def eland_to_pandas(ed_df: DataFrame, show_progress: bool = False) -> pd.DataFrame:
"""
Convert an eland.Dataframe to a pandas.DataFrame
**Note: this loads the entire Elasticsearch index into in core pandas.DataFrame structures. For large
indices this can create significant load on the Elasticsearch cluster and require signficant memory**
Parameters
----------
ed_df: eland.DataFrame
The source eland.Dataframe referencing the Elasticsearch index
show_progress: bool
Output progress of option to stdout? By default False.
Returns
-------
pandas.Dataframe
pandas.DataFrame contains all rows and columns in eland.DataFrame
Examples
--------
>>> ed_df = ed.DataFrame('localhost', 'flights').head()
>>> type(ed_df)
<class 'eland.dataframe.DataFrame'>
>>> ed_df
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]
Convert `eland.DataFrame` to `pandas.DataFrame` (Note: this loads entire Elasticsearch index into core memory)
>>> pd_df = ed.eland_to_pandas(ed_df)
>>> type(pd_df)
<class 'pandas.core.frame.DataFrame'>
>>> pd_df
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]
Convert `eland.DataFrame` to `pandas.DataFrame` and show progress every 10000 rows
>>> pd_df = ed.eland_to_pandas(ed.DataFrame('localhost', 'flights'), show_progress=True) # doctest: +SKIP
2020-01-29 12:43:36.572395: read 10000 rows
2020-01-29 12:43:37.309031: read 13059 rows
See Also
--------
eland.pandas_to_eland: Create an eland.Dataframe from pandas.DataFrame
"""
return ed_df.to_pandas(show_progress=show_progress)
def read_es(
es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch],
es_index_pattern: str,
) -> DataFrame:
"""
Utility method to create an eland.Dataframe from an Elasticsearch index_pattern.
(Similar to pandas.read_csv, but source data is an Elasticsearch index rather than
a csv file)
Parameters
----------
es_client: Elasticsearch client argument(s)
- elasticsearch-py parameters or
- elasticsearch-py instance
es_index_pattern: str
Elasticsearch index pattern
Returns
-------
eland.DataFrame
See Also
--------
eland.pandas_to_eland: Create an eland.Dataframe from pandas.DataFrame
eland.eland_to_pandas: Create a pandas.Dataframe from eland.DataFrame
"""
return DataFrame(es_client=es_client, es_index_pattern=es_index_pattern)
def pandas_to_eland(
pd_df: pd.DataFrame,
es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch],
es_dest_index: str,
es_if_exists: str = "fail",
es_refresh: bool = False,
es_dropna: bool = False,
es_type_overrides: Optional[Mapping[str, str]] = None,
thread_count: int = 4,
chunksize: Optional[int] = None,
use_pandas_index_for_es_ids: bool = True,
) -> DataFrame:
"""
Append a pandas DataFrame to an Elasticsearch index.
Mainly used in testing.
Modifies the elasticsearch destination index
Parameters
----------
es_client: Elasticsearch client argument(s)
- elasticsearch-py parameters or
- elasticsearch-py instance
es_dest_index: str
Name of Elasticsearch index to be appended to
es_if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the index already exists.
- fail: Raise a ValueError.
- replace: Delete the index before inserting new values.
- append: Insert new values to the existing index. Create if does not exist.
es_refresh: bool, default 'False'
Refresh es_dest_index after bulk index
es_dropna: bool, default 'False'
* True: Remove missing values (see pandas.Series.dropna)
* False: Include missing values - may cause bulk to fail
es_type_overrides: dict, default None
Dict of field_name: es_data_type that overrides default es data types
thread_count: int
number of the threads to use for the bulk requests
chunksize: int, default None
Number of pandas.DataFrame rows to read before bulk index into Elasticsearch
use_pandas_index_for_es_ids: bool, default 'True'
* True: pandas.DataFrame.index fields will be used to populate Elasticsearch '_id' fields.
