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eland/eland/dataframe.py

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import sys
import warnings
from io import StringIO
import numpy as np
import pandas as pd
import six
from pandas.core.computation.eval import eval
from pandas.core.common import apply_if_callable, is_bool_indexer
from pandas.core.dtypes.common import is_list_like
from pandas.core.indexing import check_bool_indexer
from pandas.io.common import _expand_user, _stringify_path
from pandas.io.formats import console
from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing
import eland.plotting as gfx
from eland import NDFrame
from eland import Series
from eland.filter import BooleanFilter, ScriptFilter
class DataFrame(NDFrame):
"""
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes
(rows and columns) referencing data stored in Elasticsearch indices.
Where possible APIs mirror pandas.DataFrame APIs.
The underlying data is stored in Elasticsearch rather than core memory.
Parameters
----------
client: Elasticsearch client argument(s) (e.g. 'localhost:9200')
- elasticsearch-py parameters or
- elasticsearch-py instance or
- eland.Client instance
index_pattern: str
Elasticsearch index pattern (e.g. 'flights' or 'filebeat-\*')
columns: list of str, optional
List of DataFrame columns. A subset of the Elasticsearch index's fields.
index_field: str, optional
The Elasticsearch index field to use as the DataFrame index. Defaults to _id if None is used.
See Also
--------
:pandas_api_docs:`pandas.DataFrame`
Examples
--------
Constructing DataFrame from an Elasticsearch configuration arguments and an Elasticsearch index
>>> df = ed.DataFrame('localhost:9200', 'flights')
>>> df.head()
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
<BLANKLINE>
[5 rows x 27 columns]
Constructing DataFrame from an Elasticsearch client and an Elasticsearch index
>>> from elasticsearch import Elasticsearch
>>> es = Elasticsearch("localhost:9200")
>>> df = ed.DataFrame(client=es, index_pattern='flights', columns=['AvgTicketPrice', 'Cancelled'])
>>> df.head()
AvgTicketPrice Cancelled
0 841.265642 False
1 882.982662 False
2 190.636904 False
3 181.694216 True
4 730.041778 False
<BLANKLINE>
[5 rows x 2 columns]
Constructing DataFrame from an Elasticsearch client and an Elasticsearch index, with 'timestamp' as the DataFrame index field
(TODO - currently index_field must also be a field if not _id)
>>> df = ed.DataFrame(client='localhost', index_pattern='flights', columns=['AvgTicketPrice', 'timestamp'], index_field='timestamp')
>>> df.head()
AvgTicketPrice timestamp
2018-01-01T00:00:00 841.265642 2018-01-01 00:00:00
2018-01-01T00:02:06 772.100846 2018-01-01 00:02:06
2018-01-01T00:06:27 159.990962 2018-01-01 00:06:27
2018-01-01T00:33:31 800.217104 2018-01-01 00:33:31
2018-01-01T00:36:51 803.015200 2018-01-01 00:36:51
<BLANKLINE>
[5 rows x 2 columns]
"""
def __init__(self,
client=None,
index_pattern=None,
columns=None,
index_field=None,
query_compiler=None):
"""
There are effectively 2 constructors:
1. client, index_pattern, columns, index_field
2. query_compiler (eland.ElandQueryCompiler)
The constructor with 'query_compiler' is for internal use only.
"""
if query_compiler is None:
if client is None or index_pattern is None:
raise ValueError("client and index_pattern must be defined in DataFrame constructor")
# python 3 syntax
super().__init__(
client=client,
index_pattern=index_pattern,
columns=columns,
index_field=index_field,
query_compiler=query_compiler)
def _get_columns(self):
"""
The column labels of the DataFrame.
Returns
-------
pandas.Index
Elasticsearch field names as pandas.Index
See Also
--------
:pandas_api_docs:`pandas.DataFrame.columns`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> assert isinstance(df.columns, pd.Index)
>>> df.columns
Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',
... 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',
... 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',
... 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',
... 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',
... 'timestamp'],
... dtype='object')
"""
return self._query_compiler.columns
columns = property(_get_columns)
@property
def empty(self):
"""Determines if the DataFrame is empty.
