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• Chart visualization

  ...s.cumsum() In [6]: ts.plot(); If the index consists of dates, it calls gcf().autofmt_xdate() to try to format the x-axis nicely as per above. On DataFrame, plot() is a convenience to plot all of the columns with labels: In [7]: df = pd....

• Essential basic functionality

  ...es and underlying data pandas objects have a number of attributes enabling you to access the metadata shape: gives the axis dimensions of the object, consistent with ndarray Axis labels Series: index (only axis) DataFrame: index (rows) an...

• Merge, join, concatenate and compare

  ...s or DataFrame objects with SQL-style joining merge_ordered(): Combine two Series or DataFrame objects along an ordered axis merge_asof(): Combine two Series or DataFrame objects by near instead of exact matching keys Series.compare() and D...

• MultiIndex / advanced indexing

  ...ndex) object The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex as an array of tuples where each tuple is unique. A MultiInde...

• Version 0.21.0 (October 27, 2017)

  ...ttempting to create columns Method drop now also accepts index/columns keywords Methods rename, reindex now also accept axis keyword CategoricalDtype for specifying categoricals GroupBy objects now have a pipe method Categorical.rename_cate...

• pandas.DataFrame.rename_axis (Python method, in pandas.DataFrame.rename_axis)
• pandas.DataFrame.set_axis (Python method, in pandas.DataFrame.set_axis)
• pandas.Series.rename_axis (Python method, in pandas.Series.rename_axis)
• pandas.Series.set_axis (Python method, in pandas.Series.set_axis)
• 10 minutes to pandas

  ...1.478427 D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988 DataFrame.sort_index() sorts by an axis: In [22]: df.sort_index(axis=1, ascending=False) Out[22]: D C B A 2013-...

• Categorical data

  ...pd.Categorical([1, 2, 3, 2]), .....: } .....: ) .....: In [258]: df.apply(lambda row: type(row["cats"]), axis=1) Out[258]: 0 <class 'int'> 1 <class 'int'> 2 <class 'int'> 3 <class 'int'> dtype: object In [259]:...

• Comparison with R / R libraries

  ...2) df[['col1', 'col2']] select(df, col1:col3) df.loc[:, 'col1':'col3'] select(df, -(col1:col3)) df.drop(cols_to_drop, axis=1) but see [1] distinct(select(df, col1)) df[['col1']].drop_duplicates() distinct(select(df, col1, col2)) df[['co...

• Comparison with SAS

  ...16.78 3.00 Female No Thur Dinner 2 8.390 [244 rows x 8 columns] In [4]: tips = tips.drop("new_bill", axis=1) Filtering Filtering in SAS is done with an if or where statement, on one or more columns. data tips; set...

• Comparison with spreadsheets

  ...16.78 3.00 Female No Thur Dinner 2 8.390 [244 rows x 8 columns] In [12]: tips = tips.drop("new_bill", axis=1) Note that we aren’t having to tell it to do that subtraction cell-by-cell — pandas handles that for us. See ho...

• Comparison with Stata

  ...16.78 3.00 Female No Thur Dinner 2 8.390 [244 rows x 8 columns] In [12]: tips = tips.drop("new_bill", axis=1) Filtering Filtering in Stata is done with an if clause on one or more columns. list if total_bill > 10 Dat...

• Contributing to the code base

  ...ncepts like “path-like”, “array-like”, “numeric”, etc… and can also hold aliases for commonly appearing parameters like axis. Development of this module is active so be sure to refer to the source for the most up to date list of available t...

• Cookbook

  ...11 six 22 BB one 33 two 44 six 55 To take the cross section of the 1st level and 1st axis the index: # Note : level and axis are optional, and default to zero In [83]: df.xs("BB", level=0, axis=0) Out[83]:...

• Copy-on-Write (CoW)

  ...hanism was added to methods that don’t require a copy of the underlying data. Popular examples are DataFrame.drop() for axis=1 and DataFrame.rename(). These methods return views when Copy-on-Write is enabled, which provides a significant pe...

• Duplicate Labels

  ...--> 1 s1.reindex(["a", "b", "c"]) File ~/work/pandas/pandas/pandas/core/series.py:5153, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance) 5136 @doc( 5137 NDFrame.reindex, # type: ignore[has-...

• Enhancing performance

  ...r result by using DataFrame.apply() (row-wise): In [5]: %timeit df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]), axis=1) 84 ms +- 1.01 ms per loop (mean +- std. dev. of 7 runs, 10 loops each) Let’s take a look and see where the tim...

• Frequently Asked Questions (FAQ)

  ...se e False dtype: bool In [20]: s.isin([2]).any() Out[20]: True For DataFrame, likewise, in applies to the column axis, testing for membership in the list of column names. Mutating with User Defined Function (UDF) methods This sect...

• Group by: split-apply-combine

  ...ributes The groups attribute is a dictionary whose keys are the computed unique groups and corresponding values are the axis labels belonging to each group. In the above example we have: In [33]: df.groupby("A").groups Out[33]: {'bar': [1,...

• How to combine data from multiple tables

  ...ables with a similar structure, in a single table. In [8]: air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0) In [9]: air_quality.head() Out[9]: date.utc location parameter value 0 2019-06-18 06:00:...

• Indexing and selecting data

  Indexing and selecting data The axis labeling information in pandas objects serves many purposes: Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactiv...

• Internals

  ...s of pandas itself. Indexing In pandas there are a few objects implemented which can serve as valid containers for the axis labels: Index: the generic “ordered set” object, an ndarray of object dtype assuming nothing about its contents. T...

• Intro to data structures

  ...w of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects. To get started, import NumPy and load pandas into your nam...

