cubedpandas 0.2.23

OLAP comfort meets Pandas power!

CubedPandas

OLAP comfort meets Pandas power!

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Remark: *CubedPandas is in an early stage of development. Features are likely subject to change. Anyhow, your Ideas, Issues and Feedback are very welcome!

CubedPandas aims to offer an easy, fast & natural approach to work with Pandas dataframes. To achieve this, CubedPandas wraps your dataframe into a lightweight, virtual multi-dimensional OLAP cube, that can be analysed and used in a very natural, compact and readable and natural manner.

For novice users, CubedPandas can be a great help to get started with Pandas, as it hides some of the complexity and verbosity of Pandas dataframes. For experienced users, CubedPandas can be a great productivity booster, as it allows to write more compact, readable and maintainable code. Just to give you a first idea, this Pandas code:

value = df.loc[(df['make'] == 'Audi') & (df['engine'] == 'hybrid'), 'price'].sum()

can be turned into this CubedPandas code:

value = df.cubed.Audi.hybrid.price

As CubedPandas does not duplicate data or modifies the underlying dataframe and does not add any performance penalty - but in some cases can even boost performance by factors - it can be used in production use cases without any concerns and should be of great help in many use cases.

And in Jupyter notebooks, CubedPandas will really start to shine. For further information, please visit the CubedPandas Documentation.

Installation and Getting Started

After installing CubedPandas using pip install cubedpandas, you are ready to go. 'Cubing' a Pandas dataframe is as simple as this:

import pandas as pd
from cubedpandas import cubed

# Create a dataframe with some sales data
df = pd.DataFrame({"product":  ["Apple",  "Pear",   "Banana", "Apple",  "Pear",   "Banana"],
                   "channel":  ["Online", "Online", "Online", "Retail", "Retail", "Retail"],
                   "customer": ["Peter",  "Peter",  "Paul",   "Paul",   "Mary",   "Mary"  ],
                   "mailing":  [True,     False,    True,     False,    True,     False   ],
                   "revenue":  [100,      150,      300,      200,      250,      350     ],
                   "cost":     [50,       90,       150,      100,      150,      175     ]})

cdf = cubed(df)  # Wrapp your dataframe into a cube and start cubing!

CubedPandas automatically infers a multi-dimensional schema from your Pandas dataframe which defines a virtual Cube over the dataframe. By default, numeric columns of the dataframe are considered as Measures - the numeric values to analyse & aggregate - all other columns are considered as Dimensions - to filter, navigate and view the data. The individual values in a dimension column are called the Members of the dimension. In the example above, column channel becomes a dimension with the two members Online and Retail, revenue and cost are the measures.

Although rarely required, you can also define your own schema. Schemas are quite powerful and flexible, as they will allow you to define dimensions and measures, aliases and (planned for upcoming releases) also custom aggregations, business logic, number formating, linked cubes and much more.

Give me a Context, so I will give you a Cell!

One key feature of CubePandas is an easy & intuitive access to individual Data Cells in multi-dimensional data space. To do so, you'll need to define a multi-dimensional Context and CubedPandas will evaluate, aggregate (sum by default) and return the requested value from the underlying dataframe.

Context objects behave like numbers (float, int), so you can use them directly in arithmetic operations. In the following examples, all addresses will refer to the exactly same rows from the dataframe and thereby all return the same value of 100.

# Let Pandas set the scene...
a = df.loc[(df["product"] == "Apple") & (df["channel"] == "Online") & (df["customer"] == "Peter"), "revenue"].sum()

# Can we do better with CubedPandas? 
b = cdf.product["Apple"].channel["Online"].customer["Peter"].revenue  # explicit, readable, flexible and fast  
c = cdf.product.Apple.channel.Online.customer.Peter.revenue  # identical, if member names are Python-compliant

# If there are no ambiguities in your data - can be easily checked - then you can use this shorthand forms:
d = cdf["Online", "Apple", "Peter", "revenue"]
e = cdf.Online.Apple.Peter.revenue
f = cdf.Online.Apple.Peter  # if 'revenue' is the default measure (it is), so it can be omitted

assert a == b == c == d == e == f == 100

Context objects act as filters on the underlying dataframe. So you can use also CubedPandas for fast and easy filtering only, e.g. like this:

df = df.cubed.product["Apple"].channel["Online"].df

For further information, samples and a complete feature list as well as valuable tips and tricks, please visit the CubedPandas Documentation.

Your feedback, ideas and support are very welcome!

Please help improve and extend CubedPandas with your feedback & ideas and use the CubedPandas GitHub Issues to request new features and report bugs. For general questions, discussions and feedback, please use the CubedPandas GitHub Discussions.

If you have fallen in love with CubedPandas or find it otherwise valuable, please consider to become a sponsor of the CubedPandas project so we can push the project forward faster and make CubePandas even more awesome.

...happy cubing!