* False: Ignore pandas.DataFrame.index when indexing into Elasticsearch
Returns
-------
eland.Dataframe
eland.DataFrame referencing data in destination_index
Examples
--------
>>> pd_df = pd.DataFrame(data={'A': 3.141,
... 'B': 1,
... 'C': 'foo',
... 'D': pd.Timestamp('20190102'),
... 'E': [1.0, 2.0, 3.0],
... 'F': False,
... 'G': [1, 2, 3],
... 'H': 'Long text - to be indexed as es type text'},
... index=['0', '1', '2'])
>>> type(pd_df)
<class 'pandas.core.frame.DataFrame'>
>>> pd_df
A B ... G H
0 3.141 1 ... 1 Long text - to be indexed as es type text
1 3.141 1 ... 2 Long text - to be indexed as es type text
2 3.141 1 ... 3 Long text - to be indexed as es type text
<BLANKLINE>
[3 rows x 8 columns]
>>> pd_df.dtypes
A float64
B int64
C object
D datetime64[ns]
E float64
F bool
G int64
H object
dtype: object
Convert `pandas.DataFrame` to `eland.DataFrame` - this creates an Elasticsearch index called `pandas_to_eland`.
Overwrite existing Elasticsearch index if it exists `if_exists="replace"`, and sync index so it is
readable on return `refresh=True`
>>> ed_df = ed.pandas_to_eland(pd_df,
... 'localhost',
... 'pandas_to_eland',
... es_if_exists="replace",
... es_refresh=True,
... es_type_overrides={'H':'text'}) # index field 'H' as text not keyword
>>> type(ed_df)
<class 'eland.dataframe.DataFrame'>
>>> ed_df
A B ... G H
0 3.141 1 ... 1 Long text - to be indexed as es type text
1 3.141 1 ... 2 Long text - to be indexed as es type text
2 3.141 1 ... 3 Long text - to be indexed as es type text
<BLANKLINE>
[3 rows x 8 columns]
>>> ed_df.dtypes
A float64
B int64
C object
D datetime64[ns]
E float64
F bool
G int64
H object
dtype: object
See Also
--------
eland.eland_to_pandas: Create a pandas.Dataframe from eland.DataFrame
"""
if chunksize is None:
chunksize = DEFAULT_CHUNK_SIZE
mapping = FieldMappings._generate_es_mappings(pd_df, es_type_overrides)
es_client = ensure_es_client(es_client)
# If table exists, check if_exists parameter
if es_client.indices.exists(index=es_dest_index):
if es_if_exists == "fail":
raise ValueError(
f"Could not create the index [{es_dest_index}] because it "
f"already exists. "
f"Change the 'es_if_exists' parameter to "
f"'append' or 'replace' data."
)
elif es_if_exists == "replace":
es_client.indices.delete(index=es_dest_index)
es_client.indices.create(index=es_dest_index, body=mapping)
elif es_if_exists == "append":
dest_mapping = es_client.indices.get_mapping(index=es_dest_index)[
es_dest_index
]
verify_mapping_compatibility(
ed_mapping=mapping,
es_mapping=dest_mapping,
es_type_overrides=es_type_overrides,
)
else:
es_client.indices.create(index=es_dest_index, body=mapping)
def action_generator(
pd_df: pd.DataFrame,
es_dropna: bool,
use_pandas_index_for_es_ids: bool,
es_dest_index: str,
) -> Generator[Dict[str, Any], None, None]:
for row in pd_df.iterrows():
if es_dropna:
values = row[1].dropna().to_dict()
else:
values = row[1].to_dict()
if use_pandas_index_for_es_ids:
# Use index as _id
id = row[0]
action = {"_index": es_dest_index, "_source": values, "_id": str(id)}
else:
action = {"_index": es_dest_index, "_source": values}
yield action
# parallel_bulk is lazy generator so use deque to consume them immediately
# maxlen = 0 because don't need results of parallel_bulk
deque(
parallel_bulk(
client=es_client,
actions=action_generator(
pd_df, es_dropna, use_pandas_index_for_es_ids, es_dest_index
),
thread_count=thread_count,
chunk_size=int(chunksize / thread_count),
),
maxlen=0,
)
if es_refresh:
es_client.indices.refresh(index=es_dest_index)
return DataFrame(es_client, es_dest_index)
def csv_to_eland( # type: ignore
filepath_or_buffer,
es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch],
es_dest_index: str,
es_if_exists: str = "fail",
es_refresh: bool = False,
es_dropna: bool = False,
es_type_overrides: Optional[Mapping[str, str]] = None,
sep=",",
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
# General Parsing Configuration
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# Iteration
# iterator=False,
chunksize=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal=b".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
# Error Handling
warn_bad_lines: bool = True,
error_bad_lines: bool = True,
on_bad_lines: str = "error",
# Internal
delim_whitespace=False,
low_memory: bool = _DEFAULT_LOW_MEMORY,
memory_map=False,
float_precision=None,
) -> "DataFrame":
"""
Read a comma-separated values (csv) file into eland.DataFrame (i.e. an Elasticsearch index).
