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.



Read text from clipboard and pass to read_csv.

DataFrame.to_clipboard([excel, sep])

Copy object to the system clipboard.


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.


read_json(path_or_buf[, orient, typ, dtype, ...])

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.


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.


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.


DataFrame.to_latex([buf, columns, ...])

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.


Retrieve pandas object stored in file.[, where, start, stop, ...])

Retrieve pandas object stored in file, optionally based on where criteria.

Print detailed information on the store.


Return a list of keys corresponding to objects stored in HDFStore.


Return a list of all the top-level nodes.


Walk the pytables group hierarchy for pandas objects.


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


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.


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.


read_orc(path[, columns])

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

DataFrame.to_orc([path, engine, index, ...])

Write a DataFrame to the ORC format.


read_sas(filepath_or_buffer[, format, ...])

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


read_spss(path[, usecols, convert_categoricals])

Load an SPSS file from the file path, returning a DataFrame.


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.


read_stata(filepath_or_buffer[, ...])

Read Stata file into DataFrame.

DataFrame.to_stata(path[, convert_dates, ...])

Export DataFrame object to Stata dta format.


Return data label of Stata file.


Return a dict, associating each variable name a dict, associating each value its corresponding label.


Return variable labels as a dict, associating each variable name with corresponding label.


Export DataFrame object to Stata dta format.