网金融 风控 分析师 电商 数据分析 数据分析 数据运营 保险 大数据 Java 数据挖掘 数据处理 SQL 数据库 数据分析 移动互联网 Hive SQLServer 数据架构 数据分析 SQL 数据挖掘 数据分析 数据分析 通信/网络设备 电商 零食水果供应 带薪年假 绩效奖金 扁平管理 数据分析 SQL 数据库 电商 MySQL 数据分析 数据挖掘 数据处理 数据分析 金融 数据分析 SQL BI SQL 数据分析 数据运营 金融 数据分析 SQL 商业 数据仓库 数据分析 数据处理 借贷 数据分析 数据运营 SPSS SQL 电商 大数据 大数据 企业服务 BI 数据分析 SPSS SQL BI 数据分析 大数据 数据挖掘 数据分析 移动互联网 金融 风控 通信/网络设备 移动互联网 数据分析 数据库 商业 SQL 大数据 工具软件 BI SQL 数据库 数据分析 数据分析 数据库开发 Hadoop 数据挖掘 大数据 Hive 数据分析 数据挖掘 金融 大数据 风控 策略设计 分析师 SPSS 数据库 SQL 电商 数据分析 SQL ETL Hadoop 数据库开发 MySQL 数据分析 大数据 大数据 商业 BI 可视化 数据分析 数据分析 可视化 商业 数据分析 金融 数据分析 电商 广告营销 数据挖掘 数据分析 通信/网络设备 大数据 数据分析 数据 电商 金融 数据挖掘 大数据 游戏 大数据 BI 数据运营 SQL 可视化 数据分析 数据挖掘 数据分析 数据分析 电商 房产服务 BI 可视化 数据分析 医疗健康 体育 BI 数据分析 数据运营 SQL 大数据 数据处理 数据分析 数据架构 MySQL 数据分析 分析师 电商 数据分析 大数据 金融 数据分析 大数据 数据分析 数据分析 营销管理 BI 数据分析 可视化 大数据 数据分析 SPSS 视频 商业 数据分析 SQL 移动互联网 大数据 SQL 数据分析 数据运营 数据分析 SPSS SQL 可视化 金融 大数据 互联网金融 风控 策略设计 分析师 企业服务 广告营销 可视化 数据分析 数据运营 MySQL 大数据 BI 数据分析 投资/融资 数据分析 数据分析 大数据 数据分析 数据分析 大数据 数据挖掘 数据分析 运营 广告营销 游戏 数据分析 人工智能 机器学习 算法 数据挖掘 移动互联网 云计算 数据分析 电商 金融 算法 数据挖掘 数据分析 数据分析 新零售 本地生活 商业 数据分析 数据库 SQL 数据分析 数据分析 BI 商业 数据分析 移动互联网 游戏 SQL 信息安全 支付 数据挖掘 数据分析 教育 数据分析 数据运营 数据分析 节日礼物 技能培训 绩效奖金 岗位晋升 SQLServer 数据分析 数据处理 电商 大数据 数据处理 数据分析 电商 MySQL 数据仓库 数据分析 数据库开发 数据分析 大数据 云计算 数据分析 数据库 BI SQL 大数据 基金 BI 数据分析 DBA SQL 大数据 数据分析 产品经理 大数据 其他 数据分析 SQLServer Spark Hadoop 算法 视频 直播 新零售 数据分析 核查 互联网金融 借贷 SQL 数据挖掘 数据仓库 数据分析 Oracle 电商 移动互联网 数据分析 电商 数据分析 数据挖掘 搜索 数据分析 电商 数据分析 数据运营 保险 互联网金融 数据分析 云计算 数据运营 数据分析 数据分析 移动互联网 电商 数据分析 数据运营 数据分析 数据运营 SQL 用户研究 商业产品 大数据 银行 风控 大数据 云计算 数据分析 大数据 电商 数据分析 SQL 数据分析 电商运营 SPSS SQL 可视化 数据分析 大数据 金融 数据挖掘 数据分析 教育 移动互联网 商业 增长黑客 数据分析 数据运营 数据分析 Hadoop Spark 数据挖掘 产品 移动互联网 证券/期货 数据分析 BI 移动互联网 数据分析 电商 旅游 大数据 MySQL 游戏 大数据 新零售 电商 商业 BI 可视化 SQL 数据分析 MySQL Spark Hive 数据处理 Hadoop NLP Redis MongoDB 电商 数据分析 Hadoop Spark Scala 数据分析 电商运营 电商 数据分析 大数据 数据分析 数据运营 增长黑客 大数据 数据挖掘 数据分析 金融 风控 分析师 策略设计 大数据 数据分析 SQL 数据分析 SPSS 五险一金 两餐补助 超低息员工贷 年底奖金 其他 数据分析 移动互联网 数据分析 Spark Hadoop 大数据 移动互联网 商业 数据分析 数据挖掘 数据分析 数据仓库 MySQL 大数据 数据分析 数据处理 数据仓库 数据分析 数据压缩 MongoDB 大数据 数据挖掘 数据分析 数据分析 电商 数据分析 数据仓库 数据分析 数据分析 Hive SQL 数据分析 BI 数据分析 数据运营 SQL MySQL Hadoop 数据分析 电商 BI 数据分析 大数据 数据分析 数据处理 数据架构 MySQL 可视化 数据分析 数据处理 SQLServer 大数据 Python 数据库 Hadoop Spark 数据分析 SPSS SQL 数据库 移动互联网 数据分析 数据分析 大数据 数据分析 BI 金融 互联网金融 运营 产品运营 数据分析 大数据 金融 可视化 数据分析 大数据 互联网金融 风险分析 信贷风险管理 数据分析 互联网金融 风控 风控 策略设计 企业服务 MySQL 数据分析 数据处理 ETL 后端 数据分析 大数据 Java 数据挖掘 算法 机器学习 其他 产品设计 产品策划 其他 通信/网络设备 数据分析 企业服务 移动互联网 大数据 移动互联网 数据挖掘 数据分析 BI 数据分析 数据运营 SQL 电商 视频 移动互联网 大数据 Java 数据分析 滴滴 数据分析 广告营销 数据分析 MySQL 产品设计 需求分析 数据分析 数据挖掘 MySQL 数据分析 数据挖掘 算法 数据挖掘 数据分析 数据分析 Python MySQL 数据库 数据分析 Hadoop 移动互联网 数据挖掘 数据分析 算法 产品策划 数据分析 用户增长 产品策划 数据分析 数据仓库 数据挖掘 数据分析 数据仓库 数据挖掘 Hive MySQL 算法 数据仓库 大数据 数据库 数据分析 SQL BI 电商 大数据 数据分析 电商 数据分析 SQLServer 大数据 数据分析 其他 大数据 SPSS 数据分析 SQL 大数据 数据分析 SQL 数据库 可视化 新零售 BI 可视化 数据分析 移动互联网 大数据 数据分析 数据运营 商业 数据分析 SQLServer 数据分析 数据挖掘 ETL 大数据 互联网金融 运营 数据分析 产品运营 效果跟踪 电商 信息安全 数据分析 数据运营 数据分析 风控 视频 直播 数据分析 数据运营 可视化 电商 数据分析 数据运营 大数据 数据分析 电商 算法 数据挖掘 数据分析 数据处理 可视化 数据库 数据分析 数据分析 数据运营 数据分析 大数据 企业服务 SQL SPSS SQL 数据分析 大数据 物流 移动互联网 数据分析 数据处理 数据挖掘 数据分析 数据运营 分析 新零售 电商 数据分析 数据挖掘 数据仓库 电商 数据分析 移动互联网 数据分析 移动互联网 教育 数据分析 SQL 数据库 信息安全 大数据 数据分析 SQL SPSS 数据库 电商 可视化 BI 数据分析 数据运营 数据分析 大数据 BI 可视化 数据分析 本地生活 数据分析 数据处理 数据分析 移动互联网 SQLServer 数据仓库 数据分析 数据挖掘 SQLServer MySQL 数据仓库 数据分析 数据挖掘 数据分析 数据分析 大数据 数据挖掘 数据分析 电商 数据分析 数据分析 数据运营 策略运营 数据分析 大数据 项目管理 大数据 产品设计 需求分析 数据分析 项目管理 视频 SPSS 数据分析 SQL 大数据 数据挖掘 数据分析 数据分析 数据运营 数据运营 数据分析 信息安全 数据分析 证券/期货 MySQL Oracle Java SQLServer 大数据 广告营销 数据分析 数据运营 数据分析 大数据 移动互联网 可视化 数据分析 商业 移动互联网 数据分析 行业分析 市场竞争分析 广告营销 技术支持 直播 数据分析 游戏 数据分析 数据库 数据分析 数据挖掘 MySQL SQL BI 数据库 数据分析 MySQL 数据分析 Java Scala 数据分析 大数据 征信 互联网金融 数据分析 数据库 数据分析 数据库 本地生活 大数据 数据分析 数据挖掘 大数据 Java NLP 运营 数据分析 物流 医疗健康 数据挖掘 数据分析 数据仓库 算法 大数据 扁平管理 弹性工作 大厨定制三餐 就近租房补贴 交通补助 弹性工作 帅哥多 领导好 大数据 移动互联网 数据挖掘 数据分析 大数据 数据分析 SPSS 数据分析 数据分析 SPSS SQL 数据分析 信息安全 大数据 Hadoop Spark 数据分析 数据挖掘 大数据 移动互联网 数据分析 内容 数据分析 数据运营 教育 数据挖掘 数据分析 数据分析 通信/网络设备 移动互联网 SPSS 数据分析 BI SPSS 数据分析 证券 股票 证券分析 工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法 数据分析 SQL 分类信息 大数据 数据挖掘 数据架构 数据仓库 数据分析'" ] }, "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L = []\n", "for i in a:\n", " L += i\n", "s = ' '.join(L)\n", "s" ] }, { "cell_type": "code", "execution_count": 334, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['数据库', 'SQL', 'Oracle', 'MongoDB', 'Python', '数据挖掘', '机器学习', '算法', 'BI', 'SPSS', 'Hive']\n" ] } ], "source": [ "L = ['数据库', 'SQL', 'Oracle', 'MongoDB', 'Python', '数据挖掘', '机器学习', '算法', 'BI', 'SPSS', 'Hive' ]\n", "print(L)" ] }, { "cell_type": "code", "execution_count": 343, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
01
0数据库132
1SQL387
2Oracle13
3MongoDB2
4Python2
5数据挖掘158
6机器学习5
7算法40
8BI153
9SPSS84
10Hive39
\n", "
