如何使用 iPython 中的 Pandas 库读取 .xlsx 文件？

How to read a .xlsx file using the pandas Library in iPython?

提问by Sabareesh Kappagantu

I want to read a .xlsx file using the Pandas Library of python and port the data to a postgreSQL table.

我想使用 python 的 Pandas 库读取 .xlsx 文件并将数据移植到 postgreSQL 表。

All I could do up until now is:

到目前为止我能做的就是：

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

Now I know that the step got executed successfully, but I want to know how i can parse the excel file that has been read so that I can understand how the data in the excel maps to the data in the variable data.
I learnt that data is a Dataframe object if I'm not wrong. So How do i parse this dataframe object to extract each line row by row.

现在我知道步骤执行成功了，但是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data中的数据。
如果我没记错的话，我了解到 data 是一个 Dataframe 对象。那么我如何解析这个数据帧对象以逐行提取每一行。

采纳答案by Andy Hayden

I usually create a dictionary containing a DataFramefor every sheet:

我通常DataFrame为每张纸创建一个包含一个的字典：

xl_file = pd.ExcelFile(file_name)

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

---

---

Update: In pandas version 0.21.0+ you will get this behavior more cleanly by passing sheet_name=Noneto read_excel:

更新：在 Pandas 0.21.0+ 版本中，您将通过传递sheet_name=None到read_excel以下内容来更清晰地获得此行为：

dfs = pd.read_excel(file_name, sheet_name=None)

---

---

In 0.20 and prior, this was sheetnamerather than sheet_name(this is now deprecated in favor of the above):

在 0.20 和更早版本中，这是sheetname而不是sheet_name（现在已弃用以支持上述内容）：

dfs = pd.read_excel(file_name, sheetname=None)

回答by flowera

DataFrame's read_excelmethod is like read_csvmethod:

DataFrame 的read_excel方法类似于read_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.

回答by Hafizur Rahman

from pandas import read_excel
# find your sheet name at the bottom left of your excel file and assign 
# it to my_sheet 
my_sheet = 'Sheet1' # change it to your sheet name
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

回答by Patrick Mutuku

If you use read_excel()on a file opened using the function open(), make sure to add rbto the open function to avoid encoding errors

如果read_excel()在使用该函数打开的文件上使用open()，请确保添加rb到 open 函数以避免编码错误

回答by Harry

Instead of using a sheet name, in case you don't know or can't open the excel file to check in ubuntu (in my case, Python 3.6.7, ubuntu 18.04), I use the parameter index_col (index_col=0 for the first sheet)

如果您不知道或无法打开 excel 文件以检查 ubuntu（在我的情况下，Python 3.6.7，ubuntu 18.04），而不是使用工作表名称，我使用参数 index_col（index_col=0 for第一张）

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

回答by Danish

Assign spreadsheet filename to file

将电子表格文件名分配给 file

Load spreadsheet

加载电子表格

Print the sheet names

打印工作表名称

Load a sheet into a DataFrame by name: df1

按名称将工作表加载到 DataFrame 中：df1

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