Input/Output

Pickling

read_pickle(path[, compression])

Load pickled pandas object (or any object) from file.

Flat File

read_table(filepath_or_buffer[, sep, …])	(DEPRECATED) Read general delimited file into DataFrame.
read_csv(filepath_or_buffer[, sep, …])	Read a comma-separated values (csv) file into DataFrame.
read_fwf(filepath_or_buffer[, colspecs, …])	Read a table of fixed-width formatted lines into DataFrame.
read_msgpack(path_or_buf[, encoding, iterator])	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[, record_path, meta, …])	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) Reads observations from Stata file, converting them into a dataframe
StataReader.data_label()	Returns data label of Stata file
StataReader.value_labels()	Returns a dict, associating each variable name a dict, associating each value its corresponding label
StataReader.variable_labels()	Returns variable labels as a dict, associating each variable name with corresponding label
StataWriter.write_file()