**Modifies an Elasticsearch index**
**Note pandas iteration options not supported**
Parameters
----------
es_client: Elasticsearch client argument(s)
- elasticsearch-py parameters or
- elasticsearch-py instance
es_dest_index: str
Name of Elasticsearch index to be appended to
es_if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the index already exists.
- fail: Raise a ValueError.
- replace: Delete the index before inserting new values.
- append: Insert new values to the existing index. Create if does not exist.
es_dropna: bool, default 'False'
* True: Remove missing values (see pandas.Series.dropna)
* False: Include missing values - may cause bulk to fail
es_type_overrides: dict, default None
Dict of columns: es_type to override default es datatype mappings
chunksize
number of csv rows to read before bulk index into Elasticsearch
Other Parameters
----------------
Parameters derived from :pandas_api_docs:`pandas.read_csv`.
See Also
--------
:pandas_api_docs:`pandas.read_csv`
Notes
-----
iterator not supported
Examples
--------
See if 'churn' index exists in Elasticsearch
>>> from elasticsearch import Elasticsearch # doctest: +SKIP
>>> es = Elasticsearch() # doctest: +SKIP
>>> es.indices.exists(index="churn") # doctest: +SKIP
False
Read 'churn.csv' and use first column as _id (and eland.DataFrame index)
::
# churn.csv
,state,account length,area code,phone number,international plan,voice mail plan,number vmail messages,total day minutes,total day calls,total day charge,total eve minutes,total eve calls,total eve charge,total night minutes,total night calls,total night charge,total intl minutes,total intl calls,total intl charge,customer service calls,churn
0,KS,128,415,382-4657,no,yes,25,265.1,110,45.07,197.4,99,16.78,244.7,91,11.01,10.0,3,2.7,1,0
1,OH,107,415,371-7191,no,yes,26,161.6,123,27.47,195.5,103,16.62,254.4,103,11.45,13.7,3,3.7,1,0
...
>>> ed.csv_to_eland(
... "churn.csv",
... es_client='localhost',
... es_dest_index='churn',
... es_refresh=True,
... index_col=0
... ) # doctest: +SKIP
account length area code churn customer service calls ... total night calls total night charge total night minutes voice mail plan
0 128 415 0 1 ... 91 11.01 244.7 yes
1 107 415 0 1 ... 103 11.45 254.4 yes
2 137 415 0 0 ... 104 7.32 162.6 no
3 84 408 0 2 ... 89 8.86 196.9 no
4 75 415 0 3 ... 121 8.41 186.9 no
... ... ... ... ... ... ... ... ... ...
