Create an array.

Return a fixed frequency DatetimeIndex with business day as the default.

Concatenate pandas objects along a particular axis.

Compute a simple cross tabulation of two (or more) factors.

Bin values into discrete intervals.

Return a fixed frequency DatetimeIndex.

eval(expr, parser='pandas', engine=None, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False) See https://pandas.pydata.org/docs/reference/api/pandas.eval.html#pandas.eval go:linkname Eval py.eval.

Encode the object as an enumerated type or categorical variable.

Create a categorical “DataFrame“ from a “DataFrame“ of dummy variables.

Convert categorical variable into dummy/indicator variables.

Infer the most likely frequency given the input index.

Return a fixed frequency IntervalIndex.

Detect missing values for an array-like object.

Detect missing values for an array-like object.

Normalize semi-structured JSON data into a flat table.

Reshape wide-format data to long.

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

Merge DataFrame or named Series objects with a database-style join.

Perform a merge by key distance.

Perform a merge for ordered data with optional filling/interpolation.

Detect non-missing values for an array-like object.

Detect non-missing values for an array-like object.

Return a fixed frequency PeriodIndex.

Return reshaped DataFrame organized by given index / column values.

Create a spreadsheet-style pivot table as a DataFrame.

Quantile-based discretization function.

Read text from clipboard and pass to :func:`~pandas.read_csv`.

See https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html go:linkname ReadCsv py.read_csv.

read_excel(io, sheet_name=0, *, header=0, names=None, index_col=None, usecols=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=_NoDefault.no_default, date_format=None, thousands=None, decimal='.', comment=None, skipfooter=0, storage_options=None, dtype_backend=_NoDefault.no_default, engine_kwargs=None) See https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html go:linkname ReadExcel py.read_excel.

Load a feather-format object from the file path.

Read a table of fixed-width formatted lines into DataFrame.

Load data from Google BigQuery.

Read from the store, close it if we opened it.

Read HTML tables into a “list“ of “DataFrame“ objects.

Convert a JSON string to pandas object.

Load an ORC object from the file path, returning a DataFrame.

Load a parquet object from the file path, returning a DataFrame.

Load pickled pandas object (or any object) from file.

Read SAS files stored as either XPORT or SAS7BDAT format files.

Load an SPSS file from the file path, returning a DataFrame.

Read SQL query or database table into a DataFrame.

Read SQL query into a DataFrame.

Read SQL database table into a DataFrame.

Read Stata file into DataFrame.

Read general delimited file into DataFrame.

Read XML document into a :class:`~pandas.DataFrame` object.

Format float representation in DataFrame with SI notation.

show_versions(as_json=False) See https://pandas.pydata.org/docs/reference/api/pandas.show_versions.html go:linkname ShowVersions py.show_versions.

Run the pandas test suite using pytest.

Return a fixed frequency TimedeltaIndex with day as the default.

Convert argument to datetime.

Convert argument to a numeric type.

Pickle (serialize) object to file.

Convert argument to timedelta.

Return unique values based on a hash table.

Compute a histogram of the counts of non-null values.

Unpivot a DataFrame from wide to long format.