third_codes/eland
1
0
Fork 0
mirror of https://github.com/elastic/eland.git synced 2025-07-11 00:02:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

Introduction of eland.Series - big refactor

Browse Source 
Creation of eland.NDFrame as base class for DataFrame and Series
This commit is contained in:
Stephen Dodson 2019-07-01 18:41:56 +00:00
parent 582d744848
commit 30df901fce
7 changed files with 816 additions and 326 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
eland/__init__.py
View File

@ -1,5 +1,7 @@
from .utils import * from .utils import *
from .dataframe import *
from .client import * from .client import *
from .mappings import * from .ndframe import *
from .index import * from .index import *
from .mappings import *
from .dataframe import *
from .series import *

357
eland/dataframe.py
View File

@ -26,18 +26,18 @@ only Elasticsearch aggregatable fields can be aggregated or grouped.
import sys import sys
import pandas as pd import pandas as pd
from elasticsearch_dsl import Search
from pandas.compat import StringIO
from pandas.core import common as com
from pandas.io.common import _expand_user, _stringify_path
from pandas.io.formats import format as fmt from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing from pandas.io.formats.printing import pprint_thing
from pandas.compat import StringIO
from pandas.io.common import _expand_user, _stringify_path
from pandas.io.formats import console from pandas.io.formats import console
import eland as ed from eland import NDFrame
from eland import Index
class DataFrame(): class DataFrame(NDFrame):
""" """
pandas.DataFrame like API that proxies into Elasticsearch index(es). pandas.DataFrame like API that proxies into Elasticsearch index(es).
@ -49,9 +49,6 @@ class DataFrame():
index_pattern : str index_pattern : str
An Elasticsearch index pattern. This can contain wildcards (e.g. filebeat-*). An Elasticsearch index pattern. This can contain wildcards (e.g. filebeat-*).
operations: list of operation
A list of Elasticsearch analytics operations e.g. filter, aggregations etc.
See Also See Also
-------- --------
@ -83,229 +80,14 @@ class DataFrame():
index_pattern, index_pattern,
mappings=None, mappings=None,
index_field=None): index_field=None):
# python 3 syntax
self._client = ed.Client(client) super().__init__(client, index_pattern, mappings=mappings, index_field=index_field)
self._index_pattern = index_pattern
# Get and persist mappings, this allows us to correctly
# map returned types from Elasticsearch to pandas datatypes
if mappings is None:
self._mappings = ed.Mappings(self._client, self._index_pattern)
else:
self._mappings = mappings
self._index = ed.Index(index_field)
def _es_results_to_pandas(self, results):
"""
Parameters
----------
results: dict
Elasticsearch results from self.client.search
Returns
-------
df: pandas.DataFrame
_source values extracted from results and mapped to pandas DataFrame
dtypes are mapped via Mapping object
Notes
-----
Fields containing lists in Elasticsearch don't map easily to pandas.DataFrame
For example, an index with mapping:
```
"mappings" : {
"properties" : {
"group" : {
"type" : "keyword"
},
"user" : {
"type" : "nested",
"properties" : {
"first" : {
"type" : "keyword"
},
"last" : {
"type" : "keyword"
}
}
}
}
}
```
Adding a document:
```
"_source" : {
"group" : "amsterdam",
"user" : [
{
"first" : "John",
"last" : "Smith"
},
{
"first" : "Alice",
"last" : "White"
}
]
}
```
(https://www.elastic.co/guide/en/elasticsearch/reference/current/nested.html)
this would be transformed internally (in Elasticsearch) into a document that looks more like this:
```
{
"group" : "amsterdam",
"user.first" : [ "alice", "john" ],
"user.last" : [ "smith", "white" ]
}
```
When mapping this a pandas data frame we mimic this transformation.
Similarly, if a list is added to Elasticsearch:
```
PUT my_index/_doc/1
{
"list" : [
0, 1, 2
]
}
```
The mapping is:
```
"mappings" : {
"properties" : {
"user" : {
"type" : "long"
}
}
}
```
TODO - explain how lists are handled (https://www.elastic.co/guide/en/elasticsearch/reference/current/array.html)
TODO - an option here is to use Elasticsearch's multi-field matching instead of pandas treatment of lists (which isn't great)
NOTE - using this lists is generally not a good way to use this API
"""
def flatten_dict(y):
out = {}
def flatten(x, name=''):
# We flatten into source fields e.g. if type=geo_point
# location: {lat=52.38, lon=4.90}
if name == '':
is_source_field = False
pd_dtype = 'object'
else:
is_source_field, pd_dtype = self._mappings.source_field_pd_dtype(name[:-1])
if not is_source_field and type(x) is dict:
for a in x:
flatten(x[a], name + a + '.')
elif not is_source_field and type(x) is list:
for a in x:
flatten(a, name)
elif is_source_field == True: # only print source fields from mappings (TODO - not so efficient for large number of fields and filtered mapping)
field_name = name[:-1]
# Coerce types - for now just datetime
if pd_dtype == 'datetime64[ns]':
x = pd.to_datetime(x)
# Elasticsearch can have multiple values for a field. These are represented as lists, so
# create lists for this pivot (see notes above)
if field_name in out:
if type(out[field_name]) is not list:
l = [out[field_name]]
out[field_name] = l
out[field_name].append(x)
else:
out[field_name] = x
flatten(y)
return out
rows = []
index = []
for hit in results['hits']['hits']:
row = hit['_source']
# get index value - can be _id or can be field value in source
if self._index.is_source_field:
index_field = row[self._index.index_field]
else:
index_field = hit[self._index.index_field]
index.append(index_field)
# flatten row to map correctly to 2D DataFrame
rows.append(flatten_dict(row))
# Create pandas DataFrame
df = pd.DataFrame(data=rows, index=index)
# _source may not contain all columns in the mapping
# therefore, fill in missing columns
# (note this returns self.columns NOT IN df.columns)
missing_columns = list(set(self.columns) - set(df.columns))
for missing in missing_columns:
is_source_field, pd_dtype = self._mappings.source_field_pd_dtype(missing)
df[missing] = None
df[missing].astype(pd_dtype)
# Sort columns in mapping order
df = df[self.columns]
return df
def head(self, n=5): def head(self, n=5):
sort_params = self._index.sort_field + ":asc" return super()._head(n)
results = self._client.search(index=self._index_pattern, size=n, sort=sort_params)
return self._es_results_to_pandas(results)
def tail(self, n=5): def tail(self, n=5):
sort_params = self._index.sort_field + ":desc" return super()._tail(n)
results = self._client.search(index=self._index_pattern, size=n, sort=sort_params)
df = self._es_results_to_pandas(results)
# reverse order (index ascending)
return df.sort_index()
def describe(self):
numeric_source_fields = self._mappings.numeric_source_fields()
# for each field we compute:
# count, mean, std, min, 25%, 50%, 75%, max
search = Search(using=self._client, index=self._index_pattern).extra(size=0)
for field in numeric_source_fields:
search.aggs.metric('extended_stats_' + field, 'extended_stats', field=field)
search.aggs.metric('percentiles_' + field, 'percentiles', field=field)
response = search.execute()
results = {}
for field in numeric_source_fields:
values = []
values.append(response.aggregations['extended_stats_' + field]['count'])
values.append(response.aggregations['extended_stats_' + field]['avg'])
values.append(response.aggregations['extended_stats_' + field]['std_deviation'])
values.append(response.aggregations['extended_stats_' + field]['min'])
values.append(response.aggregations['percentiles_' + field]['values']['25.0'])
values.append(response.aggregations['percentiles_' + field]['values']['50.0'])
values.append(response.aggregations['percentiles_' + field]['values']['75.0'])
values.append(response.aggregations['extended_stats_' + field]['max'])
# if not None
if (values.count(None) < len(values)):
results[field] = values
df = pd.DataFrame(data=results, index=['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])
return df
def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
null_counts=None): null_counts=None):
@ -325,7 +107,7 @@ class DataFrame():
lines = [] lines = []
lines.append(str(type(self))) lines.append(str(type(self)))
lines.append(self.index_summary()) lines.append(self._index_summary())
if len(self.columns) == 0: if len(self.columns) == 0:
lines.append('Empty {name}'.format(name=type(self).__name__)) lines.append('Empty {name}'.format(name=type(self).__name__))
@ -437,18 +219,14 @@ class DataFrame():
@property @property
def columns(self): def columns(self):
return pd.Index(self._mappings.source_fields()) return super()._columns
@property
def index(self):
return self._index
def set_index(self, index_field): def set_index(self, index_field):
copy = self.copy() copy = self.copy()
copy._index = ed.Index(index_field) copy._index = Index(index_field)
return copy return copy
def index_summary(self): def _index_summary(self):
head = self.head(1).index[0] head = self.head(1).index[0]
tail = self.tail(1).index[0] tail = self.tail(1).index[0]
index_summary = ', %s to %s' % (pprint_thing(head), index_summary = ', %s to %s' % (pprint_thing(head),
@ -457,13 +235,6 @@ class DataFrame():
name = "Index" name = "Index"