3328 192 415 0 2 ... 83 12.56 279.1 yes
3329 68 415 0 3 ... 123 8.61 191.3 no
3330 28 510 0 2 ... 91 8.64 191.9 no
3331 184 510 0 2 ... 137 6.26 139.2 no
3332 74 415 0 0 ... 77 10.86 241.4 yes
<BLANKLINE>
[3333 rows x 21 columns]
Validate data now exists in 'churn' index:
>>> es.search(index="churn", size=1) # doctest: +SKIP
{'took': 1, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 3333, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'churn', '_id': '0', '_score': 1.0, '_source': {'state': 'KS', 'account length': 128, 'area code': 415, 'phone number': '382-4657', 'international plan': 'no', 'voice mail plan': 'yes', 'number vmail messages': 25, 'total day minutes': 265.1, 'total day calls': 110, 'total day charge': 45.07, 'total eve minutes': 197.4, 'total eve calls': 99, 'total eve charge': 16.78, 'total night minutes': 244.7, 'total night calls': 91, 'total night charge': 11.01, 'total intl minutes': 10.0, 'total intl calls': 3, 'total intl charge': 2.7, 'customer service calls': 1, 'churn': 0}}]}}
TODO - currently the eland.DataFrame may not retain the order of the data in the csv.
"""
kwargs: Dict[str, Any] = {
"sep": sep,
"delimiter": delimiter,
"engine": engine,
"dialect": dialect,
"compression": compression,
# "engine_specified": engine_specified,
"doublequote": doublequote,
"escapechar": escapechar,
"quotechar": quotechar,
"quoting": quoting,
"skipinitialspace": skipinitialspace,
"lineterminator": lineterminator,
"header": header,
"index_col": index_col,
"names": names,
"prefix": prefix,
"skiprows": skiprows,
"skipfooter": skipfooter,
"na_values": na_values,
"true_values": true_values,
"false_values": false_values,
"keep_default_na": keep_default_na,
"thousands": thousands,
"comment": comment,
"decimal": decimal,
"parse_dates": parse_dates,
"keep_date_col": keep_date_col,
"dayfirst": dayfirst,
"date_parser": date_parser,
"cache_dates": cache_dates,
"nrows": nrows,
# "iterator": iterator,
"chunksize": chunksize,
"converters": converters,
"dtype": dtype,
"usecols": usecols,
"verbose": verbose,
"encoding": encoding,
"squeeze": squeeze,
"memory_map": memory_map,
"float_precision": float_precision,
"na_filter": na_filter,
"delim_whitespace": delim_whitespace,
"warn_bad_lines": warn_bad_lines,
"error_bad_lines": error_bad_lines,
"on_bad_lines": on_bad_lines,
"low_memory": low_memory,
"mangle_dupe_cols": mangle_dupe_cols,
"infer_datetime_format": infer_datetime_format,
"skip_blank_lines": skip_blank_lines,
}
if chunksize is None:
kwargs["chunksize"] = DEFAULT_CHUNK_SIZE
if PANDAS_VERSION >= (1, 3):
# Bug in Pandas v1.3.0
# If names and prefix both passed as None, it's considering them as specified values and throwing ValueError
# Ref: https://github.com/pandas-dev/pandas/issues/42387
if kwargs["names"] is None and kwargs["prefix"] is None:
kwargs.pop("prefix")
if kwargs["warn_bad_lines"] is True:
kwargs["on_bad_lines"] = "warn"
if kwargs["error_bad_lines"] is True:
kwargs["on_bad_lines"] = "error"
kwargs.pop("warn_bad_lines")
kwargs.pop("error_bad_lines")
else:
if on_bad_lines == "warn":
kwargs["warn_bad_lines"] = True
if on_bad_lines == "error":
kwargs["error_bad_lines"] = True
kwargs.pop("on_bad_lines")
# read csv in chunks to pandas DataFrame and dump to eland DataFrame (and Elasticsearch)
reader = pd.read_csv(filepath_or_buffer, **kwargs)
first_write = True
for chunk in reader:
pandas_to_eland(
chunk,
es_client,
es_dest_index,
chunksize=chunksize,
es_refresh=es_refresh,
es_dropna=es_dropna,
es_type_overrides=es_type_overrides,
# es_if_exists should be 'append' except on the first call to pandas_to_eland()
es_if_exists=(es_if_exists if first_write else "append"),
)
first_write = False
# Now create an eland.DataFrame that references the new index
return DataFrame(es_client, es_index_pattern=es_dest_index)
def read_csv(
filepath_or_buffer,
es_client,
es_dest_index,
es_if_exists="fail",
es_refresh=False,
es_dropna=False,
es_type_overrides=None,
sep=",",
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
# General Parsing Configuration
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# Iteration
# iterator=False,
chunksize=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal=b".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
# Error Handling
error_bad_lines=True,
warn_bad_lines=True,
# Internal
delim_whitespace=False,
low_memory=_c_parser_defaults["low_memory"],
memory_map=False,
float_precision=None,
):
"""
Read a comma-separated values (csv) file into eland.DataFrame (i.e. an Elasticsearch index).