Returns
-------
bool
If DataFrame is empty, return True, if not return False.
See Also
--------
:pandas_api_docs:`pandas.DataFrame.empty`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> df.empty
False
"""
return len(self.columns) == 0 or len(self.index) == 0
def head(self, n=5):
"""
Return the first n rows.
This function returns the first n rows for the object based on position.
The row order is sorted by index field.
It is useful for quickly testing if your object has the right type of data in it.
Parameters
----------
n: int, default 5
Number of rows to select.
Returns
-------
eland.DataFrame
eland DataFrame filtered on first n rows sorted by index field
See Also
--------
:pandas_api_docs:`pandas.DataFrame.head`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'Dest'])
>>> df.head(3)
Origin Dest
0 Frankfurt am Main Airport Sydney Kingsford Smith International Airport
1 Cape Town International Airport Venice Marco Polo Airport
2 Venice Marco Polo Airport Venice Marco Polo Airport
<BLANKLINE>
[3 rows x 2 columns]
"""
return DataFrame(query_compiler=self._query_compiler.head(n))
def tail(self, n=5):
"""
Return the last n rows.
This function returns the last n rows for the object based on position.
The row order is sorted by index field.
It is useful for quickly testing if your object has the right type of data in it.
Parameters
----------
n: int, default 5
Number of rows to select.
Returns
-------
eland.DataFrame:
eland DataFrame filtered on last n rows sorted by index field
See Also
--------
:pandas_api_docs:`pandas.DataFrame.tail`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'Dest'])
>>> df.tail()
Origin Dest
13054 Pisa International Airport Xi'an Xianyang International Airport
13055 Winnipeg / James Armstrong Richardson Internat... Zurich Airport
13056 Licenciado Benito Juarez International Airport Ukrainka Air Base
13057 Itami Airport Ministro Pistarini International Airport
13058 Adelaide International Airport Washington Dulles International Airport
<BLANKLINE>
[5 rows x 2 columns]
"""
return DataFrame(query_compiler=self._query_compiler.tail(n))
def __repr__(self):
"""
From pandas
"""
buf = StringIO()
# max_rows and max_cols determine the maximum size of the pretty printed tabular
# representation of the dataframe. pandas defaults are 60 and 20 respectively.
# dataframes where len(df) > max_rows shows a truncated view with 10 rows shown.
max_rows = pd.get_option("display.max_rows")
max_cols = pd.get_option("display.max_columns")
min_rows = pd.get_option("display.min_rows")
if len(self) > max_rows:
max_rows = min_rows
show_dimensions = pd.get_option("display.show_dimensions")
if pd.get_option("display.expand_frame_repr"):
width, _ = console.get_console_size()
else:
width = None
self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
line_width=width, show_dimensions=show_dimensions)
return buf.getvalue()
def _info_repr(self):
"""
True if the repr should show the info view.
"""
info_repr_option = (pd.get_option("display.large_repr") == "info")
return info_repr_option and not (self._repr_fits_horizontal_() and
self._repr_fits_vertical_())
def _repr_html_(self):
"""
From pandas
"""
if self._info_repr():
buf = StringIO("")
self.info(buf=buf)
# need to escape the <class>, should be the first line.
val = buf.getvalue().replace('<', r'&lt;', 1)
val = val.replace('>', r'&gt;', 1)
return '<pre>' + val + '</pre>'
if pd.get_option("display.notebook_repr_html"):
max_rows = pd.get_option("display.max_rows")
max_cols = pd.get_option("display.max_columns")
show_dimensions = pd.get_option("display.show_dimensions")
return self.to_html(max_rows=max_rows, max_cols=max_cols,
show_dimensions=show_dimensions, notebook=True)
else:
return None
def count(self):
"""
Count non-NA cells for each column.
