Importing data using pandas
Pandas allows you to import data from a wide range of data sources directly into a dataframe. These can be static files, such as CSV, TSV, fixed width files, Microsoft Excel, JSON, SAS and SPSS files, as well as a range of popular databases, such as MySQL, PostgreSQL and Google BigQuery.
| Function | Purpose |
|---|---|
read_csv() |
Reads local or remote CSV (comma separated value) files. |
read_csv() |
Reads local or remote CSV (comma separated value) files. |
read_excel() |
Reads local or remote Microsoft Excel spreadsheet files. |
read_clipboard() |
Reads the local clipboard. |
read_html() |
Reads local or remote HTML files or web pages. |
read_fwf() |
Reads local or remote fixed width text files. |
read_excel() |
Reads OpenDocument format spreadsheets. |
read_hdf() |
Reads HDFStore HDF5 PyTable files. |
read_feather() |
Reads Apache Arrow Feather format files. |
read_parquet() |
Reads Apache Parquet files from Hadoop. |
read_orc() |
Reads Optimized Row Column (ORC) format files from Hive. |
read_msgpack() |
Reads MessagePack format files. |
read_stata() |
Reads files from the Stata statistics software package. |
read_sas() |
Reads files from the SAS statistics software package. |
read_spss() |
Reads files from the SPSS statistics software package. |
read_pickle() |
Reads files from the Python Pickle format. |
read_sql() |
Reads files in a variety of SQL dialects via SQLAlchemy. |
read_gbq() |
Reads data from Google Big Query. |
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