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Fix README so that copy/pastes work without warnings (#584)

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Quentin Pradet 2023-09-05 11:56:25 +04:00 committed by GitHub
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@ -114,15 +114,15 @@ or a string containing the host to connect to:
```python
import eland as ed
# Connecting to an Elasticsearch instance running on 'localhost:9200'
df = ed.DataFrame("localhost:9200", es_index_pattern="flights")
# Connecting to an Elasticsearch instance running on 'http://localhost:9200'
df = ed.DataFrame("http://localhost:9200", es_index_pattern="flights")
# Connecting to an Elastic Cloud instance
from elasticsearch import Elasticsearch
es = Elasticsearch(
cloud_id="cluster-name:...",
http_auth=("elastic", "<password>")
basic_auth=("elastic", "<password>")
)
df = ed.DataFrame(es, es_index_pattern="flights")
```
@ -143,7 +143,7 @@ without overloading your machine.
>>> import eland as ed
>>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights')
>>> df = ed.DataFrame('http://localhost:9200', 'flights')
# eland.DataFrame instance has the same API as pandas.DataFrame
# except all data is in Elasticsearch. See .info() memory usage.
@ -205,10 +205,12 @@ libraries to be serialized and used as an inference model in Elasticsearch.
➤ [Read more about Machine Learning in Elasticsearch](https://www.elastic.co/guide/en/machine-learning/current/ml-getting-started.html)
```python
>>> from sklearn import datasets
>>> from xgboost import XGBClassifier
>>> from eland.ml import MLModel
# Train and exercise an XGBoost ML model locally
>>> training_data = datasets.make_classification(n_features=5)
>>> xgb_model = XGBClassifier(booster="gbtree")
>>> xgb_model.fit(training_data[0], training_data[1])
@ -217,7 +219,7 @@ libraries to be serialized and used as an inference model in Elasticsearch.
# Import the model into Elasticsearch
>>> es_model = MLModel.import_model(
es_client="localhost:9200",
es_client="http://localhost:9200",
model_id="xgb-classifier",
model=xgb_model,
feature_names=["f0", "f1", "f2", "f3", "f4"],