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:
- They can only be used with aggregatable fields (e.g. keyword not text)
- For list fields they return multiple counts. E.g. tags=['elastic', 'ml'] returns value_count=2 for a single document.
TODO - add additional pandas.DataFrame.count features
Returns
-------
pandas.Series:
Summary of column counts
See Also
--------
:pandas_api_docs:`pandas.DataFrame.count`
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce', columns=['customer_first_name', 'geoip.city_name'])
>>> df.count()
customer_first_name 4675
geoip.city_name 4094
dtype: int64
"""
return self._query_compiler.count()
def info_es(self):
"""
A debug summary of an eland DataFrame internals.
This includes the Elasticsearch search queries and query compiler task list.
Returns
-------
str
A debug summary of an eland DataFrame internals.
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> df = df[(df.OriginAirportID == 'AMS') & (df.FlightDelayMin > 60)]
>>> df = df[['timestamp', 'OriginAirportID', 'DestAirportID', 'FlightDelayMin']]
>>> df = df.tail()
>>> df
timestamp OriginAirportID DestAirportID FlightDelayMin
12608 2018-02-10 01:20:52 AMS CYEG 120
12720 2018-02-10 14:09:40 AMS BHM 255
12725 2018-02-10 00:53:01 AMS ATL 360
12823 2018-02-10 15:41:20 AMS NGO 120
12907 2018-02-11 20:08:25 AMS LIM 225
<BLANKLINE>
[5 rows x 4 columns]
>>> print(df.info_es())
index_pattern: flights
Index:
index_field: _id
is_source_field: False
Mappings:
capabilities: _source es_dtype pd_dtype searchable aggregatable
AvgTicketPrice True float float64 True True
Cancelled True boolean bool True True
Carrier True keyword object True True
Dest True keyword object True True
DestAirportID True keyword object True True
... ... ... ... ... ...
OriginLocation True geo_point object True True
OriginRegion True keyword object True True
OriginWeather True keyword object True True
dayOfWeek True integer int64 True True
timestamp True date datetime64[ns] True True
<BLANKLINE>
[27 rows x 5 columns]
Operations:
tasks: [('boolean_filter', {'bool': {'must': [{'term': {'OriginAirportID': 'AMS'}}, {'range': {'FlightDelayMin': {'gt': 60}}}]}}), ('columns', ['timestamp', 'OriginAirportID', 'DestAirportID', 'FlightDelayMin']), ('tail', ('_doc', 5))]
size: 5
sort_params: _doc:desc
columns: ['timestamp', 'OriginAirportID', 'DestAirportID', 'FlightDelayMin']
post_processing: ['sort_index']
<BLANKLINE>
"""
buf = StringIO()
super()._info_es(buf)
return buf.getvalue()
def _index_summary(self):
# Print index summary e.g.
# Index: 103 entries, 0 to 102
# Do this by getting head and tail of dataframe
head = self.head(1)._to_pandas().index[0]
tail = self.tail(1)._to_pandas().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 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.
See :pandas_api_docs:`pandas.DataFrame.info` for full details.
Notes
-----
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.
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce', columns=['customer_first_name', 'geoip.city_name'])
>>> df.info()
<class 'eland.dataframe.DataFrame'>
Index: 4675 entries, 0 to 4674
Data columns (total 2 columns):
customer_first_name 4675 non-null object
geoip.city_name 4094 non-null object
dtypes: object(2)
memory usage: ...
"""
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
# From pandas.DataFrame
def _put_str(s, space):
return '{s}'.format(s=s)[:space].ljust(space)
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.dtypes.value_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)
def to_html(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None, max_rows=None,
max_cols=None, show_dimensions=False, decimal='.',
bold_rows=True, classes=None, escape=True, notebook=False,
border=None, table_id=None, render_links=False):
"""
Render a Elasticsearch data as an HTML table.
