diff --git a/eland/dataframe.py b/eland/dataframe.py
index a7de746..036fcfc 100644
--- a/eland/dataframe.py
+++ b/eland/dataframe.py
@@ -463,7 +463,6 @@ class DataFrame(NDFrame):
"quotechar": quotechar,
"line_terminator": line_terminator,
"chunksize": chunksize,
- "tupleize_cols": tupleize_cols,
"date_format": date_format,
"doublequote": doublequote,
"escapechar": escapechar,
@@ -552,7 +551,7 @@ class DataFrame(NDFrame):
# 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, compat.string_types):
+ if isinstance(func, str):
# wrap in list
func = [func]
return self._query_compiler.aggs(func)
diff --git a/eland/mappings.py b/eland/mappings.py
index d76aa1b..c07931c 100644
--- a/eland/mappings.py
+++ b/eland/mappings.py
@@ -290,7 +290,7 @@ class Mappings:
return es_dtype
@staticmethod
- def _generate_es_mappings(dataframe):
+ def _generate_es_mappings(dataframe, geo_points=None):
"""Given a pandas dataframe, generate the associated Elasticsearch mapping
Parameters
@@ -325,7 +325,10 @@ class Mappings:
mappings = {}
mappings['properties'] = {}
for column_name, dtype in dataframe.dtypes.iteritems():
- es_dtype = Mappings._pd_dtype_to_es_dtype(dtype)
+ if geo_points is not None and column_name in geo_points:
+ es_dtype = 'geo_point'
+ else:
+ es_dtype = Mappings._pd_dtype_to_es_dtype(dtype)
mappings['properties'][column_name] = {}
mappings['properties'][column_name]['type'] = es_dtype
diff --git a/eland/plotting.py b/eland/plotting.py
index cf5de53..c546813 100644
--- a/eland/plotting.py
+++ b/eland/plotting.py
@@ -3,13 +3,14 @@ import numpy as np
import pandas.core.common as com
from pandas.core.dtypes.generic import (
ABCIndexClass)
+from pandas.plotting._matplotlib.tools import _flatten, _set_ticks_props, _subplots
def ed_hist_frame(ed_df, column=None, by=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
sharey=False, figsize=None, layout=None, bins=10, **kwds):
"""
- Derived from pandas.plotting._core.hist_frame 0.24.2
+ Derived from pandas.plotting._core.hist_frame 0.24.2 - TODO update to 0.25.1
Ideally, we'd call hist_frame directly with histogram data,
but weights are applied to ALL series. For example, we can
@@ -29,8 +30,6 @@ def ed_hist_frame(ed_df, column=None, by=None, grid=True, xlabelsize=None,
# Start with empty pandas data frame derived from
ed_df_bins, ed_df_weights = ed_df._hist(num_bins=bins)
- _raise_if_no_mpl()
- _converter._WARN = False
if by is not None:
raise NotImplementedError("TODO")
"""
diff --git a/eland/tests/dataframe/test_iloc_pytest.py b/eland/tests/dataframe/test_iloc_pytest.py
index 0b8d23c..256a104 100644
--- a/eland/tests/dataframe/test_iloc_pytest.py
+++ b/eland/tests/dataframe/test_iloc_pytest.py
@@ -24,31 +24,22 @@ class TestDataFrameiLoc(TestData):
pd_iloc1= pd_flights.iloc[[0]]
pd_iloc2= pd_flights.iloc[[0, 1]]
pd_iloc3 = pd_flights.iloc[:3]
- pd_iloc4 = pd_flights.iloc[[True, False, True]]
pd_iloc5 = pd_flights.iloc[0, 1]
pd_iloc6 = pd_flights.iloc[[0, 2], [1, 3]]
pd_iloc7 = pd_flights.iloc[1:3, 0:3]
- pd_iloc8 = pd_flights.iloc[:, [True, False, True, False]]
- pd_iloc9 = pd_flights.iloc[[True, False, True, False]]
ed_iloc0 = ed_flights.iloc[0]
ed_iloc1 = ed_flights.iloc[[0]]
ed_iloc2 = ed_flights.iloc[[0, 1]]
ed_iloc3 = ed_flights.iloc[:3]
- ed_iloc4 = ed_flights.iloc[[True, False, True]]
ed_iloc5 = ed_flights.iloc[0, 1]
ed_iloc6 = ed_flights.iloc[[0, 2], [1, 3]]
ed_iloc7 = ed_flights.iloc[1:3, 0:3]
- ed_iloc8 = ed_flights.iloc[:, [True, False, True, False]]
- ed_iloc9 = ed_flights.iloc[[True, False, True, False]]
#assert_pandas_eland_frame_equal(pd_iloc0, ed_iloc0) # pd_iloc0 is Series
assert_pandas_eland_frame_equal(pd_iloc1, ed_iloc1)
assert_pandas_eland_frame_equal(pd_iloc2, ed_iloc2)
assert_pandas_eland_frame_equal(pd_iloc3, ed_iloc3)
- assert_pandas_eland_frame_equal(pd_iloc4, ed_iloc4)
#assert_pandas_eland_frame_equal(pd_iloc5, ed_iloc5) # pd_iloc5 is numpy_bool
