# Input/output

## Pickling

`read_pickle`(path[, compression]) | Load pickled pandas object (or any object) from file.

## Flat file

`read_table`(filepath_or_buffer, pathlib.Path, …) | Read general delimited file into DataFrame.
`read_csv`(filepath_or_buffer, pathlib.Path, …) | Read a comma-separated values (csv) file into DataFrame.
`read_fwf`(filepath_or_buffer, pathlib.Path, …) | Read a table of fixed-width formatted lines into DataFrame.
`read_msgpack`(path_or_buf[, encoding, iterator]) | (DEPRECATED) Load msgpack pandas object from the specified file path.

## Clipboard

`read_clipboard`([sep]) | Read text from clipboard and pass to read_csv.

## Excel

`read_excel`(io[, sheet_name, header, names, …]) | Read an Excel file into a pandas DataFrame.
`ExcelFile.parse`([sheet_name, header, names, …]) | Parse specified sheet(s) into a DataFrame

`ExcelWriter`(path[, engine, date_format, …]) | Class for writing DataFrame objects into excel sheets, default is to use xlwt for xls, openpyxl for xlsx.

## JSON

`read_json`([path_or_buf, orient, typ, dtype, …]) | Convert a JSON string to pandas object.

`json_normalize`(data, VT], List[Dict[KT, …) | Normalize semi-structured JSON data into a flat table.
`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.

## HDFStore: PyTables (HDF5)

`read_hdf`(path_or_buf[, key, mode]) | Read from the store, close it if we opened it.
`HDFStore.put`(key, value[, format, append]) | Store object in HDFStore
`HDFStore.append`(key, value[, format, …]) | 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`() | Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore.
`HDFStore.groups`() | return a list of all the top-level nodes (that are not themselves a pandas storage object)
`HDFStore.walk`([where]) | Walk the pytables group hierarchy for pandas objects

## Feather

`read_feather`(path[, columns, use_threads]) | Load a feather-format object from the file path.

## Parquet

`read_parquet`(path[, engine, columns]) | Load a parquet 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.

## 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.

## 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.

`StataReader.data`(**kwargs) | (DEPRECATED) Read observations from Stata file, converting them into a dataframe
`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`() |

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