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, …)

(DEPRECATED) 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])

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(self[, sheet_name, header, …])

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(self, key, value[, format, append])

Store object in HDFStore

HDFStore.append(self, key, value[, format, …])

Append to Table in file.

HDFStore.get(self, key)

Retrieve pandas object stored in file

HDFStore.select(self, key[, where, start, …])

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

HDFStore.info(self)

Print detailed information on the store.

HDFStore.keys(self)

Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore.

HDFStore.groups(self)

return a list of all the top-level nodes (that are not themselves a pandas storage object)

HDFStore.walk(self[, 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(self, \*\*kwargs)

(DEPRECATED) Read observations from Stata file, converting them into a dataframe

StataReader.data_label

Return data label of Stata file.

StataReader.value_labels(self)

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

StataReader.variable_labels(self)

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

StataWriter.write_file(self)

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