问题
I want to read a .xlsx file using the Pandas Library of python and port the data to a postgreSQL table.
All I could do up until now is:
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.
回答1:
I usually create a dictionary containing a DataFrame for every sheet:
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=None to read_excel:
dfs = pd.read_excel(file_name, sheet_name=None)
In 0.20 and prior, this was sheetname rather than sheet_name (this is now deprecated in favor of the above):
dfs = pd.read_excel(file_name, sheetname=None)
回答2:
from pandas import read_excel
# find your sheet name at the bottom left of your excel file and assign
# it to sheet_name
my_sheet = 'Sheet1'
file_name = 'products_and_categories.xlsx' # 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3:
DataFrame's read_excel method is like read_csv method:
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4:
Assign spreadsheet filename to file
Load spreadsheet
Print the sheet names
Load a sheet into a DataFrame by name: df1
file = 'example.xlsx'
xl = pd.ExcelFile(file)
print(xl.sheet_names)
df1 = xl.parse('Sheet1')
回答5:
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)
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6:
If you use read_excel() on a file opened using the function open(), make sure to add rb to the open function to avoid encoding errors
来源：https://stackoverflow.com/questions/16888888/how-to-read-a-xlsx-file-using-the-pandas-library-in-ipython