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readme updated #92

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Sep 23, 2024
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49 changes: 25 additions & 24 deletions .idea/workspace.xml
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30 changes: 16 additions & 14 deletions readme.md
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Expand Up @@ -26,31 +26,33 @@ maintainable code, e.g. this Pandas code:

```python
# Pandas: calculate the total revenue of all hybrid Audi cars in September 2024
value = df.loc[(df['make'] == 'Audi') &
(df['engine'] == 'hybrid') &
(df['date'] >= '2024-09-01') & (df['date'] <= '2024-09-30'),
'price'].sum()
value = df.loc[
(df['make'] == 'Audi') &
(df['engine'] == 'hybrid') &
(df['date'] >= '2024-09-01') & (df['date'] <= '2024-09-30'),
'revenue'
].sum()
```

can turn into this equivalent CubedPandas code:

```python
# ...the same with CubedPandas:
value = df.cubed.make.Audi.engine.hybrid.date.september_2024.price
value = df.cubed.make.Audi.engine.hybrid.date.september_2024.revenue

# ...or even shorter
value = df.cubed.Audi.hybrid.sep_2024.price
# ...or maybe even shorter:
value = df.cubed.Audi.hybrid.sep_2024
```

CubedPandas offers a fluent interface based on the data available in the underlying DataFrame
for easy filtering, navigation and analysis of Pandas dataframes.
CubedPandas offers a fluent interface based on the data available in the underlying DataFrame.
So, filtering, navigation and analysis of Pandas dataframes becomes more intuitive, more readable and more fun.

CubedPandas neither duplicates data nor modifies the underlying DataFrame, and it introduces no
performance penalty. In fact, it can significantly speed up your data processing.
CubedPandas neither duplicates data nor modifies the underlying DataFrame, and it introduces
no performance penalty. In fact, it can sometimes significantly speed up your data processing.

[Jupyter notebooks](https://jupyter.org) is the perfect habitat for CubedPandas.
For further information, please visit the [CubedPandas Documentation](https://zeutschler.github.io/cubedpandas/)
or try the included samples.
[Jupyter notebooks](https://jupyter.org) is the perfect habitat for CubedPandas. For further information,
please visit the [CubedPandas Documentation](https://zeutschler.github.io/cubedpandas/)
or try some of the included samples.

### Getting Started

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