**Modifies an Elasticsearch index**
**Note pandas iteration options not supported**
Parameters
----------
es_client: Elasticsearch client argument(s)
- elasticsearch-py parameters or
- elasticsearch-py instance
es_dest_index: str
Name of Elasticsearch index to be appended to
es_if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the index already exists.
- fail: Raise a ValueError.
- replace: Delete the index before inserting new values.
- append: Insert new values to the existing index. Create if does not exist.
es_dropna: bool, default 'False'
* True: Remove missing values (see pandas.Series.dropna)
* False: Include missing values - may cause bulk to fail
es_type_overrides: dict, default None
Dict of columns: es_type to override default es datatype mappings
chunksize
number of csv rows to read before bulk index into Elasticsearch
Other Parameters
----------------
Parameters derived from :pandas_api_docs:`pandas.read_csv`.
See Also
--------
:pandas_api_docs:`pandas.read_csv`
Notes
-----
iterator not supported
Examples
--------
See if 'churn' index exists in Elasticsearch
>>> from elasticsearch import Elasticsearch # doctest: +SKIP
>>> es = Elasticsearch() # doctest: +SKIP
>>> es.indices.exists(index="churn") # doctest: +SKIP
False
Read 'churn.csv' and use first column as _id (and eland.DataFrame index)
::
# churn.csv
,state,account length,area code,phone number,international plan,voice mail plan,number vmail messages,total day minutes,total day calls,total day charge,total eve minutes,total eve calls,total eve charge,total night minutes,total night calls,total night charge,total intl minutes,total intl calls,total intl charge,customer service calls,churn
0,KS,128,415,382-4657,no,yes,25,265.1,110,45.07,197.4,99,16.78,244.7,91,11.01,10.0,3,2.7,1,0
1,OH,107,415,371-7191,no,yes,26,161.6,123,27.47,195.5,103,16.62,254.4,103,11.45,13.7,3,3.7,1,0
...
>>> ed.read_csv("churn.csv",
... es_client='localhost',
... es_dest_index='churn',
... es_refresh=True,
... index_col=0) # doctest: +SKIP
account length area code churn customer service calls ... total night calls total night charge total night minutes voice mail plan
0 128 415 0 1 ... 91 11.01 244.7 yes
1 107 415 0 1 ... 103 11.45 254.4 yes
2 137 415 0 0 ... 104 7.32 162.6 no
3 84 408 0 2 ... 89 8.86 196.9 no
4 75 415 0 3 ... 121 8.41 186.9 no
... ... ... ... ... ... ... ... ... ...
