如何阅读一个。xlsx文件使用熊猫库在iPython?

我通常为每个表创建一个包含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}

更新:在pandas 0.21.0+版本中，通过将sheet_name=None传递给read_excel，你可以更清晰地得到这个行为:

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

在0.20和更早的版本中，这是sheetname而不是sheet_name(现在已经弃用了，取而代之的是上面的):

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

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.

下面的方法对我很有效:

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

如果在使用open()函数打开的文件上使用read_excel()，请确保将rb添加到打开函数中以避免编码错误

我没有使用表名，以防你不知道或无法打开excel文件来检入ubuntu(在我的例子中，Python 3.6.7, ubuntu 18.04)，我使用参数index_col (index_col=0对于第一个表)

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

将电子表格文件名分配给file

负载电子表格

打印表名

通过名称:df1将一个表加载到数据帧中

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

pd.read_excel(file_name)

有时这段代码会给出xlsx文件的错误:XLRDError:Excel xlsx file; not supported

相反，你可以使用openpyxl引擎读取excel文件。

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