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"source": [
"# Pandas导入外部数据\n",
"\n",
"pandas支持方便快速的从多种格式的外部文件中读取数据形成DataFrame，或将DataFrame写入不同格式的外部文件。\n",
"下表是Pandas官方手册上给出的一张表格，表格描述的是Pandas中对各种数据文件类型的读、写函数。\n",
"\n",
"| Format Type | Data Description | Reader | Writer |\n",
"| :-----------: | :---------------------: | :--------------: | :---------------: |\n",
"| text | CSV | read_csv | to_csv |\n",
"| text | Fixed-Width Text File | read_fwf | |\n",
"| text | JSON | read_json | to_json |\n",
"| text | HTML | read_html | to_html |\n",
"| text | LaTeX | | Styler.to_latex |\n",
"| text | XML | read_xml | to_xml |\n",
"| text | Local clipboard | read_clipboard | to_clipboard |\n",
"| binary | MS Excel | read_excel | to_excel |\n",
"| binary | OpenDocument | read_excel | |\n",
"| binary | HDF5 Format | read_hdf | to_hdf |\n",
"| binary | Feather Format | read_feather | to_feather |\n",
"| binary | Parquet Format | read_parquet | to_parquet |\n",
"| binary | ORC Format | read_orc | |\n",
"| binary | Stata | read_stata | to_stata |\n",
"| binary | SAS | read_sas | |\n",
"| binary | SPSS | read_spss | |\n",
"| binary | Python Pickle Format | read_pickle | to_pickle |\n",
"| SQL | SQL | read_sql | to_sql |\n",
"| SQL | Google BigQuery | read_gbq | to_gbq |\n",
"\n",
"本节仅介绍几种常用文件的读取方式，其他方法大家如需使用可自行查询[相关文档](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)。\n",
"\n",
"## 读取txt或csv文件\n",
"```python\n",
"pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=None, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=None, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None)\n",
"```\n",
"该方法参数较多，下面简单介绍一些常用参数，其他参数含义可自行查询文档。\n",
"* filepath_or_buffer：读取的文件路径,URL（包含http,ftp,s3）链接等\n",
"* sep：指定分隔符，默认逗号分隔。\n",
"* delimiter：定界符，备选分隔符（如果指定该参数，则sep参数失效）\n",
"* delim_whitespace：boolean, 是否指定空白字符作为分隔符，如果为Ture那么delimiter参数失效。\n",
"* header：指定作为整个数据集列名的行，默认为第一行.如果数据集中没有列名，则需要设置为None\n",
"* names：用于结果的列名列表。如果数据文件中没有列标题行，就需要设置header=None\n",
"* index_col：指定数据集中的某列作为行索引\n",
"* usecols：指定只读取文件中的某几列数据\n",
"* dtype：设置每列数据的数据类型\n",
"* skiprows：设置需要忽略的行数（从文件开始处算起），或需要跳过的行号列表（从0开始）\n",
"* skipfooter：设置需要忽略的行数（从文件尾部处算起）\n",
"* nrows：设置需要读取的行数（从文件头开始算起）\n",
"* skip_blank_lines：如果为True，则跳过空行；否则记为NaN\n",
"\n",
"本节结合如下csv文件，仅讲解最简单用法，其他参数大家可结合数据文件自行尝试其效果。\n",
"<img src=\"images/ch8/11.png\" style=\"zoom:100%;\">"
]
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"metadata": {
"inputHidden": true
},
"source": [
"数据下载： \n",
"\n",
"<a href=\"images/ch8/2020各手机参数对比.xls\" target=\"_blank\">2020各手机参数对比.xls</a> \n",
"\n",
"<a href=\"images/ch8/wine point.csv\" target=\"_blank\">wine point.csv</a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"\n",
"wine_reviews = pd.read_csv(\"images/ch8/wine point.csv\")\n",
"\n",
"print(wine_reviews.shape) \n",
"pd.set_option('display.max_columns', None) # 显示所有列\n",
"pd.set_option('display.max_rows', None) # 显示所有行\n",
"pd.set_option('display.width', None) # 显示宽度是无限\n",
"print(wine_reviews) # 返回DataFrame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 明确规定某列做行索引\n",
"wine_reviews = pd.read_csv(\"images/ch8/wine point.csv\", index_col=6)\n",
"print(wine_reviews) # 返回DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {
"inputHidden": true
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"source": [
"## 读取excel文件\n",
"\n",
"```python\n",
"pandas.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, decimal='.', comment=None, skipfooter=0, convert_float=None, mangle_dupe_cols=True, storage_options=None)\n",
"```\n",
"\n",
"该函数大部分参数与read_csv()方法相同。其中sheet_name用于指定要读取的sheet，默认读取Excel文件中的第一个sheet。\n",
"需要注意的是，使用该方法读取.xlsx文件需要借助openpyxl模块，读取.xls文件需要借助xlrd模块。\n",
"\n",
"本节结合文件“2020各手机参数对比.xls”，仅讲解最简单用法，其他参数大家可结合数据文件自行尝试其效果。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"phone_infos = pd.read_excel(\"images/ch8/2020各手机参数对比.xls\", skiprows=2) # 跳过第一行的表格标题\n",
"# 读取/data/bigfiles/wine point.csv\n",
"print(phone_infos.shape) \n",
"pd.set_option('display.max_columns', None) # 显示所有列\n",
"pd.set_option('display.max_rows', None) # 显示所有行\n",
"pd.set_option('display.width', None) # 显示宽度是无限\n",
"pd.set_option('display.unicode.east_asian_width', True) # 显示时列对齐\n",
"print(phone_infos) # 返回DataFrame"
]
},
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"cell_type": "markdown",
"metadata": {
"inputHidden": true
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"source": [
"### 读取HTML网页\n",
"```python\n",
"pandas.read_html(io, match='.+', flavor=None, header=None, index_col=None, skiprows=None, attrs=None, parse_dates=False, thousands=',', encoding=None, decimal='.', converters=None, na_values=None, keep_default_na=True, displayed_only=True)\n",
"```\n",
"该函数是将HTML的表格转换为DataFrame的一种快速方便的方法，不需要用爬虫获取站点的HTML。match参数通过正则表达式匹配需要的表格；flavor参数设置解析器，默认为lxml。\n",