3328 192 415 0 2 ... 83 12.56 279.1 yes
3329 68 415 0 3 ... 123 8.61 191.3 no
3330 28 510 0 2 ... 91 8.64 191.9 no
3331 184 510 0 2 ... 137 6.26 139.2 no
3332 74 415 0 0 ... 77 10.86 241.4 yes
<BLANKLINE>
[3333 rows x 21 columns]
Validate data now exists in 'churn' index:
>>> es.search(index="churn", size=1) # doctest: +SKIP
{'took': 1, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 3333, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'churn', '_id': '0', '_score': 1.0, '_source': {'state': 'KS', 'account length': 128, 'area code': 415, 'phone number': '382-4657', 'international plan': 'no', 'voice mail plan': 'yes', 'number vmail messages': 25, 'total day minutes': 265.1, 'total day calls': 110, 'total day charge': 45.07, 'total eve minutes': 197.4, 'total eve calls': 99, 'total eve charge': 16.78, 'total night minutes': 244.7, 'total night calls': 91, 'total night charge': 11.01, 'total intl minutes': 10.0, 'total intl calls': 3, 'total intl charge': 2.7, 'customer service calls': 1, 'churn': 0}}]}}
TODO - currently the eland.DataFrame may not retain the order of the data in the csv.
"""
kwds = dict()
kwds.update(
sep=sep,
delimiter=delimiter,
engine=engine,
dialect=dialect,
compression=compression,
# engine_specified=engine_specified,
doublequote=doublequote,
escapechar=escapechar,
quotechar=quotechar,
quoting=quoting,
skipinitialspace=skipinitialspace,
lineterminator=lineterminator,
header=header,
index_col=index_col,
names=names,
prefix=prefix,
skiprows=skiprows,
skipfooter=skipfooter,
na_values=na_values,
true_values=true_values,
false_values=false_values,
keep_default_na=keep_default_na,
thousands=thousands,
comment=comment,
decimal=decimal,
parse_dates=parse_dates,
keep_date_col=keep_date_col,
dayfirst=dayfirst,
date_parser=date_parser,
cache_dates=cache_dates,
nrows=nrows,
# iterator=iterator,
chunksize=chunksize,
converters=converters,
dtype=dtype,
usecols=usecols,
verbose=verbose,
encoding=encoding,
squeeze=squeeze,
memory_map=memory_map,
float_precision=float_precision,
na_filter=na_filter,
delim_whitespace=delim_whitespace,
warn_bad_lines=warn_bad_lines,
error_bad_lines=error_bad_lines,
low_memory=low_memory,
mangle_dupe_cols=mangle_dupe_cols,
infer_datetime_format=infer_datetime_format,
skip_blank_lines=skip_blank_lines,
)
if chunksize is None:
kwds.update(chunksize=DEFAULT_CHUNK_SIZE)
# read csv in chunks to pandas DataFrame and dump to eland DataFrame (and Elasticsearch)
reader = pd.read_csv(filepath_or_buffer, **kwds)
first_write = True
for chunk in reader:
if first_write:
pandas_to_eland(
chunk,
es_client,
es_dest_index,
es_if_exists=es_if_exists,
chunksize=chunksize,
es_refresh=es_refresh,
es_dropna=es_dropna,
es_type_overrides=es_type_overrides,
)
first_write = False
else:
pandas_to_eland(
chunk,
es_client,
es_dest_index,
es_if_exists="append",
chunksize=chunksize,
es_refresh=es_refresh,
es_dropna=es_dropna,
es_type_overrides=es_type_overrides,
)
# Now create an eland.DataFrame that references the new index
return DataFrame(es_client, es_dest_index)
def pandas_to_eland(
pd_df,
es_client,
es_dest_index,
es_if_exists="fail",
es_refresh=False,
es_dropna=False,
es_geo_points=None,
chunksize=None,
use_pandas_index_for_es_ids=True,
):
"""
Append a pandas DataFrame to an Elasticsearch index.
Mainly used in testing.
Modifies the elasticsearch destination index
Parameters
----------
es_client: Elasticsearch client argument(s)
- elasticsearch-py parameters or
- elasticsearch-py instance or
- eland.Client instance
es_dest_index: str
Name of Elasticsearch index to be appended to
es_if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the index already exists.
- fail: Raise a ValueError.
- replace: Delete the index before inserting new values.