return '%s: %s entries%s' % (name, len(self), index_summary) return '%s: %s entries%s' % (name, len(self), index_summary)
@property
def dtypes(self):
return self._mappings.dtypes()
def get_dtype_counts(self):
return self._mappings.get_dtype_counts()
def count(self): def count(self):
""" """
Count non-NA cells for each column (TODO row) Count non-NA cells for each column (TODO row)
@ -487,24 +258,9 @@ class DataFrame():
return count return count
def index_count(self): def describe(self):
""" return super()._describe()
Returns
-------
index_count: int
Count of docs where index_field exists
"""
exists_query = {"query": {"exists": {"field": self._index.index_field}}}
index_count = self._client.count(index=self._index_pattern, body=exists_query)
return index_count
def _filter_by_columns(self, columns):
# Return new eland.DataFrame with modified mappings
mappings = ed.Mappings(mappings=self._mappings, columns=columns)
return DataFrame(self._client, self._index_pattern, mappings=mappings)
def __getitem__(self, key): def __getitem__(self, key):
# NOTE: there is a difference between pandas here. # NOTE: there is a difference between pandas here.
@ -544,17 +300,14 @@ class DataFrame():
else: else:
raise TypeError('__getitem__ arguments invalid: [{0}]'.format(key)) raise TypeError('__getitem__ arguments invalid: [{0}]'.format(key))
return self._filter_by_columns(columns) mappings = self._filter_mappings(columns)
def __len__(self): # Return new eland.DataFrame with modified mappings
""" return DataFrame(self._client, self._index_pattern, mappings=mappings)
Returns length of info axis, but here we use the index.
"""
return self._client.count(index=self._index_pattern)
def copy(self): def copy(self):
# TODO - test and validate...may need deep copying # TODO - test and validate...may need deep copying
return ed.DataFrame(self._client, return DataFrame(self._client,
self._index_pattern, self._index_pattern,
self._mappings, self._mappings,
self._index) self._index)
@ -590,73 +343,37 @@ class DataFrame():
if max_rows == None: if max_rows == None:
max_rows = pd.get_option('display.max_rows') max_rows = pd.get_option('display.max_rows')
sdf = self.__fake_dataframe__(max_rows=max_rows+1) df = self._fake_head_tail_df(max_rows=max_rows+1)
_show_dimensions = show_dimensions
if buf is not None: if buf is not None:
_buf = _expand_user(_stringify_path(buf)) _buf = _expand_user(_stringify_path(buf))
else: else:
_buf = StringIO() _buf = StringIO()
sdf.to_string(buf=_buf, columns=columns, df.to_string(buf=_buf, columns=columns,
col_space=col_space, na_rep=na_rep, col_space=col_space, na_rep=na_rep,
formatters=formatters, formatters=formatters,
float_format=float_format, float_format=float_format,
sparsify=sparsify, justify=justify, sparsify=sparsify, justify=justify,
index_names=index_names, index_names=index_names,
header=header, index=index, header=header, index=index,
max_rows=max_rows, max_rows=max_rows,
max_cols=max_cols, max_cols=max_cols,
show_dimensions=False, # print this outside of this call show_dimensions=False, # print this outside of this call
decimal=decimal, decimal=decimal,
line_width=line_width) line_width=line_width)
if _show_dimensions: # Our fake dataframe has incorrect number of rows (max_rows*2+1) - write out
# the correct number of rows
if show_dimensions:
_buf.write("\n\n[{nrows} rows x {ncols} columns]" _buf.write("\n\n[{nrows} rows x {ncols} columns]"
.format(nrows=self.index_count(), ncols=len(self.columns))) .format(nrows=self._index_count(), ncols=len(self.columns)))
if buf is None: if buf is None:
result = _buf.getvalue() result = _buf.getvalue()
return result return result
def __fake_dataframe__(self, max_rows=1):
head_rows = int(max_rows / 2) + max_rows % 2
tail_rows = max_rows - head_rows
head = self.head(head_rows)
tail = self.tail(tail_rows)
num_rows = len(self)
if (num_rows > max_rows):
# If we have a lot of rows, create a SparseDataFrame and use
# pandas to_string logic
# NOTE: this sparse DataFrame can't be used as the middle
# section is all NaNs. However, it gives us potentially a nice way
# to use the pandas IO methods.
# TODO - if data is indexed by time series, return top/bottom of
# time series, rather than first max_rows items
"""
if tail_rows > 0:
locations = [0, num_rows - tail_rows]
lengths = [head_rows, tail_rows]
else:
locations = [0]
lengths = [head_rows]
sdf = pd.DataFrame({item: pd.SparseArray(data=head[item],
sparse_index=
BlockIndex(
num_rows, locations, lengths))
for item in self.columns})
"""
return pd.concat([head, tail])
return pd.concat([head, tail])
# From pandas.DataFrame # From pandas.DataFrame
def _put_str(s, space): def _put_str(s, space):