assert_pandas_eland_frame_equal(pd_iloc6, ed_iloc6)
assert_pandas_eland_frame_equal(pd_iloc7, ed_iloc7)
- assert_pandas_eland_frame_equal(pd_iloc8, ed_iloc8)
- assert_pandas_eland_frame_equal(pd_iloc9, ed_iloc9)
diff --git a/eland/tests/dataframe/test_reviews_pytest.py b/eland/tests/dataframe/test_reviews_pytest.py
deleted file mode 100644
index 5c7d789..0000000
--- a/eland/tests/dataframe/test_reviews_pytest.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# File called _pytest for PyCharm compatability
-
-import gzip
-
-import pandas as pd
-
-import eland as ed
-from eland.tests.common import TestData
-
-
-class TestDataFrameReviews(TestData):
-
- def test_explore(self):
- ed_reviews = ed.DataFrame('localhost', 'anonreviews')
-
- print(ed_reviews.head())
- print(ed_reviews.describe())
- print(ed_reviews.info())
- print(ed_reviews.hist(column="rating", bins=5))
- # print(ed_reviews.head().info_es())
-
- def test_review(self):
- csv_handle = gzip.open('../anonreviews.csv.gz')
-
- reviews = pd.read_csv(csv_handle)
-
- reviews['date'] = pd.to_datetime(reviews['date'])
-
- g = reviews.groupby('reviewerId')
-
- print(g.describe())
diff --git a/eland/tests/dataframe/test_to_csv_pytest.py b/eland/tests/dataframe/test_to_csv_pytest.py
index b2b0911..d916c18 100644
--- a/eland/tests/dataframe/test_to_csv_pytest.py
+++ b/eland/tests/dataframe/test_to_csv_pytest.py
@@ -3,20 +3,23 @@
import pandas as pd
from eland.tests.common import TestData
+from eland.tests.common import ROOT_DIR
from pandas.util.testing import (assert_equal, assert_frame_equal)
import ast
+
class TestDataFrameToCSV(TestData):
def test_to_csv_head(self):
+ results_file = ROOT_DIR + '/dataframe/results/test_to_csv_head.csv'
+
ed_flights = self.ed_flights().head()
pd_flights = self.pd_flights().head()
-
- ed_flights.to_csv('results/test_to_csv_head.csv')
+ ed_flights.to_csv(results_file)
# Converting back from csv is messy as pd_flights is created from a json file
- pd_from_csv = pd.read_csv('results/test_to_csv_head.csv', index_col=0, converters={
+ pd_from_csv = pd.read_csv(results_file, index_col=0, converters={
'DestLocation': lambda x: ast.literal_eval(x),
'OriginLocation': lambda x: ast.literal_eval(x)})
pd_from_csv.index = pd_from_csv.index.map(str)
@@ -25,13 +28,15 @@ class TestDataFrameToCSV(TestData):
assert_frame_equal(pd_flights, pd_from_csv)
def test_to_csv_full(self):
+ results_file = ROOT_DIR + '/dataframe/results/test_to_csv_full.csv'
+
# Test is slow as it's for the full dataset, but it is useful as it goes over 10000 docs
ed_flights = self.ed_flights()
pd_flights = self.pd_flights()
- ed_flights.to_csv('results/test_to_csv_full.csv')
+ ed_flights.to_csv(results_file)
# Converting back from csv is messy as pd_flights is created from a json file
- pd_from_csv = pd.read_csv('results/test_to_csv_full.csv', index_col=0, converters={
+ pd_from_csv = pd.read_csv(results_file, index_col=0, converters={
'DestLocation': lambda x: ast.literal_eval(x),
'OriginLocation': lambda x: ast.literal_eval(x)})
pd_from_csv.index = pd_from_csv.index.map(str)
diff --git a/eland/tests/demo_day_20190815.ipynb b/eland/tests/demo_day_20190815.ipynb
index 7c785bd..878d00d 100644
--- a/eland/tests/demo_day_20190815.ipynb
+++ b/eland/tests/demo_day_20190815.ipynb
@@ -7144,7 +7144,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.9"
+ "version": "3.6.8"
}
},
"nbformat": 4,
diff --git a/eland/tests/operators/test_operators_pytest.py b/eland/tests/operators/test_operators_pytest.py
index bab87ca..81c329c 100644
--- a/eland/tests/operators/test_operators_pytest.py
+++ b/eland/tests/operators/test_operators_pytest.py
@@ -1,5 +1,5 @@
# -*- coding: UTF-8 -*-
-from eland.operators import *
+from eland.filter import *
class TestOperators():
@@ -21,11 +21,6 @@ class TestOperators():
'script': {'script': {'inline': 'doc["num1"].value > params.param1', 'params': {'param1': 5}}}}
