人生苦短我用Python pandas文件格式转换

前言

pandas支持多种文件格式，通过pandas的IO方法，可以实现不同格式之间的互相转换。本文通过excel与csv互转的示例和pandas的支持的文件格式，实现一个简单的文件格式转换的功能。

示例1 excel与csv互转

在前文实现了excel转csv，即通过pandas将excel转csv，反过来也可以将csv转为excel。

下面是excel与csv互转的示例代码：

• excel转csv

def export_csv(input_file, output_path):# 创建ExcelFile对象with pd.ExcelFile(input_file) as xls:# 获取工作表名称列表for i, sheet_name in enumerate(xls.sheet_names):# 读取工作表并转换为DataFramedf = pd.read_excel(xls, sheet_name=sheet_name)output_file = os.path.join(output_path, f'{i + 1}-{sheet_name}.csv')# 将DataFrame中的数据写入CSV文件。df.to_csv(output_file, index=False)

• csv转为excel

def export_excel(input_file, output_file):if not output_file:input_path = pathlib.Path(input_file)output_path = input_path.parent / (input_path.stem + '.xlsx')output_file = str(output_path)df = pd.read_csv(input_file)df.to_excel(output_file, index=False)

常用格式的方法

以下来自pandas官网 Input/Outout部分

Flat file

方法	说明
read_table(filepath_or_buffer, *[, sep, …])	Read general delimited file into DataFrame.
read_csv(filepath_or_buffer, *[, sep, …])	Read a comma-separated values (csv) file into DataFrame.
DataFrame.to_csv([path_or_buf, sep, na_rep, …])	Write object to a comma-separated values (csv) file.
read_fwf(filepath_or_buffer, *[, colspecs, …])	Read a table of fixed-width formatted lines into DataFrame.

Excel

方法	说明
read_excel(io[, sheet_name, header, names, …])	Read an Excel file into a pandas DataFrame.
DataFrame.to_excel(excel_writer, *[, …])	Write object to an Excel sheet.
ExcelFile(path_or_buffer[, engine, …])	Class for parsing tabular Excel sheets into DataFrame objects.
ExcelFile.book
ExcelFile.sheet_names
ExcelFile.parse([sheet_name, header, names, …])	Parse specified sheet(s) into a DataFrame.

方法	说明
Styler.to_excel(excel_writer[, sheet_name, …])	Write Styler to an Excel sheet.

方法	说明
ExcelWriter(path[, engine, date_format, …])	Class for writing DataFrame objects into excel sheets.

JSON

方法	说明
read_json(path_or_buf, *[, orient, typ, …])	Convert a JSON string to pandas object.
json_normalize(data[, record_path, meta, …])	Normalize semi-structured JSON data into a flat table.
DataFrame.to_json([path_or_buf, orient, …])	Convert the object to a JSON string.

方法	说明
build_table_schema(data[, index, …])	Create a Table schema from data.

XML

方法	说明
read_xml(path_or_buffer, *[, xpath, …])	Read XML document into a DataFrame object.
DataFrame.to_xml([path_or_buffer, index, …])	Render a DataFrame to an XML document.

示例2 常用格式转换

根据常用格式的IO方法，完成一个常用格式的格式转换功能。

第一步从指定格式的文件中读取数据，并将其转换为 DataFrame 对象。

第二部将 DataFrame 中的数据写入指定格式的文件中。

简要需求

• 根据输入输出的文件后缀名，自动进行格式转换，若格式不支持输出提示。
• 支持的格式csv，xlsx，json，xml。

依赖

pip install pandas
pip install openpyxl
pip install lxml

export方法

def export(input_file, output_file):if not os.path.isfile(input_file):print('Input file does not exist')returnif input_file.endswith('.csv'):df = pd.read_csv(input_file, encoding='utf-8')elif input_file.endswith('.json'):df = pd.read_json(input_file, encoding='utf-8')elif input_file.endswith('.xlsx'):df = pd.read_excel(input_file)elif input_file.endswith('.xml', encoding='utf-8'):df = pd.read_xml(input_file)else:print('Input file type not supported')returnif output_file.endswith('.csv'):df.to_csv(output_file, index=False)elif output_file.endswith('.json'):df.to_json(output_file, orient='records', force_ascii=False)elif output_file.endswith('.xlsx'):df.to_excel(output_file, index=False)elif output_file.endswith('.xml'):df.to_xml(output_file, index=False)elif output_file.endswith('.html'):df.to_html(output_file, index=False, encoding='utf-8')else:print('Output file type not supported')return

main方法

def main(argv):input_path = Noneoutput_path = Nonetry:shortopts = "hi:o:"longopts = ["ipath=", "opath="]opts, args = getopt.getopt(argv, shortopts, longopts)except getopt.GetoptError:print('usage: export.py -i <inputpath> -o <outputpath>')sys.exit(2)for opt, arg in opts:if opt in ("-h", "--help"):print('usage: export.py -i <inputpath> -o <outputpath>')sys.exit()elif opt in ("-i", "--ipath"):input_path = argelif opt in ("-o", "--opath"):output_path = argprint(f'输入路径为：{input_path}')print(f'输出路径为：{output_path}')export(input_path, output_path)

