pandasticsearch 0.1.0

A Pandastic Elasticsearch client for data analyzing.

## Pandasticsearch

[](https://travis-ci.org/onesuper/pandasticsearch)

Pandasticsearch is an Elasticsearch client for data-analysis purpose.
It provides table-like access to Elasticsearch documents, similar
to the Python Pandas library and R DataFrames.

To install:

```
pip install pandasticsearch
# if you intent to export Pandas DataFrame
pip install pandasticsearch[pandas]
```

Elasticsearch is skilled in real-time indexing, search and data-analysis.
Pandasticsearch can convert the analysis results (e.g. multi-level nested aggregation)
into [Pandas](http://pandas.pydata.org) DataFrame objects for subsequent data analysis.


## Usage

### DataFrame API

A `DataFrame` object accesses Elasticsearch data with high level operations.
It is type-safe, easy-to-use and Pandas-flavored.

```python
# Create a DataFrame object
from pandasticsearch import DataFrame
df = DataFrame.from_es('http://localhost:9200', index='people')

# Print the schema(mapping) of the index
df.print_schema()
# company
# |-- employee
# |-- name: {'index': 'not_analyzed', 'type': 'string'}
# |-- age: {'type': 'integer'}
# |-- gender: {'index': 'not_analyzed', 'type': 'string'}

# Inspect the columns
df.columns
#['name', 'age', 'gender']

# Get the column
df.name
# Column('name')

# Filter
df.filter(df.age < 13).collect()
# [Row(age=12,gender='female',name='Alice'), Row(age=11,gender='male',name='Bob')]

# Project
df.filter(df.age < 25).select('name', 'age').collect()
# [Row(age=12,name='Alice'), Row(age=11,name='Bob'), Row(age=13,name='Leo')]

# Print the rows into console
df.filter(df.age < 25).select('name').show(3)
# +------+
# | name |
# +------+
# | Alice|
# | Bob |
# | Leo |
# +------+

# Sort
df.sort(df.age.asc).select('name', 'age').collect()
#[Row(age=11,name='Bob'), Row(age=12,name='Alice'), Row(age=13,name='Leo')]

# Aggregate
df[df.gender == 'male'].agg(df.age.avg).collect()
# [Row(avg(age)=12)]

# Groupby
df.groupby('gender').collect()
# [Row(doc_count=1), Row(doc_count=2)]

# Groupby and then aggregate
df.groupby('gender').agg(df.age.max).collect()
# [Row(doc_count=1, max(age)=12), Row(doc_count=2, max(age)=13)]

# Convert to Pandas object for subsequent analysis
df[df.gender == 'male'].agg(df.age.avg).to_pandas()
# avg(age)
# 0 12

```



## Use with Another Python Client

Pandasticsearch can also be used with another full featured Python client:

* [elasticsearch-py](https://github.com/elastic/elasticsearch-py) (Official)
* [Elasticsearch-SQL](https://github.com/NLPchina/elasticsearch-sql)
* [pyelasticsearch](https://github.com/pyelasticsearch/pyelasticsearch)
* [pyes](https://github.com/aparo/pyes)


### Build query

```Python
from pandasticsearch import DataFrame
body = df[df['gender'] == 'male'].agg(df['age'].avg).to_dict()

from elasticsearch import Elasticsearch
result_dict = es.search(index="recruit", body=body)
```

### Parse result

```python
from elasticsearch import Elasticsearch
es = Elasticsearch('http://localhost:9200')
result_dict = es.search(index="recruit", body={"query": {"match_all": {}}})

from pandasticsearch import Select
Select.from_dict(result_dict).to_pandas()
```


## Related Articles

* [Spark and Elasticsearch for real-time data analysis](https://spark-summit.org/2015-east/wp-content/uploads/2015/03/SSE15-35-Leau.pdf)


## LICENSE

MIT