assert IsIn('ids', [1, 2, 3]).build() == {'ids': {'values': [1, 2, 3]}}
- def test_and_none(self):
- exp = None
- exp = exp & Less('b', 3)
- print(exp.build())
-
def test_and_filter1(self):
exp = GreaterEqual('a', 2) & Less('b', 3)
assert exp.build() == {'bool': {'must': [{'range': {'a': {'gte': 2}}}, {'range': {'b': {'lt': 3}}}]}}
diff --git a/eland/tests/pivot_review_data_pandas.ipynb b/eland/tests/pivot_review_data_pandas.ipynb
deleted file mode 100644
index e412c96..0000000
--- a/eland/tests/pivot_review_data_pandas.ipynb
+++ /dev/null
@@ -1,1382 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Pivot review data in pandas\n",
- "\n",
- "This notebook shows how data can be pivoted by python [pandas](https://pandas.pydata.org/) to reveal insights into the behaviour of reviewers. The use case and data is from Mark Harwood's talk on [entity-centric indexing](https://www.elastic.co/videos/entity-centric-indexing-mark-harwood).\n",
- "\n",
- "An alternative version of this notebook uses the [Elastic data frames](https://www.elastic.co/guide/en/elastic-stack-overview/master/ml-dataframes.html) to create the same results."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "pycharm": {
- "is_executing": false
- }
- },
- "outputs": [],
- "source": [
- "import gzip\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from pandas.plotting import scatter_matrix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Read data to pandas DataFrame"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "pycharm": {
- "is_executing": false
- }
- },
- "outputs": [],
- "source": [
- "csv_handle = gzip.open('./anonreviews.csv.gz')\n",
- "\n",
- "reviews = pd.read_csv(csv_handle)\n",
- "\n",
- "reviews['date'] = pd.to_datetime(reviews['date'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Explore data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "pycharm": {
- "is_executing": false
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " reviewerId | \n",
- " vendorId | \n",
- " rating | \n",
- " date | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 5 | \n",
- " 2006-04-07 17:08:00 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " 1 | \n",
- " 1 | \n",
- " 5 | \n",
- " 2006-05-04 12:16:00 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " 2 | \n",
- " 2 | \n",
- " 4 | \n",
- " 2006-04-21 12:26:00 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " 3 | \n",
- " 3 | \n",
- " 5 | \n",
- " 2006-04-18 15:48:00 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " 3 | \n",
- " 4 | \n",
- " 5 | \n",
- " 2006-04-18 15:49:00 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " reviewerId | \n",
- " vendorId | \n",
- " rating | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | count | \n",
- " 578805.000000 | \n",
- " 578805.000000 | \n",
- " 578805.000000 | \n",
- "
\n",
- " \n",
- " | mean | \n",
- " 174124.098437 | \n",
- " 60.645267 | \n",
- " 4.679671 | \n",
- "
\n",
- " \n",
- " | std | \n",
- " 116951.972209 | \n",
- " 54.488053 | \n",
- " 0.800891 | \n",
- "
\n",
- " \n",
- " | min | \n",
- " 0.000000 | \n",
- " 0.000000 | \n",
- " 0.000000 | \n",
- "
\n",
- " \n",
- " | 25% | \n",
- " 70043.000000 | \n",
- " 20.000000 | \n",
- " 5.000000 | \n",
- "
\n",
- " \n",
- " | 50% | \n",
- " 161052.000000 | \n",
- " 44.000000 | \n",
- " 5.000000 | \n",
- "
\n",
- " \n",
- " | 75% | \n",
- " 272697.000000 | \n",
- " 83.000000 | \n",
- " 5.000000 | \n",
- "
\n",
- " \n",
- " | max | \n",
- " 400140.000000 | \n",
- " 246.000000 | \n",
- " 5.000000 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#align = {str} 'center'\n",
- "#bottom = {ndarray} [0. 0. 0. 0. 0.]\n",
- "#height = {ndarray} [5.30000e+02 1.18700e+04 1.03770e+04 1.49710e+04 5.41057e+05]\n",
- "#kwargs = {dict} : {'log': False, 'color': '#1f77b4'}\n",
- "#self = {AxesSubplot} AxesSubplot(0.125,0.125;0.775x0.755)\n",
- "#width = {ndarray} [1. 1. 1. 1. 1.]\n",