附其它格式的方法

以下来自pandas官网 Input/Outout部分

HTML

方法	说明
read_html(io, *[, match, flavor, header, …])	Read HTML tables into a list of DataFrame objects.
DataFrame.to_html([buf, columns, col_space, …])	Render a DataFrame as an HTML table.

方法	说明
Styler.to_html([buf, table_uuid, …])	Write Styler to a file, buffer or string in HTML-CSS format.

Pickling

方法	说明
read_pickle(filepath_or_buffer[, …])	Load pickled pandas object (or any object) from file.
DataFrame.to_pickle(path, *[, compression, …])	Pickle (serialize) object to file.

Clipboard

方法	说明
read_clipboard([sep, dtype_backend])	Read text from clipboard and pass to read_csv().
DataFrame.to_clipboard(*[, excel, sep])	Copy object to the system clipboard.

Latex

方法	说明
DataFrame.to_latex([buf, columns, header, …])	Render object to a LaTeX tabular, longtable, or nested table.

方法	说明
Styler.to_latex([buf, column_format, …])	Write Styler to a file, buffer or string in LaTeX format.

HDFStore: PyTables (HDF5)

方法	说明
read_hdf(path_or_buf[, key, mode, errors, …])	Read from the store, close it if we opened it.
HDFStore.put(key, value[, format, index, …])	Store object in HDFStore.
HDFStore.append(key, value[, format, axes, …])	Append to Table in file.
HDFStore.get(key)	Retrieve pandas object stored in file.
HDFStore.select(key[, where, start, stop, …])	Retrieve pandas object stored in file, optionally based on where criteria.
HDFStore.info()	Print detailed information on the store.
HDFStore.keys([include])	Return a list of keys corresponding to objects stored in HDFStore.
HDFStore.groups()	Return a list of all the top-level nodes.
HDFStore.walk([where])	Walk the pytables group hierarchy for pandas objects.

Warning

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

Feather

方法	说明
read_feather(path[, columns, use_threads, …])	Load a feather-format object from the file path.
DataFrame.to_feather(path, **kwargs)	Write a DataFrame to the binary Feather format.

Parquet

方法	说明
read_parquet(path[, engine, columns, …])	Load a parquet object from the file path, returning a DataFrame.
DataFrame.to_parquet([path, engine, …])	Write a DataFrame to the binary parquet format.

ORC

方法	说明
read_orc(path[, columns, dtype_backend, …])	Load an ORC object from the file path, returning a DataFrame.
DataFrame.to_orc([path, engine, index, …])	Write a DataFrame to the ORC format.

SAS

方法	说明
read_sas(filepath_or_buffer, *[, format, …])	Read SAS files stored as either XPORT or SAS7BDAT format files.

SPSS

方法	说明
read_spss(path[, usecols, …])	Load an SPSS file from the file path, returning a DataFrame.

SQL

方法	说明
read_sql_table(table_name, con[, schema, …])	Read SQL database table into a DataFrame.
read_sql_query(sql, con[, index_col, …])	Read SQL query into a DataFrame.
read_sql(sql, con[, index_col, …])	Read SQL query or database table into a DataFrame.
DataFrame.to_sql(name, con, *[, schema, …])	Write records stored in a DataFrame to a SQL database.

Google BigQuery

方法	说明
read_gbq(query[, project_id, index_col, …])	(DEPRECATED) Load data from Google BigQuery.

STATA

方法	说明
read_stata(filepath_or_buffer, *[, …])	Read Stata file into DataFrame.
DataFrame.to_stata(path, *[, convert_dates, …])	Export DataFrame object to Stata dta format.

方法	说明
StataReader.data_label	Return data label of Stata file.
StataReader.value_labels()	Return a nested dict associating each variable name to its value and label.
StataReader.variable_labels()	Return a dict associating each variable name with corresponding label.
StataWriter.write_file()	Export DataFrame object to Stata dta format.