" ], "text/plain": [ " 0 1\n", "0 数据库 132\n", "1 SQL 387\n", "2 Oracle 13\n", "3 MongoDB 2\n", "4 Python 2\n", "5 数据挖掘 158\n", "6 机器学习 5\n", "7 算法 40\n", "8 BI 153\n", "9 SPSS 84\n", "10 Hive 39" ] }, "execution_count": 343, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L2 = []\n", "for i in L:\n", " L2.append((i, s.count(i)))\n", "a = pd.DataFrame(L2)\n", "a" ] }, { "cell_type": "code", "execution_count": 346, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "534" ] }, "execution_count": 346, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 数据库 \n", "a[0:4].loc[:, 1].sum()" ] }, { "cell_type": "code", "execution_count": 350, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "205" ] }, "execution_count": 350, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# python \n", "a[4:8].loc[:, 1].sum()" ] }, { "cell_type": "code", "execution_count": 352, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
01
8BI153
\n", "
" ], "text/plain": [ " 0 1\n", "8 BI 153" ] }, "execution_count": 352, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# BI \n", "a[8:9]" ] }, { "cell_type": "code", "execution_count": 354, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
01
9SPSS84
\n", "
" ], "text/plain": [ " 0 1\n", "9 SPSS 84" ] }, "execution_count": 354, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# spss\n", "a[9:10]" ] }, { "cell_type": "code", "execution_count": 356, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
01
10Hive39
\n", "
" ], "text/plain": [ " 0 1\n", "10 Hive 39" ] }, "execution_count": 356, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Hive\n", "a[10:]" ] }, { "cell_type": "code", "execution_count": 357, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "数据库 534\n", "Python 205\n", "BI 153\n", "SPSS 84\n", "Hive 39\n", "dtype: int64" ] }, "execution_count": 357, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = pd.Series([534, 205, 153, 84, 39], ['数据库', 'Python', 'BI', 'SPSS', 'Hive'])\n", "a" ] }, { "cell_type": "code", "execution_count": 362, "metadata": {}, "outputs": [], "source": [ "# 导入饼图类\n", "from pyecharts.charts import Pie\n", "def pie(a):\n", " # 实例化饼图对象\n", " pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))\n", " pie.add(series_name = \"数据分析各技能占比\",\n", " data_pair = list(zip(a.index, a.values.tolist())))\n", " # 全局配置项\n", "\n", " pie.set_global_opts(title_opts= opts.TitleOpts(title = \"数据分析各技能占比\"),\n", " # 使得图例垂直\n", " legend_opts=opts.LegendOpts(orient=\"vertical\", pos_top=\"15%\", pos_left=\"2%\"))\n", "\n", " # 系列配置项 \n", " # 系列配置项下的标签配置项\n", " pie.set_series_opts(label_opts= opts.LabelOpts(is_show = True, \n", " # formatter=\"{a}{b}{c}{d}\"对应显示\n", " #{a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）\n", " formatter=\"{b}:{c} \\n{d}%\"\n", " ), \n", " position='right') \n", " return pie.render_notebook()" ] }, { "cell_type": "code", "execution_count": 363, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 363, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pie(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据分析技能词云图 " ] }, { "cell_type": "code", "execution_count": 384, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [数据分析, SPSS, SQL]\n", "1 [数据分析]\n", "2 [数据分析, MySQL, Hive]\n", "3 [数据运营, SPSS, SQL]\n", "4 [游戏, 社交, SQL, 数据分析, 商业]\n", " ... \n", "1669 [BI, SPSS, 数据分析]\n", "1670 [证券, 股票, 证券分析]\n", "1671 [工具软件, 移动互联网, 数据挖掘, 数据架构, 数据仓库, 算法]\n", "1672 [数据分析, SQL]\n", "1673 [分类信息, 大数据, 数据挖掘, 数据架构, 数据仓库, 数据分析]\n", "Name: 岗位技能, Length: 1590, dtype: object" ] }, "execution_count": 384, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = df.岗位技能.str.split()\n", "a" ] }, { "cell_type": "code", "execution_count": 411, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['数据分析',\n", " 'SPSS',\n", " 'SQL',\n", " '数据分析',\n", " '数据分析',\n", " 'MySQL',\n", " 'Hive',\n", " '数据运营',\n", " 'SPSS',\n", " 'SQL',\n", " '游戏',\n", " '社交',\n", " 'SQL',\n", " '数据分析',\n", " '商业',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '电商',\n", " '新零售',\n", " 'Hive',\n", " 'MySQL',\n", " '数据处理',\n", " '数据分析',\n", " '电商',\n", " '广告营销',\n", " '增长黑客',\n", " 'SQL',\n", " '商业',\n", " '数据分析',\n", " '数据运营',\n", " '数据分析',\n", " '教育',\n", " '运营',\n", " '数据分析',\n", " '效果跟踪',\n", " 'MySQL',\n", " 'SQLServer',\n", " '数据分析',\n", " '云计算',\n", " '大数据',\n", " '数据分析',\n", " 'SPSS',\n", " 'SQL',\n", " '数据分析',\n", " '电商',\n", " '新零售',\n", " '运营',\n", " '产品运营',\n", " '用户运营',\n", " '电商运营',\n", " '移动互联网',\n", " '需求分析',\n", " '数据分析',\n", " '数据分析',\n", " '教育',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '算法',\n", " '数据挖掘',\n", " '大数据',\n", " '商业',\n", " '数据分析',\n", " '云计算',\n", " '大数据',\n", " '数据分析',\n", " '数据库',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " 'BI',\n", " 'BI',\n", " '数据分析',\n", " '电商',\n", " '产品设计',\n", " '产品策划',\n", " '需求分析',\n", " '数据分析',\n", " '移动互联网',\n", " '数据分析',\n", " '数据分析',\n", " '移动互联网',\n", " '数据分析',\n", " '行业分析',\n", " '数据分析',\n", " '企业服务',\n", " '房产服务',\n", " 'BI',\n", " '数据库',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " 'BI',\n", " '商业',\n", " 'MySQL',\n", " '数据分析',\n", " '电商',\n", " '汽车',\n", " '商业',\n", " 'SQL',\n", " 'SPSS',\n", " '数据分析',\n", " '数据分析',\n", " 'MySQL',\n", " 'SQLServer',\n", " '大数据',\n", " '数据分析',\n", " '商业',\n", " '数据分析',\n", " 'SPSS',\n", " 'SQL',\n", " '商业',\n", " 