- "#x = {ndarray} [0.5 1.5 2.5 3.5 4.5] \n",
- "hist = reviews.hist(column=\"rating\", bins = 5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0 1 2 3 4]\n"
- ]
- }
- ],
- "source": [
- "bins = [530.0, 11870.0, 10377.0, 14971.0, 541057.0]\n",
- "objects = range(5)\n",
- "y_pos = np.arange(len(objects))\n",
- "print(y_pos)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.bar(y_pos, bins, align='edge', width=1.0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "#### Typically how many vendors does a reviewer review? (mainly one or two)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "IOPub data rate exceeded.\n",
- "The notebook server will temporarily stop sending output\n",
- "to the client in order to avoid crashing it.\n",
- "To change this limit, set the config variable\n",
- "`--NotebookApp.iopub_data_rate_limit`.\n",
- "\n",
- "Current values:\n",
- "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
- "NotebookApp.rate_limit_window=3.0 (secs)\n",
- "\n"
- ]
- }
- ],
- "source": [
- "g = reviews.groupby('reviewerId')['vendorId'].nunique()\n",
- "\n",
- "print(type(g))\n",
- "\n",
- "print(reviews.groupby('reviewerId').indices)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(reviews.groupby('reviewerId')['vendorId'].nunique(), '.')\n",
- "plt.xlabel('reviewerId')\n",
- "plt.ylabel('dc(vendorId)')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Aggregate and Pivot data\n",
- "\n",
- "Pivot data so we get summaries for each reviewer."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "aggregations = {\n",
- " 'rating':'mean',\n",
- " 'vendorId':'nunique',\n",
- " 'reviewerId':'count'\n",
- "}\n",
- "\n",
- "grouped = reviews.groupby('reviewerId').agg(aggregations)\n",
- "grouped.columns=['avg_rating', 'dc_vendorId', 'count']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
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\n",
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- " | \n",
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- " count | \n",
- "
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- " \n",
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- " | \n",
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- " 5 | \n",
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- " \n",
- " | 3 | \n",
- " 4.571429 | \n",
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- " 7 | \n",
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- " \n",
- " | 4 | \n",
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- " | \n",
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- " dc_vendorId | \n",
- " count | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | count | \n",
- " 400141.000000 | \n",
- " 400141.000000 | \n",
- " 400141.000000 | \n",
- "
\n",
- " \n",
- " | mean | \n",
- " 4.643846 | \n",
- " 1.357017 | \n",
- " 1.446503 | \n",
- "
\n",
- " \n",
- " | std | \n",
- " 0.813586 | \n",
- " 0.989413 | \n",
- " 1.541802 | \n",
- "
\n",
- " \n",
- " | min | \n",
- " 0.000000 | \n",
- " 1.000000 | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " | 25% | \n",
- " 5.000000 | \n",
- " 1.000000 | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " | 50% | \n",
- " 5.000000 | \n",
- " 1.000000 | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " | 75% | \n",
- " 5.000000 | \n",
- " 1.000000 | \n",
- " 1.000000 | \n",
- "
\n",
- " \n",
- " | max | \n",
- " 5.000000 | \n",
- " 32.000000 | \n",
- " 168.000000 | \n",
- "
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- " \n",
- "
\n",
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
- "\n",
- "scatter_matrix(grouped)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Find 'haters'\n",
- "\n",
- "Reviewers that give more than five zero star reviews to one vendor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
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- " | 12340 | \n",