343
eland/ndframe.py Normal file
View File

@ -0,0 +1,343 @@
"""
NDFrame
---------
Base class for eland.DataFrame and eland.Series.
The underlying data resides in Elasticsearch and the API aligns as much as
possible with pandas APIs.
This allows the eland.DataFrame to access large datasets stored in Elasticsearch,
without storing the dataset in local memory.
Implementation Details
----------------------
Elasticsearch indexes can be configured in many different ways, and these indexes
utilise different data structures to pandas.
eland.DataFrame operations that return individual rows (e.g. df.head()) return
_source data. If _source is not enabled, this data is not accessible.
Similarly, only Elasticsearch searchable fields can be searched or filtered, and
only Elasticsearch aggregatable fields can be aggregated or grouped.
"""
import pandas as pd
from elasticsearch_dsl import Search
import eland as ed
class NDFrame():
"""
pandas.DataFrame/Series like API that proxies into Elasticsearch index(es).
Parameters
----------
client : eland.Client
A reference to a Elasticsearch python client
index_pattern : str
An Elasticsearch index pattern. This can contain wildcards (e.g. filebeat-*).
See Also
--------
"""
def __init__(self,
client,
index_pattern,
mappings=None,
index_field=None):
self._client = ed.Client(client)
self._index_pattern = index_pattern
# Get and persist mappings, this allows us to correctly
# map returned types from Elasticsearch to pandas datatypes
if mappings is None:
self._mappings = ed.Mappings(self._client, self._index_pattern)
else:
self._mappings = mappings
self._index = ed.Index(index_field)
def _es_results_to_pandas(self, results):
"""
Parameters
----------
results: dict
Elasticsearch results from self.client.search
Returns
-------
df: pandas.DataFrame
_source values extracted from results and mapped to pandas DataFrame
dtypes are mapped via Mapping object
Notes
-----
Fields containing lists in Elasticsearch don't map easily to pandas.DataFrame
For example, an index with mapping:
```
"mappings" : {
"properties" : {
"group" : {
"type" : "keyword"
},
"user" : {
"type" : "nested",
"properties" : {
"first" : {
"type" : "keyword"
},
"last" : {
"type" : "keyword"
}
}
}
}
}
```
Adding a document:
```
"_source" : {
"group" : "amsterdam",
"user" : [
{
"first" : "John",
"last" : "Smith"
},
{
"first" : "Alice",
"last" : "White"
}
]
}
```
(https://www.elastic.co/guide/en/elasticsearch/reference/current/nested.html)
this would be transformed internally (in Elasticsearch) into a document that looks more like this:
```
{
"group" : "amsterdam",
"user.first" : [ "alice", "john" ],
"user.last" : [ "smith", "white" ]
}
```
When mapping this a pandas data frame we mimic this transformation.
Similarly, if a list is added to Elasticsearch:
```
PUT my_index/_doc/1
{
"list" : [
0, 1, 2
]
}
```
The mapping is:
```
"mappings" : {
"properties" : {
"user" : {
"type" : "long"
}
}
}
```
TODO - explain how lists are handled (https://www.elastic.co/guide/en/elasticsearch/reference/current/array.html)
TODO - an option here is to use Elasticsearch's multi-field matching instead of pandas treatment of lists (which isn't great)
NOTE - using this lists is generally not a good way to use this API
"""
def flatten_dict(y):
out = {}
def flatten(x, name=''):
# We flatten into source fields e.g. if type=geo_point
# location: {lat=52.38, lon=4.90}
if name == '':
is_source_field = False
pd_dtype = 'object'
else:
is_source_field, pd_dtype = self._mappings.source_field_pd_dtype(name[:-1])
if not is_source_field and type(x) is dict:
for a in x:
flatten(x[a], name + a + '.')
elif not is_source_field and type(x) is list:
for a in x:
flatten(a, name)
elif is_source_field == True: # only print source fields from mappings (TODO - not so efficient for large number of fields and filtered mapping)
field_name = name[:-1]
# Coerce types - for now just datetime
if pd_dtype == 'datetime64[ns]':
x = pd.to_datetime(x)
# Elasticsearch can have multiple values for a field. These are represented as lists, so
# create lists for this pivot (see notes above)
if field_name in out:
if type(out[field_name]) is not list:
l = [out[field_name]]
out[field_name] = l
out[field_name].append(x)
else:
out[field_name] = x
flatten(y)
return out
rows = []
index = []
for hit in results['hits']['hits']:
row = hit['_source']
# get index value - can be _id or can be field value in source
if self._index.is_source_field:
index_field = row[self._index.index_field]
else:
index_field = hit[self._index.index_field]
index.append(index_field)
# flatten row to map correctly to 2D DataFrame
rows.append(flatten_dict(row))
# Create pandas DataFrame
df = pd.DataFrame(data=rows, index=index)
# _source may not contain all columns in the mapping
# therefore, fill in missing columns
# (note this returns self.columns NOT IN df.columns)
missing_columns = list(set(self._columns) - set(df.columns))
for missing in missing_columns:
is_source_field, pd_dtype = self._mappings.source_field_pd_dtype(missing)
df[missing] = None
df[missing].astype(pd_dtype)
# Sort columns in mapping order
df = df[self._columns]
return df
def _head(self, n=5):
"""
Protected method that returns head as pandas.DataFrame.
Returns
-------
_head
pandas.DataFrame of top N values
"""
sort_params = self._index.sort_field + ":asc"
results = self._client.search(index=self._index_pattern, size=n, sort=sort_params)
return self._es_results_to_pandas(results)
def _tail(self, n=5):
"""
Protected method that returns tail as pandas.DataFrame.
Returns
-------
_tail
pandas.DataFrame of last N values
"""
sort_params = self._index.sort_field + ":desc"
results = self._client.search(index=self._index_pattern, size=n, sort=sort_params)
df = self._es_results_to_pandas(results)
# reverse order (index ascending)
return df.sort_index()
def _describe(self):
numeric_source_fields = self._mappings.numeric_source_fields()
# for each field we compute:
# count, mean, std, min, 25%, 50%, 75%, max
search = Search(using=self._client, index=self._index_pattern).extra(size=0)
for field in numeric_source_fields:
search.aggs.metric('extended_stats_' + field, 'extended_stats', field=field)
search.aggs.metric('percentiles_' + field, 'percentiles', field=field)
response = search.execute()
results = {}
for field in numeric_source_fields:
values = list()
values.append(response.aggregations['extended_stats_' + field]['count'])
values.append(response.aggregations['extended_stats_' + field]['avg'])
values.append(response.aggregations['extended_stats_' + field]['std_deviation'])
values.append(response.aggregations['extended_stats_' + field]['min'])
values.append(response.aggregations['percentiles_' + field]['values']['25.0'])
values.append(response.aggregations['percentiles_' + field]['values']['50.0'])
values.append(response.aggregations['percentiles_' + field]['values']['75.0'])
values.append(response.aggregations['extended_stats_' + field]['max'])
# if not None
if values.count(None) < len(values):
results[field] = values
df = pd.DataFrame(data=results, index=['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])
return df
def _filter_mappings(self, columns):
mappings = ed.Mappings(mappings=self._mappings, columns=columns)
return mappings
@property
def index(self):
return self._index
@property
def dtypes(self):
return self._mappings.dtypes()
@property
def _columns(self):
return pd.Index(self._mappings.source_fields())
def get_dtype_counts(self):
return self._mappings.get_dtype_counts()
def _index_count(self):
"""
Returns
-------
index_count: int
Count of docs where index_field exists
"""
exists_query = {"query": {"exists": {"field": self._index.index_field}}}
index_count = self._client.count(index=self._index_pattern, body=exists_query)
return index_count
def __len__(self):
"""
Returns length of info axis, but here we use the index.
"""
return self._client.count(index=self._index_pattern)
def _fake_head_tail_df(self, max_rows=1):
"""
Create a 'fake' pd.DataFrame of the entire ed.DataFrame
by concat head and tail. Used for display.
"""
head_rows = int(max_rows / 2) + max_rows % 2
tail_rows = max_rows - head_rows
head = self._head(head_rows)
tail = self._tail(tail_rows)
return head.append(tail)