- append: Insert new values to the existing index. Create if does not exist.
es_refresh: bool, default 'False'
Refresh es_dest_index after bulk index
es_dropna: bool, default 'False'
* True: Remove missing values (see pandas.Series.dropna)
* False: Include missing values - may cause bulk to fail
es_geo_points: list, default None
List of columns to map to geo_point data type
chunksize: int, default None
Number of pandas.DataFrame rows to read before bulk index into Elasticsearch
use_pandas_index_for_es_ids: bool, default 'True'
* True: pandas.DataFrame.index fields will be used to populate Elasticsearch '_id' fields.
* False: Ignore pandas.DataFrame.index when indexing into Elasticsearch
Returns
-------
eland.Dataframe
eland.DataFrame referencing data in destination_index
Examples
--------
>>> pd_df = pd.DataFrame(data={'A': 3.141,
... 'B': 1,
... 'C': 'foo',
... 'D': pd.Timestamp('20190102'),
... 'E': [1.0, 2.0, 3.0],
... 'F': False,
... 'G': [1, 2, 3]},
... index=['0', '1', '2'])
>>> type(pd_df)
<class 'pandas.core.frame.DataFrame'>
>>> pd_df
A B ... F G
0 3.141 1 ... False 1
1 3.141 1 ... False 2
2 3.141 1 ... False 3
<BLANKLINE>
[3 rows x 7 columns]
>>> pd_df.dtypes
A float64
B int64
C object
D datetime64[ns]
E float64
F bool
G int64
dtype: object
Convert `pandas.DataFrame` to `eland.DataFrame` - this creates an Elasticsearch index called `pandas_to_eland`.
Overwrite existing Elasticsearch index if it exists `if_exists="replace"`, and sync index so it is
readable on return `refresh=True`
>>> ed_df = ed.pandas_to_eland(pd_df,
... 'localhost',
... 'pandas_to_eland',
... es_if_exists="replace",
... es_refresh=True)
>>> type(ed_df)
<class 'eland.dataframe.DataFrame'>
>>> ed_df
A B ... F G
0 3.141 1 ... False 1
1 3.141 1 ... False 2
2 3.141 1 ... False 3
<BLANKLINE>
[3 rows x 7 columns]
>>> ed_df.dtypes
A float64
B int64
C object
D datetime64[ns]
E float64
F bool
G int64
dtype: object
See Also
--------
eland.read_es: Create an eland.Dataframe from an Elasticsearch index
eland.eland_to_pandas: Create a pandas.Dataframe from eland.DataFrame
"""
if chunksize is None:
chunksize = DEFAULT_CHUNK_SIZE
client = Client(es_client)
mapping = FieldMappings._generate_es_mappings(pd_df, es_geo_points)
# If table exists, check if_exists parameter
if client.index_exists(index=es_dest_index):
if es_if_exists == "fail":
raise ValueError(
f"Could not create the index [{es_dest_index}] because it "
f"already exists. "
f"Change the if_exists parameter to "
f"'append' or 'replace' data.")
elif es_if_exists == "replace":
client.index_delete(index=es_dest_index)
client.index_create(index=es_dest_index, body=mapping)
# elif if_exists == "append":
# TODO validate mapping are compatible
else:
client.index_create(index=es_dest_index, body=mapping)
# Now add data
actions = []
n = 0
for row in pd_df.iterrows():
if es_dropna:
values = row[1].dropna().to_dict()
else:
values = row[1].to_dict()
if use_pandas_index_for_es_ids:
# Use index as _id
id = row[0]
# Use integer as id field for repeatable results
action = {
"_index": es_dest_index,
"_source": values,
"_id": str(id)
}
else:
action = {"_index": es_dest_index, "_source": values}
actions.append(action)
n = n + 1
if n % chunksize == 0:
client.bulk(actions, refresh=es_refresh)
actions = []
client.bulk(actions, refresh=es_refresh)
ed_df = DataFrame(client, es_dest_index)
return ed_df