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() |
© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/0.24.2/reference/io.html