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- " 2006-04-05 15:38:00 | \n",
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- " \n",
- " | 12341 | \n",
- " 10392 | \n",
- " 122 | \n",
- " 0 | \n",
- " 2006-04-06 09:24:00 | \n",
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- " avg_rating | \n",
- " dc_vendorId | \n",
- " count | \n",
- "
\n",
- " \n",
- " | reviewerId | \n",
- " | \n",
- " | \n",
- " | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 183751 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 73 | \n",
- "
\n",
- " \n",
- " | 260225 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 69 | \n",
- "
\n",
- " \n",
- " | 205864 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 35 | \n",
- "
\n",
- " \n",
- " | 345080 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 23 | \n",
- "
\n",
- " \n",
- " | 179944 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 22 | \n",
- "
\n",
- " \n",
- " | 345082 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 21 | \n",
- "
\n",
- " \n",
- " | 345081 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 20 | \n",
- "
\n",
- " \n",
- " | 345068 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 20 | \n",
- "
\n",
- " \n",
- " | 345069 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 19 | \n",
- "
\n",
- " \n",
- " | 345086 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 18 | \n",
- "
\n",
- " \n",
- " | 345083 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 18 | \n",
- "
\n",
- " \n",
- " | 345070 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 18 | \n",
- "
\n",
- " \n",
- " | 345085 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 17 | \n",
- "
\n",
- " \n",
- " | 345084 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 17 | \n",
- "
\n",
- " \n",
- " | 264635 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 13 | \n",
- "
\n",
- " \n",
- " | 321206 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 12 | \n",
- "
\n",
- " \n",
- " | 12539 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 11 | \n",
- "
\n",
- " \n",
- " | 159035 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 10 | \n",
- "
\n",
- " \n",
- " | 114661 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 9 | \n",
- "
\n",
- " \n",
- " | 39655 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 8 | \n",
- "
\n",
- " \n",
- " | 22515 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 7 | \n",
- "
\n",
- " \n",
- " | 180082 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 7 | \n",
- "
\n",
- " \n",
- " | 58447 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 7 | \n",
- "
\n",
- " \n",
- " | 180085 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 168143 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 160018 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 75010 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 35048 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 30474 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 28814 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- " | 393237 | \n",
- " 5.0 | \n",
- " 1 | \n",
- " 6 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " reviewerId | \n",
- " vendorId | \n",
- " rating | \n",
- " date | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 261469 | \n",
- " 183751 | \n",
- " 190 | \n",
- " 5 | \n",
- " 2006-09-22 16:36:00 | \n",
- "
\n",
- " \n",
- " | 261470 | \n",
- " 183751 | \n",
- " 190 | \n",
- " 5 | \n",
- " 2006-09-22 16:36:00 | \n",
- "
\n",
- " \n",
- " | 261471 | \n",
- " 183751 | \n",
- " 190 | \n",
- " 5 | \n",
- " 2006-09-22 16:35:00 | \n",
- "
\n",
- " \n",
- " | 261474 | \n",
- " 183751 | \n",
- " 190 | \n",
- " 5 | \n",
- " 2006-09-22 15:53:00 | \n",
- "
\n",
- " \n",
- " | 261475 | \n",
- " 183751 | \n",
- " 190 | \n",
- " 5 | \n",
- " 2006-09-22 15:53:00 | \n",
- "
\n",
- " \n",
- "
\n",
- "