'SQL',\n", " '数据分析',\n", " '数据分析',\n", " '大数据',\n", " '金融',\n", " '数据分析',\n", " '商业',\n", " '数据分析',\n", " '游戏',\n", " '数据分析',\n", " '商业',\n", " '可视化',\n", " '数据分析',\n", " '金融',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " '数据运营',\n", " '互联网金融',\n", " '数据分析',\n", " 'SQL',\n", " '教育',\n", " '移动互联网',\n", " '数据分析',\n", " '用户增长',\n", " '电商',\n", " 'SQL',\n", " '数据分析',\n", " 'BI',\n", " 'SQL',\n", " '数据分析',\n", " '商业',\n", " '社交',\n", " '移动互联网',\n", " '数据分析',\n", " '策略',\n", " '用户研究',\n", " '教育',\n", " 'SQL',\n", " 'ETL',\n", " '数据仓库',\n", " '数据分析',\n", " 'SQLServer',\n", " '移动互联网',\n", " '增长黑客',\n", " '数据分析',\n", " '商业',\n", " '数据运营',\n", " '移动互联网',\n", " '可视化',\n", " '数据运营',\n", " 'SPSS',\n", " 'SQL',\n", " '直播',\n", " '数据库',\n", " 'SQL',\n", " '大数据',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '移动互联网',\n", " '教育',\n", " '数据分析',\n", " '大数据',\n", " '金融',\n", " 'Hadoop',\n", " '算法',\n", " '数据挖掘',\n", " '数据分析',\n", " 'SQL',\n", " 'SPSS',\n", " '电商',\n", " '新零售',\n", " '数据分析',\n", " '数据运营',\n", " '数据分析',\n", " '数据库',\n", " 'SQL',\n", " '可视化',\n", " '本地生活',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " 'BI',\n", " '可视化',\n", " '数据分析',\n", " '商业',\n", " 'SQL',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '视频',\n", " '工具软件',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '商业',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '教育',\n", " '移动互联网',\n", " '数据分析',\n", " 'BI',\n", " '可视化',\n", " 'SQL',\n", " '数据库',\n", " 'SQL',\n", " '数据分析',\n", " 'BI',\n", " 'SQL',\n", " '数据库',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '项目管理',\n", " '数据分析',\n", " '大数据',\n", " '征信',\n", " '数据分析',\n", " '新零售',\n", " '数据分析',\n", " '汽车',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '教育',\n", " 'BI',\n", " '数据分析',\n", " '电商',\n", " '新零售',\n", " '数据仓库',\n", " '数据分析',\n", " '数据处理',\n", " '数据分析',\n", " 'BI',\n", " '增长黑客',\n", " '数据分析',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " 'SQL',\n", " 'BI',\n", " '可视化',\n", " '数据分析',\n", " 'BI',\n", " 'SQL',\n", " '数据库',\n", " '医疗健康',\n", " '大数据',\n", " '互联网金融',\n", " 'MySQL',\n", " '数据分析',\n", " '数据分析',\n", " '数据挖掘',\n", " '算法',\n", " '数据分析',\n", " 'SQL',\n", " '数据库',\n", " '移动互联网',\n", " 'BI',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " '移动互联网',\n", " '数据分析',\n", " '广告营销',\n", " '移动互联网',\n", " '数据分析',\n", " 'SQL',\n", " '运营',\n", " 'BI',\n", " '数据分析',\n", " '金融',\n", " '移动互联网',\n", " 'SPSS',\n", " '数据分析',\n", " 'SQL',\n", " '商业',\n", " '大数据',\n", " '数据分析',\n", " 'BI',\n", " '数据分析',\n", " 'SQL',\n", " '算法',\n", " '搜索',\n", " '数据挖掘',\n", " '数据分析',\n", " '旅游',\n", " '数据分析',\n", " 'SPSS',\n", " '商业',\n", " 'BI',\n", " '数据分析',\n", " '数据运营',\n", " '大数据',\n", " '数据分析',\n", " '数据分析',\n", " '数据挖掘',\n", " '数据分析',\n", " '商业',\n", " 'BI',\n", " '数据分析',\n", " '数据挖掘',\n", " '数据分析',\n", " '数据挖掘',\n", " '数据仓库',\n", " '大数据',\n", " '数据分析',\n", " 'SPSS',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '金融',\n", " '数据分析',\n", " '移动互联网',\n", " '电商',\n", " '旅游',\n", " 'BI',\n", " '产品经理',\n", " '数据分析',\n", " '汽车',\n", " '数据分析',\n", " '电商',\n", " '商业',\n", " 'BI',\n", " 'BI',\n", " 'SQL',\n", " 'SPSS',\n", " '商业',\n", " '数据分析',\n", " '数据库',\n", " 'SQL',\n", " '可视化',\n", " '数据分析',\n", " '增长黑客',\n", " '视频',\n", " '直播',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '数据分析',\n", " '数据运营',\n", " 'MySQL',\n", " 'Scala',\n", " '数据分析',\n", " '金融',\n", " '大数据',\n", " '数据挖掘',\n", " 'BI',\n", " '数据分析',\n", " '数据分析',\n", " '分析师',\n", " '互联网金融',\n", " 'MySQL',\n", " '教育',\n", " '决策能力',\n", " '业务流程管理',\n", " '数据分析',\n", " '移动互联网',\n", " '金融',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '互联网金融',\n", " '银行',\n", " '节日礼物',\n", " '技能培训',\n", " '年度旅游',\n", " '岗位晋升',\n", " '数据分析',\n", " '互联网金融',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '商业',\n", " '数据库',\n", " '产品',\n", " '大数据',\n", " 'SQL',\n", " '数据分析',\n", " '商业',\n", " '大数据',\n", " 'SQLServer',\n", " 'Hive',\n", " '数据分析',\n", " '商业',\n", " '可视化',\n", " '数据分析',\n", " '数据分析',\n", " '房产服务',\n", " 'BI',\n", " '数据分析',\n", " '商业',\n", " '移动互联网',\n", " '数据分析',\n", " '企业服务',\n", " '移动互联网',\n", " '数据分析',\n", " '数据运营',\n", " '数据库',\n", " 'SQL',\n", " '数据分析',\n", " 'SQL',\n", " '数据库',\n", " '金融',\n", " 'NLP',\n", " '算法',\n", " '数据分析',\n", " '医疗健康',\n", " '大数据',\n", " '数据分析',\n", " 'BI',\n", " '数据运营',\n", " '数据库',\n", " '电商',\n", " '大数据',\n", " '数据分析',\n", " 'SPSS',\n", " '数据分析',\n", " 'SPSS',\n", " 'BI',\n", " '可视化',\n", " '数据分析',\n", " '移动互联网',\n", " 'SQL',\n", " '可视化',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '增长黑客',\n", " '数据分析',\n", " '数据运营',\n", " '商业',\n", " '数据分析',\n", " 'MySQL',\n", " '教育',\n", " '算法',\n", " '数据挖掘',\n", " '数据分析',\n", " 'ETL',\n", " 'MySQL',\n", " '数据分析',\n", " '数据处理',\n", " '互联网金融',\n", " '数据分析',\n", " '数据分析',\n", " '商业',\n", " '数据分析',\n", " 'SPSS',\n", " 'SQL',\n", " '广告营销',\n", " '大数据',\n", " '商业',\n", " '数据分析',\n", " 'SPSS',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '互联网金融',\n", " '借贷',\n", " '数据分析',\n", " '数据运营',\n", " '电商',\n", " '商业',\n", " 