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# Input/output

## Pickling

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

## Flat file

`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.

## Clipboard

`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.

## Excel

`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.

`Styler.to_excel`(excel_writer[, sheet_name, ...]) | Write Styler to an Excel sheet.

`ExcelWriter`(path[, engine, date_format, ...]) | Class for writing DataFrame objects into excel sheets.

## JSON

`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.

`build_table_schema`(data[, index, ...]) | Create a Table schema from `data`.

## HTML

`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.

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

## XML

`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.

## Latex

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

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

## HDFStore: PyTables (HDF5)

`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

`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

`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

`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

`read_sas`(filepath_or_buffer, *[, format, ...]) | Read SAS files stored as either XPORT or SAS7BDAT format files.

## SPSS

`read_spss`(path[, usecols, ...]) | Load an SPSS file from the file path, returning a DataFrame.

## SQL

`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

`read_gbq`(query[, project_id, index_col, ...]) | (DEPRECATED) Load data from Google BigQuery.

## STATA

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

`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.