See Also
--------
:pandas_api_docs:`to_html` for argument details.
"""
if max_rows is None:
warnings.warn("DataFrame.to_string called without max_rows set "
"- this will return entire index results. "
"Setting max_rows=60, overwrite if different behaviour is required.")
max_rows = 60
# Create a slightly bigger dataframe than display
df = self._build_repr_df(max_rows + 1, max_cols)
if buf is not None:
_buf = _expand_user(_stringify_path(buf))
else:
_buf = StringIO()
df.to_html(buf=_buf, columns=columns, col_space=col_space, header=header,
index=index, na_rep=na_rep, formatters=formatters, float_format=float_format,
sparsify=sparsify, index_names=index_names, justify=justify, max_rows=max_rows,
max_cols=max_cols, show_dimensions=False, decimal=decimal,
bold_rows=bold_rows, classes=classes, escape=escape, notebook=notebook,
border=border, table_id=table_id, render_links=render_links)
# 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<p>{nrows} rows x {ncols} columns</p>"
.format(nrows=len(self.index), ncols=len(self.columns)))
if buf is None:
result = _buf.getvalue()
return result
def to_string(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None,
max_rows=None, max_cols=None, show_dimensions=False,
decimal='.', line_width=None):
"""
Render a DataFrame to a console-friendly tabular output.
Follows pandas implementation except we set max_rows default to avoid careless extraction of entire index.
See Also
--------
:pandas_api_docs:`to_string` for argument details.
"""
if max_rows is None:
warnings.warn("DataFrame.to_string called without max_rows set "
"- this will return entire index results. "
"Setting max_rows=60, overwrite if different behaviour is required.")
max_rows = 60
# Create a slightly bigger dataframe than display
df = self._build_repr_df(max_rows + 1, max_cols)
if buf is not None:
_buf = _expand_user(_stringify_path(buf))
else:
_buf = StringIO()
df.to_string(buf=_buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=False, # print this outside of this call
decimal=decimal,
line_width=line_width)
# 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]"
.format(nrows=len(self.index), ncols=len(self.columns)))
if buf is None:
result = _buf.getvalue()
return result
def __getattr__(self, key):
"""After regular attribute access, looks up the name in the columns
Parameters
----------
key: str
Attribute name.
Returns
-------
The value of the attribute.
"""
try:
return object.__getattribute__(self, key)
except AttributeError as e:
if key in self.columns:
return self[key]
raise e
def _getitem(self, key):
"""Get the column specified by key for this DataFrame.
Args:
key : The column name.
Returns:
A Pandas Series representing the value for the column.
"""
key = apply_if_callable(key, self)
# Shortcut if key is an actual column
try:
if key in self.columns:
return self._getitem_column(key)
except (KeyError, ValueError, TypeError):
pass
if isinstance(key, (Series, np.ndarray, pd.Index, list)):
return self._getitem_array(key)
elif isinstance(key, DataFrame):
return self.where(key)
elif isinstance(key, BooleanFilter):
return DataFrame(
query_compiler=self._query_compiler._update_query(key)
)
else:
return self._getitem_column(key)
def _getitem_column(self, key):
if key not in self.columns:
raise KeyError("Requested column is not in the DataFrame {}".format(key))
s = self._reduce_dimension(self._query_compiler.getitem_column_array([key]))
return s
def _getitem_array(self, key):
if isinstance(key, Series):
key = key._to_pandas()
if is_bool_indexer(key):
if isinstance(key, pd.Series) and not key.index.equals(self.index):
warnings.warn(
"Boolean Series key will be reindexed to match DataFrame index.",
PendingDeprecationWarning,
stacklevel=3,
)
elif len(key) != len(self.index):
raise ValueError(
"Item wrong length {} instead of {}.".format(
len(key), len(self.index)
)
)
key = check_bool_indexer(self.index, key)