'BI',\n", " '数据分析',\n", " 'SQL',\n", " '数据分析',\n", " '教育',\n", " '数据分析',\n", " 'SQL',\n", " '大数据',\n", " '商业',\n", " 'BI',\n", " '可视化',\n", " '数据分析',\n", " '大数据',\n", " '金融',\n", " 'SQL',\n", " '数据分析',\n", " '大数据',\n", " '数据分析',\n", " '可视化',\n", " 'BI',\n", " '商业',\n", " '数据分析',\n", " '互联网金融',\n", " '借贷',\n", " '数据分析',\n", " '数据运营',\n", " '数据运营',\n", " '增长黑客',\n", " '数据分析',\n", " 'Hive',\n", " 'MySQL',\n", " 'Oracle',\n", " '电商',\n", " '数据',\n", " '大数据',\n", " '数据运营',\n", " '电商',\n", " 'SQL',\n", " '互联网金融',\n", " 'Oracle',\n", " '数据分析',\n", " 'Hive',\n", " '游戏',\n", " '数据分析',\n", " '数据运营',\n", " '大数据',\n", " '数据分析',\n", " '互联网金融',\n", " '借贷',\n", " '数据分析',\n", " 'MySQL',\n", " 'Spark',\n", " '电商',\n", " 'SQL',\n", " '数据分析',\n", " '数据运营',\n", " '数据库',\n", " '后端',\n", " '音频处理',\n", " '软件开发',\n", " '滴滴',\n", " '本地生活',\n", " '移动互联网',\n", " 'BI',\n", " '数据分析',\n", " '数据运营',\n", " '大数据',\n", " '金融',\n", " '数据挖掘',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '互联网金融',\n", " '理财',\n", " 'BI',\n", " '数据分析',\n", " '数据库',\n", " '大数据',\n", " '金融',\n", " 'BI',\n", " '数据分析',\n", " '电商',\n", " '数据分析',\n", " 'BI',\n", " 'SQL',\n", " '数据库',\n", " 'BI',\n", " '数据分析',\n", " '数据分析',\n", " '风控',\n", " '分析师',\n", " '数据分析',\n", " 'SQL',\n", " '数据分析',\n", " '数据分析',\n", " '大数据',\n", " '数据分析',\n", " 'SQL',\n", " '移动互联网',\n", " '企业服务',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " 'hadoop',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '数据分析',\n", " '金融',\n", " '数据分析',\n", " '数据运营',\n", " '医疗健康',\n", " '大数据',\n", " '数据分析',\n", " 'BI',\n", " '移动互联网',\n", " '数据分析',\n", " 'SQL',\n", " '大数据',\n", " '数据分析',\n", " '数据分析',\n", " '移动互联网',\n", " '数据分析',\n", " 'Hive',\n", " '数据处理',\n", " '数据挖掘',\n", " '电商',\n", " '数据分析',\n", " '数据分析',\n", " '数据处理',\n", " '移动互联网',\n", " '数据分析',\n", " '数据运营',\n", " '数据挖掘',\n", " '数据分析',\n", " 'Spark',\n", " '电商',\n", " '数据分析',\n", " 'SPSS',\n", " 'BI',\n", " 'SQL',\n", " '产品经理',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '金融',\n", " '数据挖掘',\n", " '数据分析',\n", " 'Hive',\n", " '数据分析',\n", " '数据分析',\n", " '数据运营',\n", " '数据库',\n", " 'BI',\n", " '数据分析',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " '大数据',\n", " '旅游',\n", " '数据挖掘',\n", " '数据分析',\n", " '数据处理',\n", " '算法',\n", " 'SQL',\n", " '增长黑客',\n", " '数据分析',\n", " '大数据',\n", " '数据分析',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '数据库',\n", " '电商',\n", " '数据分析',\n", " '数据分析',\n", " '教育',\n", " '产品',\n", " 'SQL',\n", " 'SPSS',\n", " 'SQL',\n", " 'SPSS',\n", " '数据运营',\n", " '数据分析',\n", " '大数据',\n", " '房产服务',\n", " '数据分析',\n", " 'MySQL',\n", " '数据分析',\n", " '数据分析',\n", " '互联网金融',\n", " '数据分析',\n", " '数据分析',\n", " '数据挖掘',\n", " 'SQL',\n", " '商业',\n", " '借贷',\n", " '数据分析',\n", " '数据处理',\n", " '数据分析',\n", " 'MySQL',\n", " 'SQLServer',\n", " '滴滴',\n", " '教育',\n", " 'BI',\n", " 'SQL',\n", " '商业',\n", " '数据运营',\n", " '数据分析',\n", " '移动互联网',\n", " '视频',\n", " '数据分析',\n", " '数据分析',\n", " '数据架构',\n", " '数据挖掘',\n", " '数据分析',\n", " 'SQL',\n", " '电商',\n", " '新零售',\n", " 'SQL',\n", " '数据分析',\n", " '数据运营',\n", " '大数据',\n", " '旅游',\n", " '数据仓库',\n", " '数据分析',\n", " '数据处理',\n", " 'Hive',\n", " '互联网金融',\n", " '电商',\n", " 'Hive',\n", " 'Hadoop',\n", " '数据分析',\n", " '医疗健康',\n", " '移动互联网',\n", " '数据分析',\n", " 'SQLServer',\n", " '借贷',\n", " '数据分析',\n", " 'SQL',\n", " '数据库',\n", " '运营',\n", " 'APP推广',\n", " '大数据',\n", " '数据分析',\n", " '数据分析',\n", " 'MySQL',\n", " 'Hive',\n", " '数据分析',\n", " 'SQL',\n", " '教育',\n", " '移动互联网',\n", " '数据分析',\n", " '商业',\n", " 'BI',\n", " '数据运营',\n", " '大数据',\n", " '金融',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '数据分析',\n", " '新零售',\n", " '汽车',\n", " 'BI',\n", " '可视化',\n", " '数据分析',\n", " 'SQL',\n", " '行业研究',\n", " '分析师',\n", " '游戏',\n", " '大数据',\n", " '数据分析',\n", " '数据分析',\n", " '效果跟踪',\n", " '新媒体运营',\n", " '数据分析',\n", " '电商',\n", " '数据分析',\n", " '数据分析',\n", " '保险',\n", " 'Hadoop',\n", " 'ETL',\n", " 'Hive',\n", " '数据分析',\n", " '数据分析',\n", " '金融',\n", " '数据分析',\n", " '数据处理',\n", " '数据分析',\n", " '算法',\n", " '电商',\n", " '数据分析',\n", " 'SPSS',\n", " '电商',\n", " '可视化',\n", " '商业',\n", " '数据分析',\n", " 'BI',\n", " '产品',\n", " '数据分析',\n", " '滴滴',\n", " '数据分析',\n", " '教育',\n", " '数据分析',\n", " 'SPSS',\n", " 'SQL',\n", " '大数据',\n", " '移动互联网',\n", " '市场分析',\n", " '数据分析',\n", " '行业分析',\n", " '市场',\n", " '数据分析',\n", " '电商',\n", " '移动互联网',\n", " '可视化',\n", " '数据分析',\n", " '数据运营',\n", " '增长黑客',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '数据库',\n", " '数据分析',\n", " 'BI',\n", " '数据分析',\n", " '大数据',\n", " '数据分析',\n", " '电商',\n", " 'ETL',\n", " '数据分析',\n", " '地图',\n", " '大数据',\n", " '审核',\n", " '大数据',\n", " '教育',\n", " '滴滴',\n", " '数据挖掘',\n", " '数据仓库',\n", " '数据分析',\n", " 'Hadoop',\n", " 'BI',\n", " '可视化',\n", " '数据分析',\n", " '数据运营',\n", " '数据分析',\n", " '大数据',\n", " '数据分析',\n", " '数据库',\n", " '增长黑客',\n", " '商业',\n", " '数据运营',\n", " '数据分析',\n", " '商业',\n", " 'BI',\n", " '数据分析',\n", " 'SQL',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '可视化',\n", " '数据分析',\n", " 