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) 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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