396
eland/series.py Normal file
View File

@ -0,0 +1,396 @@
"""
Series
---------
One-dimensional ndarray with axis labels (including time series).
The underlying data resides in Elasticsearch and the API aligns as much as
possible with pandas.DataFrame API.
This allows the eland.Series to access large datasets stored in Elasticsearch,
without storing the dataset in local memory.
Implementation Details
----------------------
Based on NDFrame which underpins eland.1DataFrame
"""
import sys
import pandas as pd
import pandas.compat as compat
from pandas.compat import StringIO
from pandas.core.dtypes.common import (
is_categorical_dtype)
from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing
from eland import Index
from eland import NDFrame
class Series(NDFrame):
"""
pandas.Series like API that proxies into Elasticsearch index(es).
Parameters
----------
client : eland.Client
A reference to a Elasticsearch python client
index_pattern : str
An Elasticsearch index pattern. This can contain wildcards (e.g. filebeat-*).
field_name : str
The field to base the series on
See Also
--------
Examples
--------
import eland as ed
client = ed.Client(Elasticsearch())
s = ed.DataFrame(client, 'reviews', 'date')
df.head()
reviewerId vendorId rating date
0 0 0 5 2006-04-07 17:08
1 1 1 5 2006-05-04 12:16
2 2 2 4 2006-04-21 12:26
3 3 3 5 2006-04-18 15:48
4 3 4 5 2006-04-18 15:49
Notice that the types are based on Elasticsearch mappings
Notes
-----
If the Elasticsearch index is deleted or index mappings are changed after this
object is created, the object is not rebuilt and so inconsistencies can occur.
"""
def __init__(self,
client,
index_pattern,
field_name,
mappings=None,
index_field=None):
# python 3 syntax
super().__init__(client, index_pattern, mappings=mappings, index_field=index_field)
# now select column (field_name)
self._mappings = self._filter_mappings([field_name])
def head(self, n=5):
return self._df_to_series(super()._head(n))
def tail(self, n=5):
return self._df_to_series(super()._tail(n))
def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
null_counts=None):
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
This copies a lot of code from pandas.DataFrame.info as it is difficult
to split out the appropriate code or creating a SparseDataFrame gives
incorrect results on types and counts.
"""
if buf is None: # pragma: no cover
buf = sys.stdout
lines = []
lines.append(str(type(self)))
lines.append(self._index_summary())
if len(self.columns) == 0:
lines.append('Empty {name}'.format(name=type(self).__name__))
fmt.buffer_put_lines(buf, lines)
return
cols = self.columns
# hack
if max_cols is None:
max_cols = pd.get_option('display.max_info_columns',
len(self.columns) + 1)
max_rows = pd.get_option('display.max_info_rows', len(self) + 1)
if null_counts is None:
show_counts = ((len(self.columns) <= max_cols) and
(len(self) < max_rows))
else:
show_counts = null_counts
exceeds_info_cols = len(self.columns) > max_cols
def _verbose_repr():
lines.append('Data columns (total %d columns):' %
len(self.columns))
space = max(len(pprint_thing(k)) for k in self.columns) + 4
counts = None
tmpl = "{count}{dtype}"
if show_counts:
counts = self.count()
if len(cols) != len(counts): # pragma: no cover
raise AssertionError(
'Columns must equal counts '
'({cols:d} != {counts:d})'.format(
cols=len(cols), counts=len(counts)))
tmpl = "{count} non-null {dtype}"
dtypes = self.dtypes
for i, col in enumerate(self._columns):
dtype = dtypes.iloc[i]
col = pprint_thing(col)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(_put_str(col, space) + tmpl.format(count=count,
dtype=dtype))
def _non_verbose_repr():
lines.append(self._columns._summary(name='Columns'))
def _sizeof_fmt(num, size_qualifier):
# returns size in human readable format
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if num < 1024.0:
return ("{num:3.1f}{size_q} "
"{x}".format(num=num, size_q=size_qualifier, x=x))
num /= 1024.0
return "{num:3.1f}{size_q} {pb}".format(num=num,
size_q=size_qualifier,
pb='PB')
if verbose:
_verbose_repr()
elif verbose is False: # specifically set to False, not nesc None
_non_verbose_repr()
else:
if exceeds_info_cols:
_non_verbose_repr()
else:
_verbose_repr()
counts = self.get_dtype_counts()
dtypes = ['{k}({kk:d})'.format(k=k[0], kk=k[1]) for k
in sorted(counts.items())]
lines.append('dtypes: {types}'.format(types=', '.join(dtypes)))
if memory_usage is None:
memory_usage = pd.get_option('display.memory_usage')
if memory_usage:
# append memory usage of df to display
size_qualifier = ''
# TODO - this is different from pd.DataFrame as we shouldn't
# really hold much in memory. For now just approximate with getsizeof + ignore deep
mem_usage = sys.getsizeof(self)
lines.append("memory usage: {mem}\n".format(
mem=_sizeof_fmt(mem_usage, size_qualifier)))
fmt.buffer_put_lines(buf, lines)
@property
def shape(self):
"""
Return a tuple representing the dimensionality of the DataFrame.
Returns
-------