'SPSS',\n", " '数据库',\n", " '大数据',\n", " '汽车',\n", " 'Java',\n", " '数据挖掘',\n", " '数据处理',\n", " '算法',\n", " '产品',\n", " '滴滴',\n", " '电商',\n", " 'SQL',\n", " '数据分析',\n", " '本地生活',\n", " '房产服务',\n", " '数据库',\n", " 'SQL',\n", " '数据分析',\n", " '大数据',\n", " '商业',\n", " '数据分析',\n", " '游戏',\n", " '数据分析',\n", " '数据运营',\n", " '汽车',\n", " '地图',\n", " '电商',\n", " '商业',\n", " '教育',\n", " 'MySQL',\n", " '数据分析',\n", " '数据分析',\n", " '电商',\n", " 'SQLServer',\n", " '数据分析',\n", " '数据分析',\n", " '游戏',\n", " '数据分析',\n", " '数据分析',\n", " '汽车',\n", " 'Hive',\n", " '数据分析',\n", " '数据分析',\n", " '数据分析',\n", " '数据',\n", " '互联网金融',\n", " '保险',\n", " '数据分析',\n", " 'BI',\n", " 'SPSS',\n", " 'SQL',\n", " '游戏',\n", " 'BI',\n", " '数据分析',\n", " '商业',\n", " 'SQL',\n", " '金融',\n", " '数据分析',\n", " '数据分析',\n", " '数据运营',\n", " '大数据',\n", " '医疗健康',\n", " '数据分析',\n", " 'SQL',\n", " '广告营销',\n", " '数据分析',\n", " '数据挖掘',\n", " '数据分析',\n", " 'Spark',\n", " 'Hadoop',\n", " 'Hive',\n", " '数据分析',\n", " 'SQL',\n", " '分析',\n", " '财务',\n", " '游戏',\n", " '移动互联网',\n", " 'BI',\n", " 'SQL',\n", " '数据分析',\n", " 'SPSS',\n", " '数据分析',\n", " '社交',\n", " '移动互联网',\n", " 'SQL',\n", " '商业',\n", " '数据分析',\n", " '增长黑客（GrowthHacking）',\n", " '汽车',\n", " '新零售',\n", " '数据运营',\n", " 'SQL',\n", " '数据分析',\n", " '商业',\n", " '大数据',\n", " '数据挖掘',\n", " '数据分析',\n", " '数据分析',\n", " '电商',\n", " '本地生活',\n", " '商业',\n", " 'BI',\n", " '可视化',\n", " '互联网金融',\n", " '移动互联网',\n", " '房产服务',\n", " '数据分析',\n", " 'MySQL',\n", " '数据挖掘',\n", " '数据分析',\n", " '电商',\n", " '数据分析',\n", " '数据运营',\n", " '可视化',\n", " '视频',\n", " '移动互联网',\n", " '商业',\n", " '数据分析',\n", " '数据运营',\n", " 'SQL',\n", " '审核',\n", " '数据分析',\n", " '滴滴',\n", " '直播',\n", " '社交',\n", " '数据分析',\n", " '互联网金融',\n", " '借贷',\n", " 'SQL',\n", " '可视化',\n", " '数据分析',\n", " 'SPSS',\n", " '数据挖掘',\n", " '算法',\n", " '数据挖掘',\n", " '数据分析',\n", " '数据运营',\n", " '新零售',\n", " '汽车',\n", " '可视化',\n", " 'SPSS',\n", " 'SQL',\n", " '数据分析',\n", " '数据分析',\n", " 'SQL',\n", " '商业',\n", " ...]" ] }, "execution_count": 411, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L = []\n", "for i in a:\n", " L += i\n", "s = ' '.join(L)\n", "s = s.split()\n", "s" ] }, { "cell_type": "code", "execution_count": 420, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'SPSS SQL MySQL Hive 数据运营 SPSS SQL 游戏 社交 SQL 商业 数据运营 SQL 电商 新零售 Hive MySQL 数据处理 电商 广告营销 增长黑客 SQL 商业 数据运营 教育 运营 效果跟踪 MySQL SQLServer 云计算 大数据 SPSS SQL 电商 新零售 运营 产品运营 用户运营 电商运营 移动互联网 需求分析 教育 算法 数据挖掘 大数据 商业 云计算 大数据 数据库 数据运营 SQL BI BI 电商 产品设计 产品策划 需求分析 移动互联网 移动互联网 行业分析 企业服务 房产服务 BI 数据库 数据运营 SQL BI 商业 MySQL 电商 汽车 商业 SQL SPSS MySQL SQLServer 大数据 商业 SPSS SQL 商业 SQL 大数据 金融 商业 游戏 商业 可视化 金融 SQL 数据库 数据运营 互联网金融 SQL 教育 移动互联网 用户增长 电商 SQL BI SQL 商业 社交 移动互联网 策略 用户研究 教育 SQL ETL 数据仓库 SQLServer 移动互联网 增长黑客 商业 数据运营 移动互联网 可视化 数据运营 SPSS SQL 直播 数据库 SQL 大数据 SQL 移动互联网 教育 大数据 金融 Hadoop 算法 数据挖掘 SQL SPSS 电商 新零售 数据运营 数据库 SQL 可视化 本地生活 数据运营 SQL BI 可视化 商业 SQL SQL 视频 工具软件 数据运营 SQL 商业 大数据 数据挖掘 教育 移动互联网 BI 可视化 SQL 数据库 SQL BI SQL 数据库 大数据 数据挖掘 项目管理 大数据 征信 新零售 汽车 大数据 数据挖掘 教育 BI 电商 新零售 数据仓库 数据处理 BI 增长黑客 SQL 数据库 SQL BI 可视化 BI SQL 数据库 医疗健康 大数据 互联网金融 MySQL 数据挖掘 算法 SQL 数据库 移动互联网 BI SQL 数据库 移动互联网 广告营销 移动互联网 SQL 运营 BI 金融 移动互联网 SPSS SQL 商业 大数据 BI SQL 算法 搜索 数据挖掘 旅游 SPSS 商业 BI 数据运营 大数据 数据挖掘 商业 BI 数据挖掘 数据挖掘 数据仓库 大数据 SPSS SQL 金融 移动互联网 电商 旅游 BI 产品经理 汽车 电商 商业 BI BI SQL SPSS 商业 数据库 SQL 可视化 增长黑客 视频 直播 SQL 数据运营 MySQL Scala 金融 大数据 数据挖掘 BI 分析师 互联网金融 MySQL 教育 决策能力 业务流程管理 移动互联网 金融 数据运营 SQL 互联网金融 银行 节日礼物 技能培训 年度旅游 岗位晋升 互联网金融 SQL 商业 数据库 产品 大数据 SQL 商业 大数据 SQLServer Hive 商业 可视化 房产服务 BI 商业 移动互联网 企业服务 移动互联网 数据运营 数据库 SQL SQL 数据库 金融 NLP 算法 医疗健康 大数据 BI 数据运营 数据库 电商 大数据 SPSS SPSS BI 可视化 移动互联网 SQL 可视化 增长黑客 数据运营 商业 MySQL 教育 算法 数据挖掘 ETL MySQL 数据处理 互联网金融 商业 SPSS SQL 广告营销 大数据 商业 SPSS SQL 数据库 互联网金融 借贷 数据运营 电商 商业 BI SQL 教育 SQL 大数据 商业 BI 可视化 大数据 金融 SQL 大数据 可视化 BI 商业 互联网金融 借贷 数据运营 数据运营 增长黑客 Hive MySQL Oracle 电商 大数据 数据运营 电商 SQL 互联网金融 Oracle Hive 游戏 数据运营 大数据 互联网金融 借贷 MySQL Spark 电商 SQL 数据运营 数据库 后端 音频处理 软件开发 滴滴 本地生活 移动互联网 BI 数据运营 大数据 金融 数据挖掘 SQL 互联网金融 理财 BI 数据库 大数据 金融 BI 电商 BI SQL 数据库 BI 风控 分析师 SQL 大数据 SQL 移动互联网 企业服务 数据运营 SQL 数据库 hadoop 大数据 数据挖掘 金融 数据运营 医疗健康 大数据 BI 移动互联网 SQL 大数据 移动互联网 Hive 数据处理 数据挖掘 电商 数据处理 移动互联网 数据运营 数据挖掘 Spark 电商 SPSS BI SQL 产品经理 大数据 数据挖掘 金融 数据挖掘 Hive 数据运营 数据库 BI SQL 数据库 大数据 旅游 数据挖掘 数据处理 算法 SQL 增长黑客 大数据 数据运营 SQL 数据库 电商 教育 产品 SQL SPSS SQL SPSS 数据运营 大数据 房产服务 MySQL 互联网金融 数据挖掘 SQL 商业 借贷 数据处理 MySQL SQLServer 滴滴 教育 BI SQL 商业 数据运营 移动互联网 视频 数据架构 数据挖掘 SQL 电商 新零售 SQL 数据运营 大数据 旅游 数据仓库 数据处理 Hive 互联网金融 电商 Hive Hadoop 医疗健康 移动互联网 SQLServer 借贷 SQL 数据库 运营 APP推广 大数据 MySQL Hive SQL 教育 移动互联网 商业 BI 数据运营 大数据 金融 数据运营 SQL 新零售 汽车 BI 可视化 SQL 行业研究 分析师 游戏 大数据 效果跟踪 新媒体运营 电商 保险 Hadoop ETL Hive 金融 数据处理 算法 电商 SPSS 电商 可视化 商业 BI 产品 滴滴 教育 SPSS SQL 大数据 移动互联网 市场分析 行业分析 市场 电商 移动互联网 可视化 数据运营 增长黑客 数据运营 SQL 数据库 BI 大数据 电商 ETL 地图 大数据 审核 大数据 教育 滴滴 数据挖掘 数据仓库 Hadoop BI 可视化 数据运营 大数据 数据库 增长黑客 商业 数据运营 商业 BI SQL 大数据 数据挖掘 可视化 SPSS 数据库 大数据 汽车 Java 数据挖掘 数据处理 算法 产品 滴滴 电商 SQL 本地生活 房产服务 数据库 SQL 大数据 商业 游戏 数据运营 汽车 地图 电商 商业 教育 MySQL 电商 SQLServer 游戏 汽车 Hive 互联网金融 保险 BI SPSS SQL 游戏 BI 商业 SQL 金融 数据运营 大数据 医疗健康 SQL 广告营销 数据挖掘 Spark