• IO tools (text, CSV, HDF5, …)

  ...ndom.randn(5, 2), columns=list("AB")) In [246]: dfd["date"] = pd.Timestamp("20130101") In [247]: dfd = dfd.sort_index(axis=1, ascending=False) In [248]: json = dfd.to_json(date_format="iso") In [249]: json Out[249]: '{"date":{"0":"2013-...

• Nullable integer data type

  ...object dtype: object These dtypes can be merged, reshaped & casted. In [21]: pd.concat([df[["A"]], df[["B", "C"]]], axis=1).dtypes Out[21]: A Int64 B int64 C object dtype: object In [22]: df["A"].astype(float) Out[22]: 0...

• Package overview
• pandas docstring guide

  ...imal or None str or list of str If None is one of the accepted values, it always needs to be the last in the list. For axis, the convention is to use something like: axis : {0 or ‘index’, 1 or ‘columns’, None}, default None Section 4:...

• pandas.api.extensions.ExtensionArray._pad_or_backfill
• pandas.api.extensions.ExtensionArray._reduce
• pandas.api.extensions.ExtensionArray.fillna
• pandas.api.extensions.ExtensionArray.interpolate
• pandas.api.extensions.ExtensionArray.repeat
• pandas.api.extensions.ExtensionArray.shift
• pandas.api.extensions.ExtensionArray.take
• pandas.concat
• pandas.core.groupby.DataFrameGroupBy.agg
• pandas.core.groupby.DataFrameGroupBy.aggregate
• pandas.core.groupby.DataFrameGroupBy.boxplot
• pandas.core.groupby.DataFrameGroupBy.corrwith
• pandas.core.groupby.DataFrameGroupBy.cummax
• pandas.core.groupby.DataFrameGroupBy.cummin
• pandas.core.groupby.DataFrameGroupBy.cumprod
• pandas.core.groupby.DataFrameGroupBy.cumsum
• pandas.core.groupby.DataFrameGroupBy.diff
• pandas.core.groupby.DataFrameGroupBy.fillna
• pandas.core.groupby.DataFrameGroupBy.hist
• pandas.core.groupby.DataFrameGroupBy.idxmax
• pandas.core.groupby.DataFrameGroupBy.idxmin
• pandas.core.groupby.DataFrameGroupBy.pct_change
• pandas.core.groupby.DataFrameGroupBy.plot
• pandas.core.groupby.DataFrameGroupBy.rank
• pandas.core.groupby.DataFrameGroupBy.rolling
• pandas.core.groupby.DataFrameGroupBy.shift
• pandas.core.groupby.DataFrameGroupBy.skew
• pandas.core.groupby.DataFrameGroupBy.take
• pandas.core.groupby.DataFrameGroupBy.transform
• pandas.core.groupby.SeriesGroupBy.agg
• pandas.core.groupby.SeriesGroupBy.aggregate
• pandas.core.groupby.SeriesGroupBy.cummax
• pandas.core.groupby.SeriesGroupBy.cummin
• pandas.core.groupby.SeriesGroupBy.cumprod
• pandas.core.groupby.SeriesGroupBy.cumsum
• pandas.core.groupby.SeriesGroupBy.diff
• pandas.core.groupby.SeriesGroupBy.fillna
• pandas.core.groupby.SeriesGroupBy.hist
• pandas.core.groupby.SeriesGroupBy.idxmax
• pandas.core.groupby.SeriesGroupBy.idxmin
• pandas.core.groupby.SeriesGroupBy.pct_change
• pandas.core.groupby.SeriesGroupBy.plot
• pandas.core.groupby.SeriesGroupBy.rank
• pandas.core.groupby.SeriesGroupBy.rolling
• pandas.core.groupby.SeriesGroupBy.shift
• pandas.core.groupby.SeriesGroupBy.skew
• pandas.core.groupby.SeriesGroupBy.take
• pandas.core.groupby.SeriesGroupBy.transform
• pandas.core.resample.Resampler.aggregate
• pandas.core.resample.Resampler.apply
• pandas.core.resample.Resampler.interpolate
• pandas.core.window.expanding.Expanding.aggregate
• pandas.core.window.rolling.Rolling.aggregate
• pandas.cut
• pandas.DataFrame.__add__
• pandas.DataFrame.__iter__
• pandas.DataFrame.add
• pandas.DataFrame.add_prefix
• pandas.DataFrame.add_suffix
• pandas.DataFrame.agg
• pandas.DataFrame.aggregate
• pandas.DataFrame.align
• pandas.DataFrame.all
• pandas.DataFrame.any
• pandas.DataFrame.apply
• pandas.DataFrame.applymap
• pandas.DataFrame.at_time
• pandas.DataFrame.axes
• pandas.DataFrame.backfill
• pandas.DataFrame.between_time
• pandas.DataFrame.bfill
• pandas.DataFrame.boxplot
• pandas.DataFrame.clip
• pandas.DataFrame.combine
• pandas.DataFrame.compare
• pandas.DataFrame.corrwith
• pandas.DataFrame.count
• pandas.DataFrame.cummax
• pandas.DataFrame.cummin
• pandas.DataFrame.cumprod
• pandas.DataFrame.cumsum
• pandas.DataFrame.diff
• pandas.DataFrame.div
• pandas.DataFrame.drop
• pandas.DataFrame.drop_duplicates
• pandas.DataFrame.droplevel
• pandas.DataFrame.dropna
• pandas.DataFrame.empty
• pandas.DataFrame.eq
• pandas.DataFrame.ewm
• pandas.DataFrame.expanding
• pandas.DataFrame.ffill
• pandas.DataFrame.fillna
• pandas.DataFrame.filter
• pandas.DataFrame.floordiv
• pandas.DataFrame.ge