shape: tuple
0 - number of rows
1 - number of columns
"""
num_rows = len(self)
num_columns = len(self._columns)
return num_rows, num_columns
@property
def set_index(self, index_field):
copy = self.copy()
copy._index = Index(index_field)
return copy
def _index_summary(self):
head = self.head(1).index[0]
tail = self.tail(1).index[0]
index_summary = ', %s to %s' % (pprint_thing(head),
pprint_thing(tail))
name = "Index"
return '%s: %s entries%s' % (name, len(self), index_summary)
def count(self):
"""
Count non-NA cells for each column (TODO row)
Counts are based on exists queries against ES
This is inefficient, as it creates N queries (N is number of fields).
An alternative approach is to use value_count aggregations. However, they have issues in that:
1. They can only be used with aggregatable fields (e.g. keyword not text)
2. For list fields they return multiple counts. E.g. tags=['elastic', 'ml'] returns value_count=2
for a single document.
"""
counts = {}
for field in self._mappings.source_fields():
exists_query = {"query": {"exists": {"field": field}}}
field_exists_count = self._client.count(index=self._index_pattern, body=exists_query)
counts[field] = field_exists_count
count = pd.Series(data=counts, index=self._mappings.source_fields())
return count
def describe(self):
return super()._describe()
def _df_to_series(self, df):
return df.iloc[:, 0]
# ----------------------------------------------------------------------
# Rendering Methods
def __repr__(self):
"""
From pandas
"""
buf = StringIO()
max_rows = pd.get_option("display.max_rows")
self.to_string(buf=buf, na_rep='NaN', float_format=None, header=True, index=True, length=False,
dtype=False, name=False, max_rows=max_rows)
return buf.getvalue()
def to_string(self, buf=None, na_rep='NaN',
float_format=None, header=True,
index=True, length=True, dtype=True,
name=True, max_rows=None):
"""
From pandas
Render a string representation of the Series.
Parameters
----------
buf : StringIO-like, optional
buffer to write to
na_rep : string, optional
string representation of NAN to use, default 'NaN'
float_format : one-parameter function, optional
formatter function to apply to columns' elements if they are floats
default None
header : boolean, default True
Add the Series header (index name)
index : bool, optional
Add index (row) labels, default True
length : boolean, default False
Add the Series length
dtype : boolean, default False
Add the Series dtype
name : boolean, default False
Add the Series name if not None
max_rows : int, optional
Maximum number of rows to show before truncating. If None, show
all.
Returns
-------
formatted : string (if not buffer passed)
"""
if max_rows == None:
max_rows = pd.get_option("display.max_rows")
df = self._fake_head_tail_df(max_rows=max_rows + 1)
s = self._df_to_series(df)
formatter = Series.SeriesFormatter(s, len(self), name=name, length=length,
header=header, index=index,
dtype=dtype, na_rep=na_rep,
float_format=float_format,
max_rows=max_rows)
result = formatter.to_string()
# catch contract violations
if not isinstance(result, compat.text_type):
raise AssertionError("result must be of type unicode, type"
" of result is {0!r}"
"".format(result.__class__.__name__))
if buf is None:
return result
else:
try:
buf.write(result)
except AttributeError:
with open(buf, 'w') as f:
f.write(result)
class SeriesFormatter(fmt.SeriesFormatter):
"""
A hacked overridden version of pandas.io.formats.SeriesFormatter that writes correct length
"""
def __init__(self, series, series_length, buf=None, length=True, header=True, index=True,
na_rep='NaN', name=False, float_format=None, dtype=True,
max_rows=None):
super().__init__(series, buf=buf, length=length, header=header, index=index,
na_rep=na_rep, name=name, float_format=float_format, dtype=dtype,
max_rows=max_rows)
self._series_length = series_length
def _get_footer(self):
"""
Overridden with length change
(from pandas 0.24.2 io.formats.SeriesFormatter)
"""
name = self.series.name
footer = ''
if getattr(self.series.index, 'freq', None) is not None:
footer += 'Freq: {freq}'.format(freq=self.series.index.freqstr)
if self.name is not False and name is not None:
if footer:
footer += ', '
series_name = pprint_thing(name,
escape_chars=('\t', '\r', '\n'))
footer += ("Name: {sname}".format(sname=series_name)
if name is not None else "")
if (self.length is True or
(self.length == 'truncate' and self.truncate_v)):
if footer:
footer += ', '
footer += 'Length: {length}'.format(length=self._series_length)
if self.dtype is not False and self.dtype is not None:
name = getattr(self.tr_series.dtype, 'name', None)
if name:
if footer:
footer += ', '
footer += 'dtype: {typ}'.format(typ=pprint_thing(name))
# level infos are added to the end and in a new line, like it is done
# for Categoricals
if is_categorical_dtype(self.tr_series.dtype):
level_info = self.tr_series._values._repr_categories_info()
if footer:
footer += "\n"
footer += level_info
return compat.text_type(footer)