Hadoop Hive SQL 财务 游戏 移动互联网 BI SQL SPSS 社交 移动互联网 SQL 商业 增长黑客（GrowthHacking） 汽车 新零售 数据运营 SQL 商业 大数据 数据挖掘 电商 本地生活 商业 BI 可视化 互联网金融 移动互联网 房产服务 MySQL 数据挖掘 电商 数据运营 可视化 视频 移动互联网 商业 数据运营 SQL 审核 滴滴 直播 社交 互联网金融 借贷 SQL 可视化 SPSS 数据挖掘 算法 数据挖掘 数据运营 新零售 汽车 可视化 SPSS SQL SQL 商业 数据运营 市场 大数据 互联网金融 SQL 运营 商业 BI 可视化 SQL 电商 电商 新零售 视频 数据处理 数据架构 数据挖掘 移动互联网 大数据 工具软件 增长黑客（GrowthHacking） 商业 增长黑客 数据运营 数据运营 分类信息 可视化 数据库 SQL 大数据 云计算 数据挖掘 算法 数据处理 SQL 数据库 电商 广告营销 电商 移动互联网 数据挖掘 Hive MySQL 移动互联网 保险 增长黑客 可视化 数据运营 SQL 电商 广告营销 大数据 视频 直播 行业分析 市场分析 市场竞争分析 商业 物流 数据处理 SQLServer 大数据 商业 BI 可视化 电商 移动互联网 SQL 物流 电商 新零售 商业 SQL 电商 新零售 教育 Hadoop MySQL 广告营销 大数据 BI 数据运营 汽车 SPSS SQL 电商 电商 房产服务 BI SQL 广告营销 SQL 审核 市场分析 行业分析 物流 SQLServer 电商 新零售 效果跟踪 免费班车 岗位晋升 五险一金 定期体检 增长黑客 SQL 数据挖掘 数据处理 互联网金融 教育 运营 效果跟踪 大数据 数据挖掘 其他 大数据 银行 数据挖掘 SQLServer 游戏 移动互联网 数据运营 大数据 移动互联网 数据运营 SQL SPSS 大数据 本地生活 移动互联网 医疗健康 物流 企业服务 大数据 算法 数据挖掘 数据架构 大数据 Hadoop 大数据 企业服务 SPSS 数据库 移动互联网 通信/网络设备 移动互联网 大数据 大数据 SQL 市场 数据挖掘 风控 产品 大数据 移动互联网 智能硬件 数据运营 BI 大数据 大数据 银行 数据挖掘 移动互联网 电商 数据运营 可视化 商业 MySQL 游戏 移动互联网 效果跟踪 移动互联网 电商 大数据 算法 数据挖掘 分析师 大数据 金融 数据库 教育 移动互联网 电商 运营 机器学习 数据挖掘 建模 大数据 数据运营 商业 大数据 金融 数据库 数据运营 SPSS SQL 数据库 大数据 信息安全 数据运营 大数据 商业 物流 金融 移动互联网 企业服务 金融 SQL 市场竞争分析 消费者分析 市场分析 数字营销 Oracle MySQL Hadoop 数据架构 大数据 企业服务 大数据 医疗健康 数据库 SQL DBA SQL 游戏运营 游戏运营 电商 物流 MySQL Oracle 数据处理 游戏 数据挖掘 大数据 移动互联网 分析师 互联网金融 大数据 基金 数据运营 电商 大数据 数据运营 SPSS 商业 大数据 金融 商业 可视化 数据运营 数据挖掘 算法 电商 移动互联网 SPSS 大数据 产品设计 产品策划 交互设计 移动互联网 SQL SPSS 数据处理 SQL 电商 数据运营 数据库 SQL 直播 视频 SQL 数据运营 BI Hive 数据挖掘 SQLServer 数据库 数据运营 移动互联网 教育 增长黑客 数据运营 可视化 产品设计 需求分析 数据挖掘 BI 电商 电商 大数据 商业 广告营销 移动互联网 数据运营 新零售 商业 数据运营 数据库 Hadoop 数据处理 MySQL 移动互联网 SQL 数据库 SQL BI SQL 数据库 电商 大数据 MySQL 数据挖掘 DBA 大数据 数据库 广告营销 新零售 物流 移动互联网 数据运营 数据库 电商 大数据 商业 SPSS BI 教育 移动互联网 电商 数据库 数据运营 商业 新零售 大数据 体育 移动互联网 数据运营 数据库 电商 可视化 商业 数据挖掘 数据架构 数据仓库 游戏 广告营销 电商 新零售 SQL 可视化 HR 人力资源 人事 大数据 移动互联网 运营 电商 电商 商业 BI 游戏 市场 广告营销 商业 移动互联网 汽车 运营 用户增长 游戏 产品运营 游戏运营 SQL 数据库 社交 移动互联网 SPSS SQL SQL 电商 大数据 数据挖掘 Hadoop MySQL 大数据 广告营销 SQL 可视化 数据库 医疗健康 大数据 SQLServer 数据处理 媒体 运维 SQL 数据处理 Hive 移动互联网 SQLServer 移动互联网 商业 增长黑客 BI 游戏 移动互联网 数据架构 数据挖掘 MySQL 社交 移动互联网 数据运营 增长黑客 互联网金融 数据挖掘 数据架构 数据处理 社交 大数据 客服 售前 售后 电商 BI Hive 金融 通信/网络设备 移动互联网 游戏 数据运营 医疗健康 移动互联网 可视化 SPSS SQL 移动互联网 移动互联网 电商 数据处理 云计算 大数据 数据挖掘 数据架构 数据仓库 大数据 算法 数据挖掘 电商 数据处理 MySQL 大数据 广告营销 游戏 电商 网店推广 推广 SEO 大数据 云计算 Hive MySQL Oracle SQL 电商 社交 商业 SQL SPSS 电商 BI 可视化 SQL 数据库 运营 策略运营 用户运营 大数据 算法 数据挖掘 BI SQL 大数据 Hive MySQL Oracle SQLServer Hadoop 大数据 广告营销 运营 用户增长 商业 数据运营 SPSS 工具软件 新零售 数据库 大数据 数据挖掘 项目管理 敏捷 解决方案 Hive 电商 可视化 数据运营 数据库 大数据 数据挖掘 移动互联网 游戏 MySQL 数据处理 游戏 数据运营 SPSS 大数据 数据挖掘 电商 大数据 教育 大数据 BI 商业 可视化 SPSS 数据处理 银行 数据挖掘 大数据 大数据 数据挖掘 游戏 移动互联网 SQL 电商 数据运营 游戏 游戏 数据库 SQL 可视化 大数据 企业服务 BI 可视化 数据运营 大数据 云计算 游戏 市场分析 数字营销 电商 大数据 数据运营 SQL 数据库 大数据 移动互联网 商业 SPSS SQL 移动互联网 信息安全 电商 数据运营 大数据 金融 BI 数据库 SQL SPSS 教育 SPSS SQL BI 风控 风险分析 数据库 SQL 大数据 新零售 产品运营 策略运营 数据运营 SQL 数据库 电商 游戏 数据挖掘 算法 数据处理 SQL 数据运营 游戏 数据运营 旅游 工具软件 互联网金融 理财 分析师 行业研究 风控 医疗健康 电商 推广 SEO 网店推广 大数据 市场 电商 大数据 算法 搜索 数据挖掘 PKI 游戏 大数据 游戏 电商 数据运营 SPSS SQL 数据库 SQL SPSS 产品设计 产品策划 需求分析 大数据 电商 数据运营 电商 外汇 理财顾问 汽车 大数据 数据运营 互联网金融 Hadoop 数据仓库 Oracle 游戏运营 产品运营 移动互联网 大数据 电商 财务 大数据 MySQL Oracle 游戏 游戏运营 Java NLP MySQL 工具软件 信息安全 增长黑客 运营 移动互联网 大数据 市场分析 产销协调 广告营销 电商 市场竞争分析 市场分析 消费者分析 营销策略 企业服务 大数据 数据挖掘 算法 数据处理 电商 新零售 市场分析 市场竞争分析 大数据 移动互联网 人工智能 建模 算法 数据挖掘 大数据 移动互联网 BI ETL Hadoop MySQL 电商 SQL 电商 Hive MySQL 电商 数据运营 SPSS 商业 大数据 云计算 电商 金融 SQL SPSS 商业 电商 BI SQL 社交 大数据 数据库 SQL 数据运营 电商 广告营销 移动互联网 工具软件 可视化 数据库 数据运营 电商 移动互联网 行业分析 市场分析 商业策划 商业 大数据 数据挖掘 移动互联网 广告营销 数据库 工具软件 移动互联网 算法 数据挖掘 移动互联网 工具软件 数据运营 数据库 大数据 软件开发 大数据 企业服务 电商 SQL 数据库 电商 直播 网站分析 市场 效果跟踪 商业 BI SQL 数据仓库 数据架构 电商 BI 大数据 BI 数据运营 电商 SQL 电商 大数据 大数据 金融 BI 电商 BI 电商 数据挖掘 保险 互联网金融 数据库 SQL 移动互联网 金融 Hadoop Hive 数据挖掘 分析师 大数据 数据挖掘 信息安全 新零售 电商 商业 运营 游戏 数据挖掘 互联网金融 MySQL Hive 电商 大数据 大数据 数据挖掘 大数据 BI 可视化 大数据 教育 大数据 电商 互联网金融 电商 Hive MySQL 数据架构 电商 广告营销 营销策略 消费者分析 市场分析 移动互联网 MySQL 数据处理 SPSS SQL 数据库 电商 Hive MySQL Spark 数据处理 数据挖掘 电商 数据挖掘 互联网金融 SQL 物流 数据运营 BI 可视化 电商 BI 电商 教育 电商 广告营销 市场分析 消费者分析 营销策略 大数据 电商 MySQL BI BI SQL 数据挖掘 搜索 策略运营 数据挖掘 Java Hive SQLServer 电商 BI 电商 数据挖掘 医疗健康 移动互联网 数据挖掘 数据仓库 MySQL 数据挖掘 大数据 医疗健康 企业服务 电商 大数据 数据处理 算法 电商 行业分析 企业服务 大数据 SQL 数据库 电商 数据处理 MySQL SQLServer 数据挖掘 移动互联网 电商 商业 数据运营 数据库 电商 财务 资产/项目评估 风控 滴滴 电商 用户运营 大数据 医疗健康 SQL SPSS 商业 体育 大数据 效果跟踪 跨境电商 大数据 电商 数据挖掘 数据仓库 数据架构 工具软件 大数据 大数据 金融 电商 数据库 企业服务 可视化 数据库 Hive MySQL 移动互联网 企业服务 大数据 电商 金融 大数据 企业服务 MySQL 电商 移动互联网 电商 电商 医疗健康 商业 可视化 数据运营 电商 新零售 电商 电商 电商 BI SQL 商业 MySQL 支付 MySQL Java 电商 物流 上市 资产管理 尽职调查 游戏 会计 数据仓库 数据库开发 其他 通信/网络设备 电商 新零售 算法 数据挖掘 大数据 信息安全 电商 移动互联网 SQLServer Hive 算法 电商 行业分析 市场分析 数据仓库 数据架构 数据挖掘 电商 SPSS SQL 数据挖掘 大数据 征信 算法 数据挖掘 大数据 信息安全 SQL 电商 大数据 BI 电商 SQLServer MySQL 电商 数据挖掘 数据运营 数据库 SQL 数据库 通信/网络设备 数据挖掘 算法 数据库开发 金融 风控 分析师 跨境电商 移动互联网 大数据 金融 数据挖掘 MySQL 数据库 BI 可视化 MySQL 电商 移动互联网 广告营销 策划 创意 Oracle 数据仓库 数据挖掘 新零售 BI 大数据 数据挖掘 移动互联网 新零售 物流 SQL 物流 数据库 大数据 互联网金融 医疗健康 MySQL Hive SQL 数据运营 大数据 可视化 数据库 SQL 电商 工具软件 SQL 数据处理 SQLServer 数据挖掘 SQL 信息安全 数据库 SQL 互联网金融 移动互联网 SQL 金融 SQL 数据库 可视化 数据库 大数据 移动互联网 SQL 互联网金融 SQL 游戏 SQL SPSS 移动互联网 教育 BI 