Python library: pandas

Module members

Read functions

Panda's read_…() functions read data of various formats and create a DataFrame.

`read_clipboard()`
`read_csv()`
`read_excel()`	Reads an Excel worksheet from an Excel workbook. See also pandas' `ExcelFile` and `ExcelWriter` objects
`read_feather()`	See also the `feather-library` package.
`read_fwf()`	Fixed-width formatted lines
`read_gbq()`	Google Big Query. Requires the `pandas_gbq` package.
`read_hdf()`	Hierarchical Data Format (HDF). See also the `HDFStore` object.
`read_html()`
`read_json()`
`read_orc()`	Optimized Row Columnar (ORC) files. See also `pyarrow.orc`.
`read_parquet()`	Apache Parquet.
`read_pickle()`	Reads «pickled» data (see the `pickle` module)
`read_sas()`	SAS formats XPORT and SAS7BDAT.
`read_spss()`	Statistical Package for Social Sciences
`read_sql()`	A convenience wrapper around `read_sql_table()` and `read_sql_query()`.
`read_sql_query()`
`read_sql_table()`	Returns the content of a table from an `SQLAlchemy` connectable as a data frame - does not support DBAPI connections.
`read_stata()`
`read_table()`
`read_xml()`

Some of these methods have a corresponding to_…() method in DataFrame such as to_excel() or to_clipboard etc.

Other members

`annotations`
`api`	a module
`array()`
`arrays`	a module
`ArrowDtype`
`bdate_range()`
`BooleanDtype`
`Categorical`
`CategoricalDtype`
`CategoricalIndex`
`compat`	a module
`concat()`
`_config`	a module
`core`	a module
`crosstab()`
`cut()`
`DataFrame`	A two dimensional data structure. Compare with the one dimensional `Series` object.
`DateOffset`
`date_range()`	Returns a `DatetimeIndex` object that represents equally spaced points in time (for example `pd.date_range('2022-01-01', periods=365)` or `pd.date_range('2022-08-28', periods=24, freq='H')`). Compare with `bdate_range`.
`DatetimeIndex`
`DatetimeTZDtype`
`describe_option`
`errors`	a module
`eval()`
`ExcelFile`
`ExcelWriter`
`factorize()`
`Flags`
`Float32Dtype`
`Float64Dtype`
`Float64Index`	deprecated in favor of `pandas.Index`
`from_dummies()`
`get_dummies()`
`get_option`
`Grouper`
`HDFStore`
`Index`
`IndexSlice`
`infer_freq()`
`Int16Dtype`
`Int32Dtype`
`Int64Dtype`
`Int64Index`	deprecated in favor of `pandas.Index`.
`Int8Dtype`
`Interval`
`IntervalDtype`
`IntervalIndex`
`interval_range()`
`io`	a a module
`isna()`
`isnull()`
`_is_numpy_dev`
`json_normalize()`
`_libs`	a module
`lreshape()`
`melt()`
`merge()`
`merge_asof()`
`merge_ordered()`
`MultiIndex`
`NA`
`NamedAgg`
`NaT`
`notna()`
`notnull()`
`offsets`	a module
`option_context`
`options`
`pandas`	a module
`Period`
`PeriodDtype`
`PeriodIndex`
`period_range()`
`pivot()`
`pivot_table()`
`plotting`	a module
`qcut()`
`RangeIndex`
`reset_option`
`Series`	A one dimensional labeled array whose elements can be any data type. Compare with the two dimensional `DataFrame`.
`set_eng_float_format()`
`set_option`
`show_versions()`
`SparseDtype`
`StringDtype`
`test()`
`_testing`	a module
`testing`	a a module
`Timedelta`
`TimedeltaIndex`
`timedelta_range()`
`Timestamp`
`to_datetime()`
`to_numeric()`
`to_pickle()`
`to_timedelta()`
`tseries`	a module
`_typing`	a module
`UInt16Dtype`
`UInt32Dtype`
`UInt64Dtype`
`UInt64Index`	deprecated in favor of `pandas.Index`
`UInt8Dtype`
`unique()`
`util`	a module
`value_counts()`
`_version`	a module
`wide_to_long()`

qcut

import pandas

q =  pandas.qcut ( [ 1, 2, 2, 4, 5, 9, 10, 12, 15, 16, 16, 18, 20, 23 ], 4)

print(type(q))
# <class 'pandas.core.arrays.categorical.Categorical'>

print(q)
#
# Length: 14
# Categories (4, interval[float64, right]): [(0.999, 4.25] < (4.25, 11.0] < (11.0, 16.0] <
#                                           (16.0, 23.0]]

Github repository about-python, path: /libraries/pandas/qcut.py

Series

import pandas

a = pandas.Series( [  42 , -11 ,  19 ])
b = pandas.Series( ['foo','bar','baz'])

print(type (a)) # <class 'pandas.core.series.Series'>

print(a)
# 0    42
# 1   -11
# 2    19

print(b)
# 0    foo
# 1    bar
# 2    baz

Github repository about-python, path: /libraries/pandas/Series/Series.py

Ibis	Runs on SQLAlchemy and has an SQL compiler to translate its pandas-like API to SQL statements.
PySpark	Uses Spark as backend
cuDF	A GPU dataframe library built on top of Apache Arrow and RAPIDS.
Vaex	Uses hdf5
Koalas	Is built on top of PySpark
Dask
Modin
Turi Create

Python library: pandas

Module members

Read functions

Other members

qcut

Series

See also