# We convert to a RangeIndex because getitem_row_array is expecting a list
# of indices, and RangeIndex will give us the exact indices of each boolean
# requested.
key = pd.RangeIndex(len(self.index))[key]
if len(key):
return DataFrame(
query_compiler=self._query_compiler.getitem_row_array(key)
)
else:
return DataFrame(columns=self.columns)
else:
if any(k not in self.columns for k in key):
raise KeyError(
"{} not index".format(
str([k for k in key if k not in self.columns]).replace(",", "")
)
)
return DataFrame(
query_compiler=self._query_compiler.getitem_column_array(key)
)
def _create_or_update_from_compiler(self, new_query_compiler, inplace=False):
"""Returns or updates a DataFrame given new query_compiler"""
assert (
isinstance(new_query_compiler, type(self._query_compiler))
or type(new_query_compiler) in self._query_compiler.__class__.__bases__
), "Invalid Query Compiler object: {}".format(type(new_query_compiler))
if not inplace:
return DataFrame(query_compiler=new_query_compiler)
else:
self._query_compiler = new_query_compiler
def _reduce_dimension(self, query_compiler):
return Series(query_compiler=query_compiler)
def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
columns=None, header=True, index=True, index_label=None,
mode='w', encoding=None, compression='infer', quoting=None,
quotechar='"', line_terminator=None, chunksize=None,
tupleize_cols=None, date_format=None, doublequote=True,
escapechar=None, decimal='.'):
"""
Write Elasticsearch data to a comma-separated values (csv) file.
See Also
--------
:pandas_api_docs:`to_csv` for argument details.
"""
kwargs = {
"path_or_buf": path_or_buf,
"sep": sep,
"na_rep": na_rep,
"float_format": float_format,
"columns": columns,
"header": header,
"index": index,
"index_label": index_label,
"mode": mode,
"encoding": encoding,
"compression": compression,
"quoting": quoting,
"quotechar": quotechar,
"line_terminator": line_terminator,
"chunksize": chunksize,
"date_format": date_format,
"doublequote": doublequote,
"escapechar": escapechar,
"decimal": decimal,
}
return self._query_compiler.to_csv(**kwargs)
def _to_pandas(self):
"""
Utility method to convert eland.Dataframe to pandas.Dataframe
Returns
-------
pandas.DataFrame
"""
return self._query_compiler.to_pandas()
def _empty_pd_df(self):
return self._query_compiler._empty_pd_ef()
def select_dtypes(self, include=None, exclude=None):
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Compatible with :pandas_api_docs:`pandas.DataFrame.select_dtypes`
Returns
-------
eland.DataFrame
DataFrame contains only columns of selected dtypes
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights',
... columns=['AvgTicketPrice', 'Dest', 'Cancelled', 'timestamp', 'dayOfWeek'])
>>> df.dtypes
AvgTicketPrice float64
Dest object
Cancelled bool
timestamp datetime64[ns]
dayOfWeek int64
dtype: object
>>> df = df.select_dtypes(include=[np.number, 'datetime'])
>>> df.dtypes
AvgTicketPrice float64
timestamp datetime64[ns]
dayOfWeek int64
dtype: object
"""
empty_df = self._empty_pd_df()
empty_df = empty_df.select_dtypes(include=include, exclude=exclude)
return self._getitem_array(empty_df.columns)
@property
def shape(self):
"""
Return a tuple representing the dimensionality of the DataFrame.
Returns
-------
shape: tuple
0. number of rows
1. number of columns
Notes
-----
- number of rows ``len(df)`` queries Elasticsearch
- number of columns ``len(df.columns)`` is cached. If mappings are updated, DataFrame must be updated.
Examples
--------
>>> df = ed.read_es('localhost', 'ecommerce')
>>> df.shape
(4675, 45)
"""
num_rows = len(self)
num_columns = len(self.columns)
return num_rows, num_columns
def keys(self):
"""
Return columns
See :pandas_api_docs:`pandas.DataFrame.keys`
Returns
-------
pandas.Index
Elasticsearch field names as pandas.Index
"""
return self.columns
def aggregate(self, func, axis=0, *args, **kwargs):
"""
Aggregate using one or more operations over the specified axis.