数据运营 SQL 数据库 年底双薪 绩效奖金 岗位晋升 定期体检 SQL 大数据 电商 企业服务 可视化 SQL 数据运营 新零售 风控 数据运营 BI SQL 数据库 大数据 金融 商业 电商 数据挖掘 数据处理 MySQL 数据运营 移动互联网 商业 数据运营 社交 移动互联网 可视化 SQL BI Java 数据运营 信息安全 机器学习 建模 算法 BI 商业 互联网金融 MySQL 汽车 移动互联网 企业服务 大数据 MySQL 数据挖掘 BI 数据运营 商业 可视化 BI 互联网金融 风控 分析师 信用产品 编辑 大数据 商业 BI 可视化 电商 新零售 BI SQL 数据库 商业 BI 社交 移动互联网 大数据 SQL 互联网金融 数据运营 数据库 SQL 大数据 互联网金融 金融 商业 BI 可视化 移动互联网 本地生活 BI SQL 数据库 内容 大数据 可视化 SPSS 电商 金融 数据挖掘 云计算 大数据 BI SQL 游戏 SQL 电商 数据库 电商 大数据 BI 信息安全 大数据 数据仓库 数据架构 数据挖掘 大数据 征信 SPSS 金融 大数据 BI 可视化 数据库 大数据 医疗健康 MySQL ETL 数据仓库 旅游 大数据 ETL Hive 数据处理 大数据 其他 ETL DB2 电商 移动互联网 BI SQL 年底双薪 节日礼物 技能培训 绩效奖金 电商 大数据 数据运营 数据库 BI 可视化 SQL 游戏 广告营销 企业服务 商业 DBA 教育 移动互联网 数据运营 SQL 大数据 金融 金融 电商 移动互联网 大数据 可视化 数据运营 数据处理 数据库开发 电商 医疗健康 数据运营 电商 SPSS SQL 数据库 其他 金融 保险 SPSS SQL 商业 SPSS 电商 社交 数据处理 SQL 大数据 工具软件 旅游 移动互联网 电商 SQL Spark 互联网金融 互联网金融 SQLServer 数据挖掘 MySQL SPSS SQL 数据库 大数据 BI 移动互联网 数据仓库 数据库开发 大数据 移动互联网 本地生活 数据运营 SQL 数据库 BI 金融 大数据 MySQL SQLServer 数据处理 移动互联网 金融 SQL 大数据 商业 增长黑客 BI 数据运营 电商 本地生活 BI 商业 电商 社交 可视化 SQL 数据库 电商 大数据 移动互联网 数据仓库 电商 BI SPSS 新零售 大数据 数据挖掘 数据处理 商业 SQL 建模 深度学习 机器学习 数据挖掘 本地生活 BI 数据运营 SQL 大数据 BI SPSS 电商 BI 数据运营 SQL Hive MySQL 可视化 增长黑客 BI 社交 BI 可视化 数据运营 电商 移动互联网 数据挖掘 电商 数据架构 数据处理 大数据 BI 大数据 MySQL Hadoop 数据仓库 SPSS SQL 电商 SQLServer Hive SQL 数据库 数据库 风控 广告营销 数字营销 电商 新零售 SQL DBA 数据库 移动互联网 大数据 移动互联网 大数据 MySQL 风控 大数据 金融 数据处理 SQL BI 可视化 大数据 移动互联网 游戏 大数据 数据挖掘 信贷风险管理 风控 风险分析 移动互联网 SQL BI 可视化 大数据 数据挖掘 Hadoop 证券/期货 互联网金融 金融 大数据 数据仓库 数据库开发 移动互联网 MySQL 金融 数据挖掘 数据挖掘 数据处理 旅游 移动互联网 消费者分析 市场分析 海外市场 旅游 SPSS 电商 新零售 hadoop 风控 信贷风险管理 大数据 大数据 银行 MySQL SQLServer 移动互联网 MySQL 电商 大数据 移动互联网 商业 增长黑客 可视化 SQL 电商 SQL 商业 可视化 银行 数据挖掘 数据运营 数据库 SQL 大数据 大数据 商业 SPSS BI 数据库 DBA 电商 数据运营 大数据 移动互联网 BI 商业 SQL 可视化 SQL SQLServer 数据处理 大数据 金融 Hive MySQL 数据库 SQL 电商 新零售 数据运营 SQL 电商 其他 新零售 电商 MySQL 数据仓库 社交 大数据 媒体 数据挖掘 电商 SQL 可视化 HR 薪酬福利 人力资源 移动互联网 SPSS 大数据 旅游 SQL 汽车 金融 SQL 市场分析 市场竞争分析 行业分析 互联网金融 电商 电商 BI 可视化 SQL 本地生活 汽车 SQL 商业 移动互联网 数据运营 游戏 大数据 BI 游戏 商业 电商 移动互联网 BI 增长黑客（GrowthHacking） 大数据 数据运营 大数据 SPSS 广告创意 广告协调 SPSS 大数据 保险 金融 移动互联网 大数据 数据运营 电商 商业 数据运营 SQL 物流 大数据 可视化 增长黑客 数据运营 数据库 医疗健康 移动互联网 新零售 本地生活 BI 增长黑客 BI 可视化 大数据 金融 游戏 SPSS 大数据 数据挖掘 风控 风险管理 风险评估 游戏 MySQL 数据处理 电商 直播 银行 SQL 数据库 数据挖掘 游戏 BI 电商 数据运营 Oracle SQLServer 电商 移动互联网 数据运营 SQLServer 移动互联网 风控 新零售 电商 可视化 商业 教育 大数据 移动互联网 数据库 SQL 市场 广告营销 新零售 SQL 可视化 商业 风控 策略设计 电商 大数据 运营 金融 算法 数据挖掘 大数据 数据挖掘 教育 SPSS 商业 数据运营 SQL 数据挖掘 SQL 教育 大数据 数据挖掘 大数据 数据挖掘 工具软件 数据运营 SQL BI 可视化 股票期权 年底双薪 带薪年假 弹性工作 电商 运营 银行 商业 BI 可视化 视频 产品策划 智能硬件 BI 大数据 MySQL Oracle 新零售 大数据 大数据 金融 大数据 金融 数据挖掘 大数据 房产服务 企业服务 BI 可视化 数据库 大数据 数据库 BI 大数据 移动互联网 大数据 广告营销 汽车 营销 行业分析 市场分析 SQL 数据库 数据运营 SQLServer 数据挖掘 大数据 数据挖掘 大数据 新零售 数据运营 增长黑客 大数据 工具软件 移动互联网 SQLServer 数据处理 大数据 数据挖掘 大数据 算法 数据挖掘 电商 SQLServer 医疗健康 BI 教育 游戏 SQL 数据库 SQL Hive Flink Spark Hadoop 数据库开发 市场分析 营销策略 运营 SEM SEO 地图 分类信息 运营 大数据 企业服务 SQLServer 数据处理 新零售 电商 新零售 SQL 数据库 可视化 大数据 互联网金融 SQL 人力资源 电商 数据运营 可视化 BI 教育 数据运营 游戏 SQL 数据库 广告营销 数据运营 SQL 大数据 其他 SQL 数据库 电商 移动互联网 算法 数据架构 数据处理 移动互联网 教育 BI SQL 游戏 数据运营 银行 BI 数据库 移动互联网 BI 数据运营 大数据 大数据 金融 云计算 大数据 SPSS 数据运营 一手内推信息 猎头专业服务 名企保面试 精准高效对接 教育 移动互联网 SQL SPSS 新零售 大数据 商业 大数据 金融 电商 运营 金融 银行 ETL 数据处理 大数据 互联网金融 互联网金融 数据挖掘 互联网金融 风控 分析师 电商 数据运营 保险 大数据 Java 数据挖掘 数据处理 SQL 数据库 移动互联网 Hive SQLServer 数据架构 SQL 数据挖掘 通信/网络设备 电商 零食水果供应 带薪年假 绩效奖金 扁平管理 SQL 数据库 电商 MySQL 数据挖掘 数据处理 金融 SQL BI SQL 数据运营 金融 SQL 商业 数据仓库 数据处理 借贷 数据运营 SPSS SQL 电商 大数据 大数据 企业服务 BI SPSS SQL BI 大数据 数据挖掘 移动互联网 金融 风控 通信/网络设备 移动互联网 数据库 商业 SQL 大数据 工具软件 BI SQL 数据库 数据库开发 Hadoop 数据挖掘 大数据 Hive 数据挖掘 金融 大数据 风控 策略设计 分析师 SPSS 数据库 SQL 电商 SQL ETL Hadoop 数据库开发 MySQL 大数据 大数据 商业 BI 可视化 可视化 商业 金融 电商 广告营销 数据挖掘 通信/网络设备 大数据 电商 金融 数据挖掘 大数据 游戏 大数据 BI 数据运营 SQL 可视化 数据挖掘 电商 房产服务 BI 可视化 医疗健康 体育 BI 数据运营 SQL 大数据 数据处理 数据架构 MySQL 分析师 电商 大数据 金融 大数据 营销管理 BI 可视化 大数据 SPSS 视频 商业 SQL 移动互联网 大数据 SQL 数据运营 SPSS SQL 可视化 金融 大数据 互联网金融 风控 策略设计 分析师 企业服务 广告营销 可视化 数据运营 MySQL 大数据 BI 投资/融资 大数据 大数据 数据挖掘 运营 广告营销 游戏 人工智能 机器学习 算法 数据挖掘 移动互联网 云计算 电商 金融 算法 数据挖掘 新零售 本地生活 商业 数据库 SQL BI 商业 移动互联网 游戏 SQL 信息安全 支付 数据挖掘 教育 数据运营 节日礼物 技能培训 绩效奖金 岗位晋升 SQLServer 数据处理 电商 大数据 数据处理 电商 MySQL 数据仓库 数据库开发 大数据 云计算 数据库 BI SQL 大数据 基金 BI DBA SQL 大数据 产品经理 大数据 其他 SQLServer Spark Hadoop 算法 视频 直播 新零售 核查 互联网金融 借贷 SQL 数据挖掘 数据仓库 Oracle 电商 移动互联网 电商 数据挖掘 搜索 电商 数据运营 保险 互联网金融 云计算 数据运营 移动互联网 电商 数据运营 数据运营 SQL 用户研究 商业产品 大数据 银行 风控 大数据 云计算 大数据 电商 SQL 电商运营 SPSS SQL 可视化 大数据 金融 数据挖掘 教育 移动互联网 商业 增长黑客 数据运营 Hadoop Spark 数据挖掘 产品 移动互联网 证券/期货 BI 移动互联网 电商 旅游 大数据 MySQL 游戏 大数据 新零售 电商 商业 BI 可视化 SQL MySQL Spark Hive 数据处理 Hadoop NLP Redis MongoDB 电商 Hadoop Spark Scala 电商运营 电商 大数据 数据运营 增长黑客 大数据 数据挖掘 金融 风控 分析师 策略设计 大数据 SQL SPSS 五险一金 两餐补助 超低息员工贷 年底奖金 其他 移动互联网 Spark Hadoop 大数据 移动互联网 商业 数据挖掘 数据仓库 MySQL 大数据 数据处理 数据仓库 数据压缩 MongoDB 大数据 数据挖掘 电商 数据仓库 Hive SQL BI 数据运营 SQL MySQL Hadoop 电商 BI 大数据 数据处理 数据架构 MySQL 可视化 数据处理 SQLServer 大数据 Python 数据库 Hadoop Spark SPSS SQL 数据库 移动互联网 大数据 BI 金融 互联网金融 运营 产品运营 大数据 金融 可视化 大数据 互联网金融 风险分析 信贷风险管理 互联网金融 风控 风控 策略设计 企业服务 MySQL 数据处理 ETL 后端 大数据 Java 数据挖掘 算法 机器学习 其他 产品设计 产品策划 其他 通信/网络设备 企业服务 移动互联网 大数据 移动互联网 数据挖掘 BI 数据运营 SQL 电商 视频 移动互联网 大数据 Java 滴滴 广告营销 MySQL 产品设计 需求分析 数据挖掘 MySQL 数据挖掘 算法 数据挖掘 Python