最終更新:2019-08-13 (火) 18:43:54 (1717d)
pandas/関数
https://pandas.pydata.org/pandas-docs/stable/api.html
Input/output
Pickling
- pandas.read_pickle?(path[, compression]) - Load pickled pandas object (or any object) from file.
Flat file
- pandas.read_table(filepath_or_buffer, pathlib.Path, …) - Read general delimited file into DataFrame?.
- pandas.read_csv(filepath_or_buffer, pathlib.Path, …) - Read a comma-separated values (csv) file into DataFrame?.
- pandas.read_fwf?(filepath_or_buffer, pathlib.Path, …) - Read a table of fixed-width formatted lines into DataFrame?.
- pandas.read_msgpack?(path_or_buf[, encoding, iterator]) - (DEPRECATED) Load msgpack pandas object from the specified file path.
Clipboard
- pandas.read_clipboard?([sep]) - Read text from clipboard and pass to read_csv.
Excel
- pandas.read_excel?(io[, sheet_name, header, names, …]) - Read an Excel file into a pandas DataFrame?.
- pandas.ExcelFile?.parse(self[, sheet_name, header, …]) - Parse specified sheet(s) into a DataFrame?
- pandas.ExcelWriter?(path[, engine, date_format, …]) - Class for writing DataFrame? objects into excel sheets, default is to use xlwt for xls, openpyxl for xlsx.
JSON
- pandas.read_json?([path_or_buf, orient, typ, dtype, …]) - Convert a JSON string to pandas object.
- pandas.json_normalize?(data, List[Dict]], …) - Normalize semi-structured JSON data into a flat table.
- pandas.build_table_schema?(data[, index, …]) - Create a Table schema from data.
HTML
- pandas.read_html?(io[, match, flavor, header, …]) - Read HTML tables into a list of DataFrame? objects.
HDFStore: PyTables (HDF5)
- pandas.read_hdf?(path_or_buf[, key, mode]) - Read from the store, close it if we opened it.
- pandas.HDFStore.put?(self, key, value[, format, append]) - Store object in HDFStore
- pandas.HDFStore.append?(self, key, value[, format, …]) - Append to Table in file.
- pandas.HDFStore.get?(self, key) - Retrieve pandas object stored in file
- pandas.HDFStore.select?(self, key[, where, start, …]) - Retrieve pandas object stored in file, optionally based on where criteria
- pandas.HDFStore.info?(self) - Print detailed information on the store.
- pandas.HDFStore.keys?(self) - Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore.
- pandas.HDFStore.groups?(self) - return a list of all the top-level nodes (that are not themselves a pandas storage object)
- pandas.HDFStore.walk?(self[, where]) - Walk the pytables group hierarchy for pandas objects
Feather
- pandas.read_feather?(path[, columns, use_threads]) - Load a feather-format object from the file path.
Parquet
- pandas.read_parquet?(path[, engine, columns]) - Load a parquet object from the file path, returning a DataFrame?.
SAS
- pandas.read_sas?(filepath_or_buffer[, format, …]) - Read SAS files stored as either XPORT or SAS7BDAT format files.
SQL
- pandas.read_sql_table?(table_name, con[, schema, …]) - Read SQL database table into a DataFrame?.
- pandas.read_sql_query?(sql, con[, index_col, …]) - Read SQL query into a DataFrame?.
- pandas.read_sql?(sql, con[, index_col, …]) - Read SQL query or database table into a DataFrame?.
Google BigQuery
STATA
- pandas.read_stata?(filepath_or_buffer[, …]) - Read Stata file into DataFrame?.
- pandas.StataReader.data?(self, \*\*kwargs) - (DEPRECATED) Read observations from Stata file, converting them into a dataframe
- pandas.StataReader.data_label? - Return data label of Stata file.
- pandas.StataReader.value_labels?(self) - Return a dict, associating each variable name a dict, associating each value its corresponding label.
- pandas.StataReader.variable_labels?(self) - Return variable labels as a dict, associating each variable name with corresponding label.
- pandas.StataWriter.write_file?(self) -