Parameters
----------
func: function, str, list or dict
Function to use for aggregating the data. If a function, must either
work when passed a %(klass)s or when passed to %(klass)s.apply.
Accepted combinations are:
- function
- string function name
- list of functions and/or function names, e.g. ``[np.sum, 'mean']``
- dict of axis labels -> functions, function names or list of such.
Currently, we only support ``['count', 'mad', 'max', 'mean', 'median', 'min', 'mode', 'quantile',
'rank', 'sem', 'skew', 'sum', 'std', 'var']``
axis
Currently, we only support axis=0 (index)
*args
Positional arguments to pass to `func`
**kwargs
Keyword arguments to pass to `func`
Returns
-------
DataFrame, Series or scalar
if DataFrame.agg is called with a single function, returns a Series
if DataFrame.agg is called with several functions, returns a DataFrame
if Series.agg is called with single function, returns a scalar
if Series.agg is called with several functions, returns a Series
See Also
--------
:pandas_api_docs:`pandas.DataFrame.aggregate`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
DistanceKilometers AvgTicketPrice
sum 9.261629e+07 8.204365e+06
min 0.000000e+00 1.000205e+02
std 4.578263e+03 2.663867e+02
"""
axis = pd.DataFrame._get_axis_number(axis)
if axis == 1:
raise NotImplementedError("Aggregating via index not currently implemented - needs index transform")
# currently we only support a subset of functions that aggregate columns.
# ['count', 'mad', 'max', 'mean', 'median', 'min', 'mode', 'quantile', 'rank', 'sem', 'skew', 'sum', 'std', 'var', 'nunique']
if isinstance(func, str):
# wrap in list
func = [func]
return self._query_compiler.aggs(func)
elif is_list_like(func):
# we have a list!
return self._query_compiler.aggs(func)
agg = aggregate
hist = gfx.ed_hist_frame
def query(self, expr):
"""
Query the columns of a DataFrame with a boolean expression.
TODO - add additional pandas arguments
Parameters
----------
expr: str
A boolean expression
Returns
-------
eland.DataFrame:
DataFrame populated by results of the query
TODO - add link to eland user guide
See Also
--------
:pandas_api_docs:`pandas.DataFrame.query`
:pandas_user_guide:`indexing`
Examples
--------
>>> df = ed.read_es('localhost', 'flights')
>>> df.shape
(13059, 27)
>>> df.query('FlightDelayMin > 60').shape
(2730, 27)
"""
if isinstance(expr, BooleanFilter):
return DataFrame(
query_compiler=self._query_compiler._update_query(BooleanFilter(expr))
)
elif isinstance(expr, six.string_types):
column_resolver = {}
for key in self.keys():
column_resolver[key] = self.get(key)
# Create fake resolvers - index resolver is empty
resolvers = column_resolver, {}
# Use pandas eval to parse query - TODO validate this further
filter = eval(expr, target=self, resolvers=tuple(tuple(resolvers)))
return DataFrame(
query_compiler=self._query_compiler._update_query(filter)
)
else:
raise NotImplementedError(expr, type(expr))
def get(self, key, default=None):
"""
Get item from object for given key (ex: DataFrame column).
Returns default value if not found.
Parameters
----------
key: object
Returns
-------
value: same type as items contained in object
See Also
--------
:pandas_api_docs:`pandas.DataFrame.get`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> df.get('Carrier')
0 Kibana Airlines
1 Logstash Airways
2 Logstash Airways
3 Kibana Airlines
4 Kibana Airlines
...
13054 Logstash Airways
13055 Logstash Airways
13056 Logstash Airways
13057 JetBeats
13058 JetBeats
Name: Carrier, Length: 13059, dtype: object
"""
if key in self.keys():
return self._getitem(key)
else:
return default
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