# Datapad: A Fluent API for Exploratory Data Analysis in Python¶

Datapad is a Python library for processing sequence and stream data using a Fluent style API. Data scientists and researchers use it as a lightweight toolset to efficiently explore datasets and to massage data for modeling tasks.

It can be viewed as a combination of syntatic sugar for Python’s itertools module and supercharged tooling for working with Fields and Structured sequences.

Datapad optimizes for developer happiness by providing an intuitive, consistent, and minimal API to manipulate a wide variety of data.

## Exploratory data analysis with Datapad¶

See what you can do with datapad in the examples below.

**Count all unique items in a sequence:**

```
>>> import datapad as dp
>>> data = ['a', 'b', 'b', 'c', 'c', 'c']
>>> seq = dp.Sequence(data)
>>> seq.count(distinct=True) \
... .collect()
[('a', 1),
('b', 2),
('c', 3)]
```

**Transform individual fields in a sequence:**

```
>>> import datapad as dp
>>> import datapad.fields as F
>>> data = [
... {'a': 1, 'b': 2},
... {'a': 4, 'b': 4},
... {'a': 5, 'b': 7}
... ]
>>> seq = dp.Sequence(data)
>>> seq.map(F.apply('a', lambda x: x*2)) \
... .map(F.apply('b', lambda x: x*3)) \
... .collect()
[{'a': 2, 'b': 6},
{'a': 8, 'b': 12},
{'a': 10, 'b': 21}]
```

**Chain together multiple transforms for the elements of a sequence:**

```
>>> import datapad as dp
>>> data = ['a', 'b', 'b', 'c', 'c', 'c']
>>> seq = dp.Sequence(data)
>>> seq.distinct() \
... .map(lambda x: x+'z') \
... .map(lambda x: (x, len(x))) \
... .collect()
[('az', 2),
('bz', 2),
('cz', 2)]
```

For a more in-depth overview, see the “Getting Started” guide.