6
eland/tests/client/test_mappings_pytest.py
View File

@ -16,7 +16,7 @@ class TestMapping(TestData):
assert TEST_MAPPING1_EXPECTED_DF.index.tolist() == mappings.all_fields() assert TEST_MAPPING1_EXPECTED_DF.index.tolist() == mappings.all_fields()
assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings.mappings_capabilities['es_dtype'])) assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings._mappings_capabilities['es_dtype']))
assert TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT == mappings.count_source_fields() assert TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT == mappings.count_source_fields()
@ -24,7 +24,7 @@ class TestMapping(TestData):
mappings = ed.Mappings(ed.Client(ELASTICSEARCH_HOST), TEST_MAPPING1_INDEX_NAME) mappings = ed.Mappings(ed.Client(ELASTICSEARCH_HOST), TEST_MAPPING1_INDEX_NAME)
assert TEST_MAPPING1_EXPECTED_DF.index.tolist() == mappings.all_fields() assert TEST_MAPPING1_EXPECTED_DF.index.tolist() == mappings.all_fields()
assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings.mappings_capabilities['es_dtype'])) assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings._mappings_capabilities['es_dtype']))
assert TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT == mappings.count_source_fields() assert TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT == mappings.count_source_fields()
# Pick 1 source field # Pick 1 source field
@ -43,7 +43,7 @@ class TestMapping(TestData):
# Check original is still ok # Check original is still ok
assert TEST_MAPPING1_EXPECTED_DF.index.tolist() == mappings.all_fields() assert TEST_MAPPING1_EXPECTED_DF.index.tolist() == mappings.all_fields()
assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings.mappings_capabilities['es_dtype'])) assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings._mappings_capabilities['es_dtype']))
assert TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT == mappings.count_source_fields() assert TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT == mappings.count_source_fields()
def test_dtypes(self): def test_dtypes(self):

2
eland/tests/dataframe/test_indexing_pytest.py → eland/tests/dataframe/test_basics_pytest.py
View File

@ -7,7 +7,7 @@ import io
from pandas.util.testing import ( from pandas.util.testing import (
assert_series_equal, assert_frame_equal) assert_series_equal, assert_frame_equal)
class TestDataFrameIndexing(TestData): class TestDataFrameBasics(TestData):
def test_mapping(self): def test_mapping(self):
ed_flights_mappings = pd.DataFrame(self.ed_flights()._mappings._mappings_capabilities ed_flights_mappings = pd.DataFrame(self.ed_flights()._mappings._mappings_capabilities

32
eland/tests/series/test_basics_pytest.py Normal file
View File

@ -0,0 +1,32 @@
# File called _pytest for PyCharm compatability
from eland.tests.common import TestData
import pandas as pd
import eland as ed
import io
from eland.tests import ELASTICSEARCH_HOST
from eland.tests import FLIGHTS_INDEX_NAME
from pandas.util.testing import (
assert_series_equal, assert_frame_equal)
class TestSeriesBasics(TestData):
def test_head_tail(self):
pd_s = self.pd_flights()['Carrier']
ed_s = ed.Series(ELASTICSEARCH_HOST, FLIGHTS_INDEX_NAME, 'Carrier')
pd_s_head = pd_s.head(10)
ed_s_head = ed_s.head(10)
assert_series_equal(pd_s_head, ed_s_head)
pd_s_tail = pd_s.tail(10)
ed_s_tail = ed_s.tail(10)
assert_series_equal(pd_s_tail, ed_s_tail)
def test_print(self):
ed_s = ed.Series(ELASTICSEARCH_HOST, FLIGHTS_INDEX_NAME, 'timestamp')
print(ed_s.to_string())