Input/Output — pandas 0.24.0rc1 documentation (original) (raw)
Pickling¶
| read_pickle(path[, compression]) | Load pickled pandas object (or any object) from file. |
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Flat File¶
| read_table(filepath_or_buffer[, sep, …]) | (DEPRECATED) Read general delimited file into DataFrame. |
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| 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. |
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Excel¶
| read_excel(io[, sheet_name, header, names, …]) | Read an Excel file into a pandas DataFrame. |
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| 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. |
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JSON¶
| read_json([path_or_buf, orient, typ, dtype, …]) | Convert a JSON string to pandas object. |
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| json_normalize(data[, record_path, meta, …]) | Normalize semi-structured JSON data into a flat table. |
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| 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. |
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HDFStore: PyTables (HDF5)¶
| read_hdf(path_or_buf[, key, mode]) | Read from the store, close it if we opened it. |
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| 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 |
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Parquet¶
| read_parquet(path[, engine, columns]) | Load a parquet object from the file path, returning a DataFrame. |
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SAS¶
| read_sas(filepath_or_buffer[, format, …]) | Read SAS files stored as either XPORT or SAS7BDAT format files. |
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SQL¶
| read_sql_table(table_name, con[, schema, …]) | Read SQL database table into a DataFrame. |
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| 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. |
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