如何使用iPython中的pandas库读取.xlsx文件？

我想使用python的Pandas库读取一个.xlsx文件，并将数据移植到postgreSQL表中

到目前为止，我所能做的就是：

import pandas as pd
data = pd.ExcelFile("*File Name*")

现在我知道该步骤已成功执行，但我想知道如何解析已读取的excel文件，以便了解excel中的数据如何映射到变量数据中的数据

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如果我没有错的话，我知道数据是一个数据帧对象。那么，如何解析此dataframe对象以逐行提取每一行。

我通常为每个工作表创建一个包含

dataframe

的字典：

xl_file = pd.ExcelFile(file_name)

dfs = {sheet_name: xl_file.parse(sheet_name) 
          for sheet_name in xl_file.sheet_names}

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更新：在pandas版本0.21.0+中，通过传递到以下位置，您将更清楚地了解此行为：

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在0.20及更早版本中，这是

sheet name

，而不是

sheet\u name

（现在为了支持上述内容，不推荐使用此选项）：

---

DataFrame的

read\u excel

方法类似于

read\u csv

方法：

dfs = pd.read_excel(xlsx_file, sheetname="sheet1")


Help on function read_excel in module pandas.io.excel:

read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds)
    Read an Excel table into a pandas DataFrame

    Parameters
    ----------
    io : string, path object (pathlib.Path or py._path.local.LocalPath),
        file-like object, pandas ExcelFile, or xlrd workbook.
        The string could be a URL. Valid URL schemes include http, ftp, s3,
        and file. For file URLs, a host is expected. For instance, a local
        file could be file://localhost/path/to/workbook.xlsx
    sheetname : string, int, mixed list of strings/ints, or None, default 0

        Strings are used for sheet names, Integers are used in zero-indexed
        sheet positions.

        Lists of strings/integers are used to request multiple sheets.

        Specify None to get all sheets.

        str|int -> DataFrame is returned.
        list|None -> Dict of DataFrames is returned, with keys representing
        sheets.

        Available Cases

        * Defaults to 0 -> 1st sheet as a DataFrame
        * 1 -> 2nd sheet as a DataFrame
        * "Sheet1" -> 1st sheet as a DataFrame
        * [0,1,"Sheet5"] -> 1st, 2nd & 5th sheet as a dictionary of DataFrames
        * None -> All sheets as a dictionary of DataFrames

    header : int, list of ints, default 0
        Row (0-indexed) to use for the column labels of the parsed
        DataFrame. If a list of integers is passed those row positions will
        be combined into a ``MultiIndex``
    skiprows : list-like
        Rows to skip at the beginning (0-indexed)
    skip_footer : int, default 0
        Rows at the end to skip (0-indexed)
    index_col : int, list of ints, default None
        Column (0-indexed) to use as the row labels of the DataFrame.
        Pass None if there is no such column.  If a list is passed,
        those columns will be combined into a ``MultiIndex``
    names : array-like, default None
        List of column names to use. If file contains no header row,
        then you should explicitly pass header=None
    converters : dict, default None
        Dict of functions for converting values in certain columns. Keys can
        either be integers or column labels, values are functions that take one
        input argument, the Excel cell content, and return the transformed
        content.
    true_values : list, default None
        Values to consider as True

        .. versionadded:: 0.19.0

    false_values : list, default None
        Values to consider as False

        .. versionadded:: 0.19.0

    parse_cols : int or list, default None
        * If None then parse all columns,
        * If int then indicates last column to be parsed
        * If list of ints then indicates list of column numbers to be parsed
        * If string then indicates comma separated list of column names and
          column ranges (e.g. "A:E" or "A,C,E:F")
    squeeze : boolean, default False
        If the parsed data only contains one column then return a Series
    na_values : scalar, str, list-like, or dict, default None
        Additional strings to recognize as NA/NaN. If dict passed, specific
        per-column NA values. By default the following values are interpreted
        as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
    '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'nan'.
    thousands : str, default None
        Thousands separator for parsing string columns to numeric.  Note that
        this parameter is only necessary for columns stored as TEXT in Excel,
        any numeric columns will automatically be parsed, regardless of display
        format.
    keep_default_na : bool, default True
        If na_values are specified and keep_default_na is False the default NaN
        values are overridden, otherwise they're appended to.
    verbose : boolean, default False
        Indicate number of NA values placed in non-numeric columns
    engine: string, default None
        If io is not a buffer or path, this must be set to identify io.
        Acceptable values are None or xlrd
    convert_float : boolean, default True
        convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
        data will be read in as floats: Excel stores all numbers as floats
        internally
    has_index_names : boolean, default None
        DEPRECATED: for version 0.17+ index names will be automatically
        inferred based on index_col.  To read Excel output from 0.16.2 and
        prior that had saved index names, use True.

    Returns
    -------
    parsed : DataFrame or Dict of DataFrames
        DataFrame from the passed in Excel file.  See notes in sheetname
        argument for more information on when a Dict of Dataframes is returned.

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以下几点对我很有用：

from pandas import read_excel
my_sheet = 'Sheet1' # change it to your sheet name, you can find your sheet name at the bottom left of your excel file
file_name = 'products_and_categories.xlsx' # change it to the name of your excel file
df = read_excel(file_name, sheet_name = my_sheet)
print(df.head()) # shows headers with top 5 rows

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如果您对使用函数

open（）

打开的文件使用

read_excel（）

，请确保将

rb

添加到open函数中，以避免编码错误，而不是使用工作表名称，以防您不知道或无法打开excel文件以签入ubuntu（在我的例子中，是Python 3.6.7，ubuntu 18.04），我使用参数index_col（第一张图纸的index_col=0）

---

将电子表格文件名分配给

文件

加载电子表格

打印图纸名称

按名称将工作表加载到数据帧中：df1

file = 'example.xlsx'
xl = pd.ExcelFile(file)
print(xl.sheet_names)
df1 = xl.parse('Sheet1')

有时，此代码会为xlsx文件提供一个错误，如：

XLRDError:excelXLSX文件；不支持

相反，您可以使用

openpyxl

引擎来读取excel文件

df_samples = pd.read_excel(r'filename.xlsx', engine='openpyxl')

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它对我有用

df=pd.ExcelFile（'File Name'）.parse（'sheet 1'）；见医生谢谢安迪。这起作用了。现在，我的下一步是将其写入postgreSQL数据库。哪个图书馆最适合使用？SQLAlchemy？嗯，如果你说，博士后可能也有类似的效果。。。但不是100%。（这是个好问题。）我知道怎么做了。我用了炼金术。你是对的，它与mysql非常相似。它包括创建一个引擎，然后收集元数据并处理数据。再次感谢安迪！：）感谢您的帮助。

pandas.DataFrame.to\u sql

可能会有所帮助。对于阅读，您可以使用返回数据帧对象的

dp.py

。请使用

openpyxl

引擎更新此答案，如前所述。您还可以使用

sheet_name=0

或命名工作表而不是0。正确，它可以工作。但它需要依赖xlrd。（pip3.7.4.exe在Windows上安装xlrd）

import pandas as pd
file_name = 'some_data_file.xlsx' 
df = pd.read_excel(file_name, index_col=0)
print(df.head()) # print the first 5 rows

file = 'example.xlsx'
xl = pd.ExcelFile(file)
print(xl.sheet_names)
df1 = xl.parse('Sheet1')

pd.read_excel(file_name) 

df_samples = pd.read_excel(r'filename.xlsx', engine='openpyxl')