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])	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.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, dtype, …])	Convert a JSON string to pandas object.
`to_json`(path_or_buf, obj[, orient, …])

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, …])

Render object to a LaTeX tabular, longtable, or nested table/tabular.

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])

Load an ORC object from the file path, returning a DataFrame.

SAS¶

read_sas(filepath_or_buffer[, format, …])

Read SAS files stored as either XPORT or SAS7BDAT format files.

SPSS¶

read_spss(path[, usecols, convert_categoricals])

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, …])

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 dict, associating each variable name a dict, associating each value its corresponding label.
`StataReader.variable_labels`()	Return variable labels as a dict, associating each variable name with corresponding label.
`StataWriter.write_file`()

API reference pandas.read_pickle