"\n",
"本节以[ESPN网站的中超排名表](https://www.espn.com/soccer/team/_/id/21506/wuhan-three-towns)为例，讲解其基本用法。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pip install lxml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pip install html5lib"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
"Collecting BeautifulSoup4\n",
" Downloading https://mirrors.aliyun.com/pypi/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)\n",
"\u001b[K |████████████████████████████████| 147 kB 4.3 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting soupsieve>1.2\n",
" Downloading https://mirrors.aliyun.com/pypi/packages/49/37/673d6490efc51ec46d198c75903d99de59baffdd47aea3d071b80a9e4e89/soupsieve-2.4.1-py3-none-any.whl (36 kB)\n",
"Installing collected packages: soupsieve, BeautifulSoup4\n",
"Successfully installed BeautifulSoup4-4.12.3 soupsieve-2.4.1\n",
"\u001b[33mWARNING: You are using pip version 21.1.3; however, version 24.0 is available.\n",
"You should consider upgrading via the '/home/jovyan/work/.localenv/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install BeautifulSoup4\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "No tables found",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_149/3569134230.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'display.unicode.east_asian_width'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 显示时列对齐\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# 函数会将每个table转化为一个DataFrame，返回由两个DataFrame构成的列表\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mCSL_2022\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://www.taobao.com/'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 该页面只有1个table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCSL_2022\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCSL_2022\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCSL_2022\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/basenv/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/basenv/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mread_html\u001b[0;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, thousands, encoding, decimal, converters, na_values, keep_default_na, displayed_only)\u001b[0m\n\u001b[1;32m 1111\u001b[0m \u001b[0mna_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1112\u001b[0m \u001b[0mkeep_default_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_default_na\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1113\u001b[0;31m \u001b[0mdisplayed_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisplayed_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1114\u001b[0m )\n",
"\u001b[0;32m~/.virtualenvs/basenv/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m 924\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 925\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mretained\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;31m# for mypy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mretained\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/basenv/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m 904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 905\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 906\u001b[0;31m \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 907\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcaught\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;31m# if `io` is an io-like object, check if it's seekable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/basenv/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mparse_tables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mparsed\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfooter\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mtuples\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_doc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_thead_tbody_tfoot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/basenv/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse_tables\u001b[0;34m(self, doc, match, attrs)\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No tables found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: No tables found"
]
}
],
"source": [
"import pandas as pd\n",
"pd.set_option('display.max_columns', None) # 显示所有列\n",
"pd.set_option('display.max_rows', None) # 显示所有行\n",
"pd.set_option('display.width', None) # 显示宽度是无限\n",
"pd.set_option('display.unicode.east_asian_width', True) # 显示时列对齐\n",
"# 函数会将每个table转化为一个DataFrame，返回由两个DataFrame构成的列表\n",
"CSL_2022 = pd.read_html('https://www.taobao.com/') # 该页面只有1个table \n",
"print(type(CSL_2022), type(CSL_2022[0]))\n",
"print(CSL_2022[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