MySQL 数据库 Hadoop 移动互联网 数据挖掘 算法 产品策划 用户增长 产品策划 数据仓库 数据挖掘 数据仓库 数据挖掘 Hive MySQL 算法 数据仓库 大数据 数据库 SQL BI 电商 大数据 电商 SQLServer 大数据 其他 大数据 SPSS SQL 大数据 SQL 数据库 可视化 新零售 BI 可视化 移动互联网 大数据 数据运营 商业 SQLServer 数据挖掘 ETL 大数据 互联网金融 运营 产品运营 效果跟踪 电商 信息安全 数据运营 风控 视频 直播 数据运营 可视化 电商 数据运营 大数据 电商 算法 数据挖掘 数据处理 可视化 数据库 数据运营 大数据 企业服务 SQL SPSS SQL 大数据 物流 移动互联网 数据处理 数据挖掘 数据运营 新零售 电商 数据挖掘 数据仓库 电商 移动互联网 移动互联网 教育 SQL 数据库 信息安全 大数据 SQL SPSS 数据库 电商 可视化 BI 数据运营 大数据 BI 可视化 本地生活 数据处理 移动互联网 SQLServer 数据仓库 数据挖掘 SQLServer MySQL 数据仓库 数据挖掘 大数据 数据挖掘 电商 数据运营 策略运营 大数据 项目管理 大数据 产品设计 需求分析 项目管理 视频 SPSS SQL 大数据 数据挖掘 数据运营 数据运营 信息安全 证券/期货 MySQL Oracle Java SQLServer 大数据 广告营销 数据运营 大数据 移动互联网 可视化 商业 移动互联网 行业分析 市场竞争分析 广告营销 技术支持 直播 游戏 数据库 数据挖掘 MySQL SQL BI 数据库 MySQL Java Scala 大数据 征信 互联网金融 数据库 数据库 本地生活 大数据 数据挖掘 大数据 Java NLP 运营 物流 医疗健康 数据挖掘 数据仓库 算法 大数据 扁平管理 弹性工作 大厨定制三餐 就近租房补贴 交通补助 弹性工作 帅哥多 领导好 大数据 移动互联网 数据挖掘 大数据 SPSS SPSS SQL 信息安全 大数据 Hadoop Spark 数据挖掘 大数据 移动互联网 内容 数据运营 教育 数据挖掘 通信/网络设备 移动互联网 SPSS BI SPSS 证券 股票 证券分析 工具软件 移动互联网 数据挖掘 数据架构 数据仓库 算法 SQL 分类信息 大数据 数据挖掘 数据架构 数据仓库'" ] }, "execution_count": 420, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 删除里面的 数据分析 数据 分析\n", "s = [i for i in s if i not in ['数据分析', '数据', '分析']]\n", "s = ' '.join(s)\n", "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 421, "metadata": {}, "outputs": [], "source": [ "# from wordcloud import WordCloud\n", "# from PIL import Image\n", "# import numpy as np\n", "# from wordcloud import ImageColorGenerator\n", "\n", "# mask = np.array(Image.open(r\"C:\\Users\\EDZ\\Desktop\\案列\\拉勾网数据分析职位分析\\code\\数据分析.png\"))\n", "# image_colors = ImageColorGenerator(mask)\n", "\n", "# '''从文本中生成词云图'''\n", "# wordcloud = WordCloud(font_path=r\"C:\\Users\\EDZ\\Desktop\\字体\\bb1781\\微软vista雅黑.ttf\", # 定义SimHei字体文件\n", "# background_color='white', # 背景色为白色\n", "# height=400, # 高度设置为400\n", "# width=800, # 宽度设置为800\n", "# scale=20, # 长宽拉伸程度程度设置为20\n", "# prefer_horizontal=0.2, # 调整水平显示倾向程度为0.2\n", "# mask=mask, # 添加蒙版, 即轮廓\n", "# max_words=1000, # 设置最大显示字数为1000\n", "# relative_scaling=0.3, # 设置字体大小与词频的关联程度为0.3\n", "# max_font_size=80 # 缩小最大字体为80\n", "# ).generate(s)\n", "\n", "# plt.figure(figsize=[8, 4])\n", "# plt.imshow(wordcloud.recolor(color_func=image_colors), alpha=1)\n", "# plt.axis('off')\n", "# '''保存到本地'''\n", "# plt.savefig('数据分析技能词云图.jpg', dpi=600, bbox_inches='tight', quality=95)\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 423, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from wordcloud import WordCloud\n", "from PIL import Image\n", "import numpy as np\n", "\n", "mask = np.array(Image.open(r\"C:\\Users\\EDZ\\Desktop\\案列\\拉勾网数据分析职位分析\\code\\12.png\"))\n", "\n", "\n", "'''从文本中生成词云图'''\n", "wordcloud = WordCloud(font_path=r\"C:\\Users\\EDZ\\Desktop\\字体\\bb1781\\微软vista雅黑.ttf\", # 定义SimHei字体文件\n", " background_color='white', # 背景色为白色\n", " height=400, # 高度设置为400\n", " width=800, # 宽度设置为800\n", " scale=20, # 长宽拉伸程度程度设置为20\n", " prefer_horizontal=0.2, # 调整水平显示倾向程度为0.2\n", " mask=mask, # 添加蒙版, 即轮廓\n", " max_words=1000, # 设置最大显示字数为1000\n", " relative_scaling=0.3, # 设置字体大小与词频的关联程度为0.3\n", " max_font_size=80 # 缩小最大字体为80\n", " ).generate(s)\n", "\n", "plt.figure(figsize=(8, 6), dpi=800)\n", "plt.imshow(wordcloud)\n", "plt.axis('off')\n", "'''保存到本地'''\n", "plt.savefig('数据分析技能词云图.jpg', dpi=600)\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 各城市 数据分析职位数目 地理图" ] }, { "cell_type": "code", "execution_count": 427, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "北京 450\n", "上海 359\n", "深圳 260\n", "广州 175\n", "杭州 145\n", "成都 46\n", "武汉 44\n", "南京 27\n", "长沙 16\n", "厦门 14\n", "西安 12\n", "佛山 10\n", "郑州 9\n", "重庆 8\n", "苏州 8\n", "天津 7\n", "Name: 城市, dtype: int64" ] }, "execution_count": 427, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = df.城市.value_counts()\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 440, "metadata": {}, "outputs": [], "source": [ "def geo(a):\n", " # 导入地理类\n", " from pyecharts.charts import Geo\n", " # 导入配置项类\n", " from pyecharts import options as opts\n", " # 导入背景主题类\n", " from pyecharts.globals import ThemeType\n", "\n", " # 给地理图对象设置主题\n", " geo = Geo(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))\n", " # 设置地图北京 China\n", " geo.add_schema(maptype='china')\n", "\n", " # 添加数据\n", " geo.add( series_name='各城市数据分析职位地理图'\n", " , data_pair=list(zip(a.index, a.tolist())) \n", " , type_ = 'effectScatter')\n", "\n", " # 添加全局配置项\n", " # 群居配置项下的标题配置项\n", " geo.set_global_opts(title_opts= opts.TitleOpts(title = '全国各城市数据分析职位统计图'),\n", " # 全局配置项下的视觉设置配置项\n", " visualmap_opts=opts.VisualMapOpts(min_ = 0 , max_=400),\n", " # 全局配置项下的工具箱配置项\n", " toolbox_opts= opts.ToolboxOpts())\n", " # 系列配置项\n", " # 系列配置项下的标签配置项\n", " geo.set_series_opts(label_opts= opts.LabelOpts(is_show = False))\n", " return geo.render_notebook()" ] }, { "cell_type": "code", "execution_count": 441, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 441, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "165px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }