diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 7df75a4ea..6ff881cba 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -5,29 +5,214 @@ ### Introduction -In this final lesson on Regression, one of the basic 'classic' ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict categories. +In this final lesson on Regression, one of the basic 'classic' ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict binary categories. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not? In this lesson, you will learn: - A new library for data visualization - Techniques for Logistic Regression + +Deepen your understanding of working with this type of Regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) ## Prerequisite -Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one small category that we can work with: Color. Let's build a Logistic Regression model to predict that, given a pumpkin's size, what color it will be (orange or white). There is also a 'striped' category in our dataset but there are few instances, so we will not use it. +Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one small category that we can work with: Color. Let's build a Logistic Regression model to predict that, given some variables, what color a given pumpkin will be (orange ๐ŸŽƒ or white ๐Ÿ‘ป). + +For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway. > ๐ŸŽƒ Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking! -### Preparation -We have loaded up the [starter notebook](./notebook.ipynb) with pumpkin data once again and cleaned it so as to preserve a dataset containing Color and Item Size. +## About Logistic Regression +Logistic Regression differs from Linear Regression, which you learned about previously, in a few important ways. +### Binary Classification +Logistic Regression does not offer the same features as Linear Regression. The former offers a prediction about a binary category ("orange or not orange") whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of month, how much its price will rise. +### Other Classifications ---- -## ๐Ÿš€Challenge +There are other types of Logistic Regression, including Multinomial and Ordinal. Multinomial involves having more than one categories - "Orange, White, and Striped". Ordinal involves ordered categories, such as if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl). + +### It's Still Linear + +Even though this type of Regression is all about category predictions, it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not. + +### Variables DO NOT have to correlate + +Remember how Linear Regression worked better with more correlated variables? Logistic Regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations. +### You Need a Lot of Clean Data + +Logistic Regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind. + +โœ… Think about the types of data that would lend themselves well to Logistic Regression + +## Tidy the Data + +First, clean the data a bit, dropping null values and selecting only some of the columns: + +```python +from sklearn.preprocessing import LabelEncoder + +new_columns = ['Color','Origin','Item Size','Variety','City Name','Package'] + +new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1) + +new_pumpkins.dropna(inplace=True) + +new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform) +``` + +You can always take a peek at your new dataframe: + +```python +new_pumpkins.info +``` +### Visualization + +By now you have loaded up the [starter notebook](./notebook.ipynb) with pumpkin data once again and cleaned it so as to preserve a dataset containing a few variables, including Color. Let's visualize the dataframe in the notebook using a different library: [Seaborn](https://seaborn.pydata.org/index.html), which is built on Matplotlib which we used earlier. Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each point in a side-by side grid. + +```python +import seaborn as sns + +g = sns.PairGrid(new_pumpkins) +g.map(sns.scatterplot) +``` + +![A grid of visualized data](./images/grid.png) + +By observing data side-by-side, you can see how the Color data relates to the other columns. + +โœ… Given this scatterplot grid, what are some interesting explorations you can envision? + +Since Color is a binary category (Orange or Not), it's called 'categorical data' and needs 'a more [specialized approach](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) to visualization'. There are other ways to visualize the relationship of this category with other variables. You can visualize variables side-by-side with Seaborn plots. Try a 'swarm' plot to show the distribution of values: + +```python +sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins) +``` + +![A swarm of visualized data](./images/swarm.png) + +A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'. + +```python +sns.catplot(x="Color", y="Item Size", + kind="violin", data=new_pumpkins) +``` +![a violin type chart](images/violin.png) + +โœ… Try creating this plot, and other Seaborn plots, using other variables. + +Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore Logistic Regression to determine a given pumpkin's likely color. + +> infographic here + +> **๐Ÿงฎ Show Me The Math** +> +> Remember how Linear Regression often used ordinary least squares to arrive at a value? Logistic Regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://en.wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus: +> +> ![logistic function](images/logistic.png) +> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class '1' of the binary choice. If not, it will be classified as '0'. + +## Build your model -Add a challenge for students to work on collaboratively in class to enhance the project +Building a model to find these binary classification is surprisingly straightforward in Scikit-Learn. -Optional: add a screenshot of the completed lesson's UI if appropriate +Select the variables you want to use in your classification model and split the training and test sets: + +```python +from sklearn.model_selection import train_test_split + +Selected_features = ['Origin','Item Size','Variety','City Name','Package'] + +X = new_pumpkins[Selected_features] +y = new_pumpkins['Color'] + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) + +``` + +Now you can train your model and print out its result: + +```python +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score, classification_report +from sklearn.linear_model import LogisticRegression + +model = LogisticRegression() +model.fit(X_train, y_train) +predictions = fit.predict(X_test) + +print(classification_report(y_test, predictions)) +print('Predicted labels: ', predictions) +print('Accuracy: ', accuracy_score(y_test, predictions)) +``` + +Take a look at your model's scoreboard. It's not too bad, considering you have only about 1000 rows of data: + +``` + precision recall f1-score support + + 0 0.85 0.95 0.90 166 + 1 0.38 0.15 0.22 33 + + accuracy 0.82 199 + macro avg 0.62 0.55 0.56 199 +weighted avg 0.77 0.82 0.78 199 + +Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 + 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 1 0 1 0 0 1 0 0 0 1 0] +``` + +Let's unpack some of those [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report): + +๐ŸŽ“ Precision: The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled) + +๐ŸŽ“ Recall: The fraction of relevant instances that were retrieved, whether well-labeled or not + +๐ŸŽ“ f1-score: A weighted average of the precision and recall, with best being 1 and worst being 0 + +๐ŸŽ“ Support: The number of occurrences of each label retrieved + +๐ŸŽ“ Accuracy: The percentage of labels predicted accurately for a sample. + +๐ŸŽ“ Macro Avg: The calculation of the unweighted mean metrics for each label, not taking label imbalance into account. + +๐ŸŽ“ Weighted Avg: The calculation of the mean metrics for each label, taking label imbalance into account by weighting them by their support (the number of true instances for each label). + +## Visualize the ROC Curve of this Model + +This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables. + +Let's do one more visualization to see the so-called 'ROC' score: + +```python +from sklearn.metrics import roc_curve, roc_auc_score + +y_scores = model.predict_proba(X_test) +# calculate ROC curve +fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1]) +sns.lineplot([0, 1], [0, 1]) +sns.lineplot(fpr, tpr) +``` +Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly: + +![ROC](./images/ROC.png) + +Finally, use Scikit-Learn's [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) to compute the actual 'Area Under the Receiver' (AUC): + +```python +auc = roc_auc_score(y_test,y_scores[:,1]) +print(auc) +``` +The result is `0.6976998904709748`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_. + +In future lessons on classifications, you will learn how to iterate to improve your model's scores. But for now, congratulations! You've completed these regression lessons! + +--- +## ๐Ÿš€Challenge +There's a lot more to unpack regarding Logistic Regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets. ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) ## Review & Self Study diff --git a/2-Regression/4-Logistic/images/ROC.png b/2-Regression/4-Logistic/images/ROC.png new file mode 100644 index 000000000..af609a1c3 Binary files /dev/null and b/2-Regression/4-Logistic/images/ROC.png differ diff --git a/2-Regression/4-Logistic/images/grid.png b/2-Regression/4-Logistic/images/grid.png new file mode 100644 index 000000000..48f6b6608 Binary files /dev/null and b/2-Regression/4-Logistic/images/grid.png differ diff --git a/2-Regression/4-Logistic/images/logistic.png b/2-Regression/4-Logistic/images/logistic.png new file mode 100644 index 000000000..ee2ed47df Binary files /dev/null and b/2-Regression/4-Logistic/images/logistic.png differ diff --git a/2-Regression/4-Logistic/images/swarm.png b/2-Regression/4-Logistic/images/swarm.png new file mode 100644 index 000000000..d802deb2c Binary files /dev/null and b/2-Regression/4-Logistic/images/swarm.png differ diff --git a/2-Regression/4-Logistic/images/violin.png b/2-Regression/4-Logistic/images/violin.png new file mode 100644 index 000000000..3fe1e646a Binary files /dev/null and b/2-Regression/4-Logistic/images/violin.png differ diff --git a/2-Regression/4-Logistic/notebook.ipynb b/2-Regression/4-Logistic/notebook.ipynb index 37b121694..a08ea65fd 100644 --- a/2-Regression/4-Logistic/notebook.ipynb +++ b/2-Regression/4-Logistic/notebook.ipynb @@ -73,7 +73,6 @@ ], "source": [ "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", @@ -81,121 +80,6 @@ "pumpkins.head()\n" ] }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\nInt64Index: 716 entries, 23 to 1752\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Color 716 non-null object\n 1 Variety 716 non-null object\ndtypes: object(2)\nmemory usage: 16.8+ KB\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Color 716\n", - "Variety 716\n", - "dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 45 - } - ], - "source": [ - "\n", - "new_columns = ['Variety', 'Color']\n", - "\n", - "\n", - "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", - "\n", - "\n", - "new_pumpkins = pd.DataFrame({'Color': pumpkins['Color'],'Variety': pumpkins['Variety']})\n", - "\n", - "new_pumpkins.dropna(inplace=True)\n", - "new_pumpkins.info()\n", - "\n", - "\n", - "\n", - "new_pumpkins.count()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 46 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.bar('Color','Variety',data=new_pumpkins)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Color Variety\n", - "23 0 HOWDEN TYPE\n", - "24 0 HOWDEN TYPE\n", - "25 0 HOWDEN TYPE\n", - "26 0 HOWDEN TYPE\n", - "87 0 PIE TYPE\n", - "... ... ...\n", - "1748 0 MINIATURE\n", - "1749 0 MINIATURE\n", - "1750 2 MINIATURE\n", - "1751 0 MINIATURE\n", - "1752 2 MINIATURE\n", - "\n", - "[716 rows x 2 columns]" - ], - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ColorVariety
230HOWDEN TYPE
240HOWDEN TYPE
250HOWDEN TYPE
260HOWDEN TYPE
870PIE TYPE
.........
17480MINIATURE
17490MINIATURE
17502MINIATURE
17510MINIATURE
17522MINIATURE
\n

716 rows ร— 2 columns

\n
" - }, - "metadata": {}, - "execution_count": 48 - } - ], - "source": [ - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)\n", - "\n", - "new_pumpkins\n", - "\n", - "#print(new_pumpkins['Color'].corr(new_pumpkins['Variety']))\n" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/2-Regression/4-Logistic/solution/notebook.ipynb b/2-Regression/4-Logistic/solution/notebook.ipynb index c6ee6470e..1714ea849 100644 --- a/2-Regression/4-Logistic/solution/notebook.ipynb +++ b/2-Regression/4-Logistic/solution/notebook.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -66,7 +66,7 @@ "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
City NameTypePackageVarietySub VarietyGradeDateLow PriceHigh PriceMostly Low...Unit of SaleQualityConditionAppearanceStorageCropRepackTrans ModeUnnamed: 24Unnamed: 25
0BALTIMORENaN24 inch binsNaNNaNNaN4/29/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
1BALTIMORENaN24 inch binsNaNNaNNaN5/6/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
2BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
3BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
4BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN11/5/1690.0100.090.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
\n

5 rows ร— 26 columns

\n
" }, "metadata": {}, - "execution_count": 184 + "execution_count": 7 } ], "source": [ @@ -81,11 +81,12 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", + "\n", "new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']\n", "\n", "new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", @@ -104,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -112,58 +113,57 @@ "data": { "text/plain": [ "" + "[991 rows x 6 columns]>" ] }, "metadata": {}, - "execution_count": 186 + "execution_count": 11 } ], "source": [ - "new_pumpkins\n", "new_pumpkins.info" ] }, { "source": [ - "Working with Item Size to Color, create a scatterplot " + "Working with Item Size to Color, create a scatterplot using Seaborn" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, - "execution_count": 187 + "execution_count": 13 }, { "output_type": "display_data", "data": { "text/plain": "
", - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -179,25 +179,34 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.swarmplot(x=\"Color\", y=\"Item Size\", data=new_pumpkins)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, - "execution_count": 188 + "execution_count": 10 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" @@ -205,61 +214,78 @@ } ], "source": [ - "sns.catplot(x=\"Color\", y=\"Item Size\", kind=\"swarm\", data=new_pumpkins)" + "sns.catplot(x=\"Color\", y=\"Item Size\",\n", + " kind=\"violin\", data=new_pumpkins)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "Selected_features = ['Origin','Item Size','Variety','City Name','Package']\n", + "\n", + "X = new_pumpkins[Selected_features]\n", + "y = new_pumpkins['Color']\n", + "\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n" ] }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 14, "metadata": {}, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 189 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } + "output_type": "error", + "ename": "NameError", + "evalue": "name 'fit' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'fit' is not defined" + ] } ], "source": [ - "sns.catplot(x=\"Color\", y=\"Item Size\", kind=\"box\", data=new_pumpkins)" + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score, classification_report \n", + "from sklearn.linear_model import LogisticRegression\n", + "model = LogisticRegression()\n", + "model.fit(X_train, y_train)\n", + "predictions = fit.predict(X_test)\n", + "\n", + "print(classification_report(y_test, predictions))\n", + "print('Predicted labels: ', predictions)\n", + "print('Accuracy: ', accuracy_score(y_test, predictions))\n" ] }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 16, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, - "execution_count": 190 + "execution_count": 16 }, { "output_type": "display_data", "data": { - "text/plain": "
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\n" }, "metadata": { "needs_background": "light" @@ -267,8 +293,31 @@ } ], "source": [ - "sns.catplot(x=\"Color\", y=\"Item Size\",\n", - " kind=\"violin\", data=new_pumpkins)" + "from sklearn.metrics import roc_curve, roc_auc_score\n", + "\n", + "y_scores = model.predict_proba(X_test)\n", + "# calculate ROC curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n", + "sns.lineplot([0, 1], [0, 1])\n", + "sns.lineplot(fpr, tpr)" + ] + }, + { + "source": [ + "auc = roc_auc_score(y_test,y_scores[:,1])\n", + "print(auc)" + ], + "cell_type": "code", + "metadata": {}, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.6976998904709748\n" + ] + } ] }, { diff --git a/2-Regression/README.md b/2-Regression/README.md index 7f0ab9921..9d1741219 100644 --- a/2-Regression/README.md +++ b/2-Regression/README.md @@ -18,4 +18,4 @@ In this section, you will get set up to begin machine learning tasks, including "ML with Regression" was written with โ™ฅ๏ธ by [Jen Looper](https://twitter.com/jenlooper) -The pumpkin dataset is suggested by [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) and its data is sourced from the [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distributed by the United States Department of Agriculture. This data is in the public domain. +The pumpkin dataset is suggested by [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) and its data is sourced from the [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distributed by the United States Department of Agriculture. We have added some points around color based on variety to normalize the distribution. This data is in the public domain. diff --git a/2-Regression/data/US-pumpkins.csv b/2-Regression/data/US-pumpkins.csv index 21c5f2e74..a1955e5b0 100644 --- a/2-Regression/data/US-pumpkins.csv +++ b/2-Regression/data/US-pumpkins.csv @@ -1,27 +1,27 @@ ๏ปฟCity Name,Type,Package,Variety,Sub Variety,Grade,Date,Low Price,High Price,Mostly Low,Mostly High,Origin,Origin District,Item Size,Color,Environment,Unit of Sale,Quality,Condition,Appearance,Storage,Crop,Repack,Trans Mode,, BALTIMORE,,24 inch bins,,,,4/29/17,270,280,270,280,MARYLAND,,lge,,,,,,,,,E,,, BALTIMORE,,24 inch bins,,,,5/6/17,270,280,270,280,MARYLAND,,lge,,,,,,,,,E,,, -BALTIMORE,,24 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,DELAWARE,,med,,,,,,,,,N,,, -BALTIMORE,,24 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,VIRGINIA,,med,,,,,,,,,N,,, -BALTIMORE,,24 inch bins,HOWDEN TYPE,,,11/5/16,90,100,90,100,MARYLAND,,lge,,,,,,,,,N,,, -BALTIMORE,,24 inch bins,HOWDEN TYPE,,,11/12/16,90,100,90,100,MARYLAND,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/24/16,160,170,160,170,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,PENNSYLVANIA,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,170,160,170,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,160,160,160,PENNSYLVANIA,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,170,160,170,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,160,160,160,PENNSYLVANIA,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/15/16,150,160,150,160,MARYLAND,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/15/16,150,170,150,170,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/22/16,150,160,150,160,MARYLAND,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/22/16,140,160,140,160,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/29/16,100,160,100,160,MARYLAND,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,160,130,160,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,130,130,130,MARYLAND,,sml,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/5/16,120,130,120,130,MARYLAND,,xlge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/5/16,100,130,100,130,MARYLAND,,lge,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/5/16,90,100,90,100,MARYLAND,,med,,,,,,,,,N,,, -BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/12/16,100,120,100,100,MARYLAND,,lge,,,,,,,,,N,,, +BALTIMORE,,24 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,DELAWARE,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,24 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,VIRGINIA,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,24 inch bins,HOWDEN TYPE,,,11/5/16,90,100,90,100,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,24 inch bins,HOWDEN TYPE,,,11/12/16,90,100,90,100,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/24/16,160,170,160,170,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,170,160,170,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,160,160,160,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,170,160,170,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,160,160,160,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/15/16,150,160,150,160,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/15/16,150,170,150,170,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/22/16,150,160,150,160,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/22/16,140,160,140,160,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/29/16,100,160,100,160,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,160,130,160,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,130,130,130,MARYLAND,,sml,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/5/16,120,130,120,130,MARYLAND,,xlge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/5/16,100,130,100,130,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/5/16,90,100,90,100,MARYLAND,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,36 inch bins,HOWDEN TYPE,,,11/12/16,100,120,100,100,MARYLAND,,lge,ORANGE,,,,,,,,N,,, BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,160,160,160,MARYLAND,,med-lge,ORANGE,,,,,,,,N,,, BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,160,160,160,MARYLAND,,med-lge,ORANGE,,,,,,,,N,,, BALTIMORE,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,160,150,160,MARYLAND,,lge,ORANGE,,,,,,,,N,,, @@ -69,81 +69,81 @@ BALTIMORE,,24 inch bins,FAIRYTALE,,,11/12/16,200,200,200,200,MARYLAND,,xlge,,,,, BALTIMORE,,24 inch bins,FAIRYTALE,,,11/19/16,200,200,200,200,MARYLAND,,xlge,,,,,,,,,N,,, BALTIMORE,,24 inch bins,FAIRYTALE,,,11/19/16,200,230,200,230,VIRGINIA,,xlge,,,,,,,,,N,,, BALTIMORE,,24 inch bins,FAIRYTALE,,,11/26/16,210,210,210,210,VIRGINIA,,xlge,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/24/16,15,15,15,15,DELAWARE,,med,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/24/16,18,18,18,18,DELAWARE,,sml,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/1/16,18,18,18,18,DELAWARE,,sml,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/1/16,17,17,17,17,OHIO,,med,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,15,15,15,15,DELAWARE,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,18,18,18,18,DELAWARE,,sml,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,17,17,17,17,OHIO,,med,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,17,18.5,17,18.5,PENNSYLVANIA,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,15,15,15,15,DELAWARE,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,17,17,17,17,OHIO,,med,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,17,18.5,17,18.5,PENNSYLVANIA,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,15,15,15,15,DELAWARE,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,17,17,17,17,OHIO,,med,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,17,18.5,17,18.5,PENNSYLVANIA,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,15,15,15,15,DELAWARE,,,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,16,17,16,17,OHIO,,med,,,,,,,,,N,,, -BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,16,18,16,18,PENNSYLVANIA,,,,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/24/16,15,15,15,15,DELAWARE,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/24/16,18,18,18,18,DELAWARE,,sml,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/1/16,18,18,18,18,DELAWARE,,sml,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/1/16,17,17,17,17,OHIO,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,15,15,15,15,DELAWARE,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,18,18,18,18,DELAWARE,,sml,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,17,17,17,17,OHIO,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,17,18.5,17,18.5,PENNSYLVANIA,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,15,15,15,15,DELAWARE,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,17,17,17,17,OHIO,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,17,18.5,17,18.5,PENNSYLVANIA,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,15,15,15,15,DELAWARE,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,17,17,17,17,OHIO,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,17,18.5,17,18.5,PENNSYLVANIA,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,15,15,15,15,DELAWARE,,,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,16,17,16,17,OHIO,,med,ORANGE,,,,,,,,N,,, +BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,16,18,16,18,PENNSYLVANIA,,,ORANGE,,,,,,,,N,,, BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/16/17,18,18,18,18,MARYLAND,,med,ORANGE,,,,,,,,N,,, BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/23/17,16,16,16,16,DELAWARE,,med,ORANGE,,,,,,,,N,,, BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/23/17,18,18,18,18,MARYLAND,,med,ORANGE,,,,,,,,N,,, BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/23/17,16,16,16,16,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/30/17,16,16,16,16,DELAWARE,,med,ORANGE,,,,,,,,N,,, BALTIMORE,,1 1/9 bushel cartons,PIE TYPE,,,9/30/17,16,16,16,16,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, -BALTIMORE,,24 inch bins,PIE TYPE,,,9/24/16,160,160,160,160,VIRGINIA,,sml,,,,,,,,,N,,, +BALTIMORE,,24 inch bins,PIE TYPE,,,9/24/16,160,160,160,160,VIRGINIA,,sml,ORANGE,,,,,,,,N,,, BALTIMORE,,24 inch bins,PIE TYPE,,,9/23/17,200,200,200,200,MARYLAND,,sml,ORANGE,,,,,,,,N,,, BALTIMORE,,24 inch bins,PIE TYPE,,,9/30/17,190,200,190,200,MARYLAND,,sml,ORANGE,,,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/24/16,50,60,50,60,MARYLAND,,,WHITE,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/24/16,50,60,50,60,MARYLAND,,,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/24/16,50,60,50,60,VIRGINIA,,,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/1/16,50,60,50,60,MARYLAND,,,,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/24/16,50,60,50,60,MARYLAND,,,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/24/16,50,60,50,60,VIRGINIA,,,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/1/16,50,60,50,60,MARYLAND,,,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/1/16,50,60,50,60,MARYLAND,,,WHITE,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/1/16,50,60,50,60,VIRGINIA,,,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,MARYLAND,,,,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/1/16,50,60,50,60,VIRGINIA,,,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,MARYLAND,,,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,MARYLAND,,,WHITE,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,MARYLAND,,jbo,,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,MARYLAND,,jbo,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,40,60,40,60,MARYLAND,,lge,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,40,60,40,60,MARYLAND,,med,WHITE,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,VIRGINIA,,,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,VIRGINIA,,jbo,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/15/16,50,60,50,60,MARYLAND,,jbo,,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,VIRGINIA,,,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/8/16,50,60,50,60,VIRGINIA,,jbo,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/15/16,50,60,50,60,MARYLAND,,jbo,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/15/16,40,60,40,60,MARYLAND,,lge,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/15/16,40,60,40,60,MARYLAND,,med,WHITE,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/15/16,50,60,50,60,VIRGINIA,,jbo,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/22/16,50,60,50,60,MARYLAND,,jbo,,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/15/16,50,60,50,60,VIRGINIA,,jbo,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/22/16,50,60,50,60,MARYLAND,,jbo,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/22/16,40,60,40,60,MARYLAND,,lge,WHITE,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/22/16,50,60,50,60,VIRGINIA,,jbo,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/29/16,50,60,50,60,MARYLAND,,jbo,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/29/16,50,60,50,60,VIRGINIA,,jbo,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/23/17,50,50,50,50,MARYLAND,,jbo,,,EACH,,,,,,N,,, -BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/30/17,50,50,50,50,MARYLAND,,jbo,,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/22/16,50,60,50,60,VIRGINIA,,jbo,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/29/16,50,60,50,60,MARYLAND,,jbo,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,10/29/16,50,60,50,60,VIRGINIA,,jbo,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/23/17,50,50,50,50,MARYLAND,,jbo,WHITE,,EACH,,,,,,N,,, +BALTIMORE,,24 inch bins,BIG MACK TYPE,,,9/30/17,50,50,50,50,MARYLAND,,jbo,WHITE,,EACH,,,,,,N,,, BALTIMORE,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,9/16/17,160,240,160,240,MARYLAND,,,,,,,,,,,N,,, BALTIMORE,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,9/23/17,240,240,240,240,MARYLAND,,,,,,,,,,,N,,, BALTIMORE,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,9/30/17,240,240,240,240,MARYLAND,,,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,9/24/16,18,18,18,18,MARYLAND,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,9/24/16,18,18,18,18,MARYLAND,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,9/24/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/1/16,18,18,18,18,MARYLAND,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/1/16,18,18,18,18,MARYLAND,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/1/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/1/16,17,17,17,17,OHIO,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/1/16,17,17,17,17,OHIO,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,15,18,15,18,MARYLAND,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,16,17,16,17,OHIO,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,16,18,16,18,VIRGINIA,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,15,15,15,15,MARYLAND,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,15,18,15,18,MARYLAND,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,16,17,16,17,OHIO,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/8/16,16,18,16,18,VIRGINIA,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,16,17,16,17,OHIO,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,16,18,16,18,VIRGINIA,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,15,15,15,15,MARYLAND,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,16,17,16,17,OHIO,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/15/16,16,18,16,18,VIRGINIA,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,16,17,16,17,OHIO,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,16,18,16,18,VIRGINIA,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,15,17,15,17,MARYLAND,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,16,17,16,17,OHIO,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,16,18,16,18,VIRGINIA,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,15,17,15,17,MARYLAND,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,15,15,15,15,MARYLAND,,sml,WHITE,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,16,17,16,17,OHIO,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,16,18,16,18,VIRGINIA,,sml,,,,,,,,,N,,, -BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,11/5/16,16,17,16,17,OHIO,,sml,,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,16,17,16,17,OHIO,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,16,18,16,18,VIRGINIA,,sml,WHITE,,,,,,,,N,,, +BALTIMORE,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,11/5/16,16,17,16,17,OHIO,,sml,WHITE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,,,9/16/17,18,18,18,18,MARYLAND,,,ORANGE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,,,9/23/17,18,18,18,18,MARYLAND,,,ORANGE,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,,,9/23/17,18,18,18,18,PENNSYLVANIA,,,ORANGE,,,,,,,,N,,, @@ -152,41 +152,41 @@ BALTIMORE,,1/2 bushel cartons,MINIATURE,,,9/30/17,18,18,18,18,PENNSYLVANIA,,,ORA BALTIMORE,,1/2 bushel cartons,MINIATURE,ROUND TYPE,,9/24/16,15,15,15,15,MARYLAND,,sml,,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,ROUND TYPE,,10/1/16,15,15,15,15,MARYLAND,,sml,,,,,,,,,N,,, BALTIMORE,,1/2 bushel cartons,MINIATURE,ROUND TYPE,,10/8/16,15,15,15,15,MARYLAND,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,140,145,140,145,ALABAMA,,med,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,140,145,140,145,ALABAMA,,med,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,140,145,140,145,ALABAMA,,med,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,140,145,140,145,ALABAMA,,med,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,140,154.75,140,154.75,MICHIGAN,,jbo,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,145,154.75,145,154.75,MICHIGAN,,xlge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,150,154.75,150,154.75,MICHIGAN,,med-lge,,,,,,,,,N,,, -ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,150,150,150,150,MICHIGAN,,sml,,,,,,,,,N,,, -ATLANTA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,CANADA,,med,,,,,,,,,N,,, -ATLANTA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,145,140,145,MICHIGAN,,lge,,,,,,,,,N,,, -ATLANTA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,140,140,140,TENNESSEE,,lge,,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/1/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/8/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,140,145,140,145,ALABAMA,,med,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/15/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,140,145,140,145,ALABAMA,,med,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/22/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,140,145,140,145,ALABAMA,,med,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,10/29/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,140,145,140,145,ALABAMA,,med,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,140,154.75,140,154.75,MICHIGAN,,jbo,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,145,154.75,145,154.75,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,150,154.75,150,154.75,MICHIGAN,,med-lge,ORANGE,,,,,,,,N,,, +ATLANTA,,24 inch bins,HOWDEN TYPE,,,11/5/16,150,150,150,150,MICHIGAN,,sml,ORANGE,,,,,,,,N,,, +ATLANTA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,CANADA,,med,ORANGE,,,,,,,,N,,, +ATLANTA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,145,140,145,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +ATLANTA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,140,140,140,TENNESSEE,,lge,ORANGE,,,,,,,,N,,, ATLANTA,,1 1/9 bushel cartons,PIE TYPE,,,11/5/16,14.5,15,14.5,15,MICHIGAN,,sml,,,,,,,,,N,,, ATLANTA,,1 1/9 bushel cartons,PIE TYPE,,,11/12/16,14.5,15,14.5,15,MICHIGAN,,sml,,,,,,,,,N,,, ATLANTA,,1 1/9 bushel cartons,PIE TYPE,,,11/19/16,14.5,15,14.5,15,MICHIGAN,,sml,,,,,,,,,N,,, @@ -329,18 +329,18 @@ BOSTON,,36 inch bins,HOWDEN TYPE,,,11/5/16,170,225,190,190,MASSACHUSETTS,,lge,OR BOSTON,,36 inch bins,HOWDEN TYPE,,,11/5/16,160,225,180,180,MASSACHUSETTS,,med-lge,ORANGE,,,,,,,,N,,, BOSTON,,36 inch bins,HOWDEN TYPE,,,11/5/16,160,225,175,175,MASSACHUSETTS,,med,ORANGE,,,,,,,,N,,, BOSTON,,36 inch bins,HOWDEN TYPE,,,11/5/16,150,225,175,175,MASSACHUSETTS,,sml,ORANGE,,,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,200,225,210,225,MASSACHUSETTS,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,185,200,MASSACHUSETTS,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,175,200,MASSACHUSETTS,,lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,200,225,225,225,MICHIGAN,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,200,200,MICHIGAN,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,200,200,MICHIGAN,,lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,200,225,210,225,MASSACHUSETTS,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,185,200,MASSACHUSETTS,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,175,175,MASSACHUSETTS,,lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,200,225,225,225,MICHIGAN,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,200,200,MICHIGAN,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,200,200,MICHIGAN,,lge,,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,200,225,210,225,MASSACHUSETTS,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,185,200,MASSACHUSETTS,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,175,200,MASSACHUSETTS,,lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,200,225,225,225,MICHIGAN,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,200,200,MICHIGAN,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,200,200,200,MICHIGAN,,lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,200,225,210,225,MASSACHUSETTS,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,185,200,MASSACHUSETTS,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,175,175,MASSACHUSETTS,,lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,200,225,225,225,MICHIGAN,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,200,200,MICHIGAN,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,HOWDEN TYPE,,,9/30/17,175,200,200,200,MICHIGAN,,lge,ORANGE,,PER BIN,,,,,,N,,, BOSTON,,24 inch bins,CINDERELLA,,,9/24/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, BOSTON,,24 inch bins,CINDERELLA,,,9/24/16,260,285,285,285,MASSACHUSETTS,,xlge,WHITE,,,,,,,,N,,, BOSTON,,24 inch bins,CINDERELLA,,,10/1/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, @@ -476,20 +476,20 @@ BOSTON,,36 inch bins,PIE TYPE,,,11/19/16,200,220,200,200,PENNSYLVANIA,,med-lge,O BOSTON,,36 inch bins,PIE TYPE,,,11/26/16,200,220,200,200,MASSACHUSETTS,,med-lge,ORANGE,,,,,,,,N,,, BOSTON,,36 inch bins,PIE TYPE,,,11/26/16,200,220,200,200,NEW YORK,,med-lge,ORANGE,,,,,,,,N,,, BOSTON,,36 inch bins,PIE TYPE,,,11/26/16,200,220,200,200,PENNSYLVANIA,,med-lge,ORANGE,,,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/2/17,300,300,300,300,MASSACHUSETTS,,,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/9/17,300,300,300,300,MASSACHUSETTS,,,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/9/17,200,200,200,200,PENNSYLVANIA,,,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/16/17,270,300,280,300,MASSACHUSETTS,,med-lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/16/17,270,300,280,300,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/16/17,270,300,280,300,PENNSYLVANIA,,med-lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/23/17,270,300,280,300,MASSACHUSETTS,,med-lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/23/17,270,300,280,300,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/23/17,270,300,280,300,PENNSYLVANIA,,med-lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/30/17,270,300,280,300,MASSACHUSETTS,,med-lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/30/17,270,300,280,300,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,PIE TYPE,,,9/30/17,270,300,280,300,PENNSYLVANIA,,med-lge,,,PER BIN,,,,,,N,,, -BOSTON,,bushel cartons,PIE TYPE,,,9/23/17,20,24,24,24,MASSACHUSETTS,,med-lge,,,,,,,,,N,,, -BOSTON,,bushel cartons,PIE TYPE,,,9/30/17,20,24,24,24,MASSACHUSETTS,,med-lge,,,,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/2/17,300,300,300,300,MASSACHUSETTS,,,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/9/17,300,300,300,300,MASSACHUSETTS,,,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/9/17,200,200,200,200,PENNSYLVANIA,,,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/16/17,270,300,280,300,MASSACHUSETTS,,med-lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/16/17,270,300,280,300,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/16/17,270,300,280,300,PENNSYLVANIA,,med-lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/23/17,270,300,280,300,MASSACHUSETTS,,med-lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/23/17,270,300,280,300,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/23/17,270,300,280,300,PENNSYLVANIA,,med-lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/30/17,270,300,280,300,MASSACHUSETTS,,med-lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/30/17,270,300,280,300,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,PIE TYPE,,,9/30/17,270,300,280,300,PENNSYLVANIA,,med-lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,bushel cartons,PIE TYPE,,,9/23/17,20,24,24,24,MASSACHUSETTS,,med-lge,ORANGE,,,,,,,,N,,, +BOSTON,,bushel cartons,PIE TYPE,,,9/30/17,20,24,24,24,MASSACHUSETTS,,med-lge,ORANGE,,,,,,,,N,,, BOSTON,,24 inch bins,BLUE TYPE,,,9/24/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, BOSTON,,24 inch bins,BLUE TYPE,,,10/1/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, BOSTON,,24 inch bins,BLUE TYPE,,,10/8/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, @@ -511,28 +511,28 @@ BOSTON,,24 inch bins,BIG MACK TYPE,,,10/15/16,260,285,285,285,MASSACHUSETTS,,xlg BOSTON,,24 inch bins,BIG MACK TYPE,,,10/22/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, BOSTON,,24 inch bins,BIG MACK TYPE,,,10/29/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, BOSTON,,24 inch bins,BIG MACK TYPE,,,11/5/16,260,285,285,285,MASSACHUSETTS,,xlge,ORANGE,,,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/9/17,175,175,175,175,MASSACHUSETTS,,,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,200,225,225,225,MASSACHUSETTS,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,MASSACHUSETTS,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,MASSACHUSETTS,,lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,MASSACHUSETTS,,med,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,175,175,MASSACHUSETTS,,sml,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,200,225,225,225,PENNSYLVANIA,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,200,225,210,225,MASSACHUSETTS,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,185,200,MASSACHUSETTS,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,175,200,MASSACHUSETTS,,lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,175,200,MASSACHUSETTS,,med,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,175,175,MASSACHUSETTS,,sml,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,200,225,225,225,PENNSYLVANIA,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,185,200,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,200,225,210,225,MASSACHUSETTS,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,185,200,MASSACHUSETTS,,xlge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,175,190,MASSACHUSETTS,,lge,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,175,185,MASSACHUSETTS,,med,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,175,175,MASSACHUSETTS,,sml,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,200,225,225,225,PENNSYLVANIA,,jbo,,,PER BIN,,,,,,N,,, -BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,185,200,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/9/17,175,175,175,175,MASSACHUSETTS,,,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,200,225,225,225,MASSACHUSETTS,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,MASSACHUSETTS,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,MASSACHUSETTS,,lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,MASSACHUSETTS,,med,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,175,175,MASSACHUSETTS,,sml,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,200,225,225,225,PENNSYLVANIA,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/16/17,175,200,200,200,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,200,225,210,225,MASSACHUSETTS,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,185,200,MASSACHUSETTS,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,175,200,MASSACHUSETTS,,lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,175,200,MASSACHUSETTS,,med,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,175,175,MASSACHUSETTS,,sml,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,200,225,225,225,PENNSYLVANIA,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/23/17,175,200,185,200,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,200,225,210,225,MASSACHUSETTS,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,185,200,MASSACHUSETTS,,xlge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,175,190,MASSACHUSETTS,,lge,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,175,185,MASSACHUSETTS,,med,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,175,175,MASSACHUSETTS,,sml,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,200,225,225,225,PENNSYLVANIA,,jbo,ORANGE,,PER BIN,,,,,,N,,, +BOSTON,,36 inch bins,BIG MACK TYPE,,,9/30/17,175,200,185,200,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, BOSTON,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,9/24/16,15,18,15,15,MASSACHUSETTS,,sml,WHITE,,,,,,,,N,,, BOSTON,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,9/24/16,15,18,15,15,MASSACHUSETTS,,sml,ORANGE,,,,,,,,N,,, BOSTON,,1/2 bushel cartons,MINIATURE,FLAT TYPE,,10/1/16,15,18,15,15,MASSACHUSETTS,,sml,WHITE,,,,,,,,N,,, @@ -567,51 +567,51 @@ CHICAGO,,24 inch bins,HOWDEN TYPE,,,9/23/17,200,225,200,225,CANADA,ONTARIO,lge,O CHICAGO,,24 inch bins,HOWDEN TYPE,,,9/23/17,200,220,200,220,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, CHICAGO,,24 inch bins,HOWDEN TYPE,,,9/30/17,200,225,200,225,CANADA,ONTARIO,lge,ORANGE,,,,,,,,N,,, CHICAGO,,24 inch bins,HOWDEN TYPE,,,9/30/17,200,220,200,220,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/24/16,135,135,135,135,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/24/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/1/16,130,135,130,135,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/1/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/8/16,130,130,130,130,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/8/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/15/16,130,135,130,135,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/15/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/15/16,60,60,60,60,OHIO,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,135,130,135,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/22/16,60,60,60,60,OHIO,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/29/16,125,135,125,135,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/29/16,60,60,60,60,OHIO,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/5/16,125,130,125,130,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/5/16,130,130,130,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/5/16,60,60,60,60,OHIO,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/12/16,125,130,125,130,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/12/16,120,130,120,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/12/16,60,60,60,60,OHIO,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/19/16,125,130,125,130,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/19/16,100,130,100,130,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/19/16,60,60,60,60,OHIO,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/26/16,125,125,125,125,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/26/16,100,125,100,125,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/3/16,125,125,125,125,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/3/16,100,125,100,125,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/10/16,125,125,125,125,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/10/16,100,125,100,125,MICHIGAN,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/23/17,170,170,170,170,MICHIGAN,,xlge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/30/17,170,170,170,170,MICHIGAN,,xlge,,,,,,,,,N,,, -CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/2/17,200,200,200,200,ILLINOIS,,med-lge,,,,,,,,,N,,, -CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/9/17,200,200,200,200,ILLINOIS,,med-lge,,,,,,,,,N,,, -CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/16/17,200,200,200,200,ILLINOIS,,med-lge,,,,,,,,,N,,, -CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/23/17,200,200,200,200,ILLINOIS,,med-lge,,,,,,,,,N,,, -CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/17,200,200,200,200,ILLINOIS,,med-lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,9/24/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/1/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/8/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/15/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/22/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/29/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,11/5/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, -CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,11/12/16,150,150,150,150,ILLINOIS,,lge,,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/24/16,135,135,135,135,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/24/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/1/16,130,135,130,135,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/1/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/8/16,130,130,130,130,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/8/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/15/16,130,135,130,135,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/15/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/15/16,60,60,60,60,OHIO,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,135,130,135,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/22/16,60,60,60,60,OHIO,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/29/16,125,135,125,135,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,10/29/16,60,60,60,60,OHIO,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/5/16,125,130,125,130,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/5/16,130,130,130,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/5/16,60,60,60,60,OHIO,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/12/16,125,130,125,130,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/12/16,120,130,120,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/12/16,60,60,60,60,OHIO,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/19/16,125,130,125,130,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/19/16,100,130,100,130,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/19/16,60,60,60,60,OHIO,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/26/16,125,125,125,125,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,11/26/16,100,125,100,125,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/3/16,125,125,125,125,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/3/16,100,125,100,125,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/10/16,125,125,125,125,ILLINOIS,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,12/10/16,100,125,100,125,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/23/17,170,170,170,170,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN TYPE,,,9/30/17,170,170,170,170,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/2/17,200,200,200,200,ILLINOIS,,med-lge,WHITE,,,,,,,,N,,, +CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/9/17,200,200,200,200,ILLINOIS,,med-lge,WHITE,,,,,,,,N,,, +CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/16/17,200,200,200,200,ILLINOIS,,med-lge,WHITE,,,,,,,,N,,, +CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/23/17,200,200,200,200,ILLINOIS,,med-lge,WHITE,,,,,,,,N,,, +CHICAGO,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/17,200,200,200,200,ILLINOIS,,med-lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,9/24/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/1/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/8/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/15/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/22/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,10/29/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,11/5/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, +CHICAGO,,36 inch bins,HOWDEN WHITE TYPE,,,11/12/16,150,150,150,150,ILLINOIS,,lge,WHITE,,,,,,,,N,,, CHICAGO,,36 inch bins,FAIRYTALE,,,9/24/16,200,200,200,200,ILLINOIS,,med,,,,,,,,,N,,, CHICAGO,,36 inch bins,FAIRYTALE,,,10/1/16,200,200,200,200,ILLINOIS,,med,,,,,,,,,N,,, CHICAGO,,36 inch bins,FAIRYTALE,,,10/8/16,200,200,200,200,ILLINOIS,,med,,,,,,,,,N,,, @@ -877,37 +877,37 @@ COLUMBIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,150,150,150,150,PENNSYLVANIA,,med, COLUMBIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,125,125,125,125,PENNSYLVANIA,,sml,ORANGE,,,,,,,,N,,, COLUMBIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,140,140,140,140,VIRGINIA,,lge,ORANGE,,,,,,,,N,,, COLUMBIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,140,140,140,140,VIRGINIA,,med,ORANGE,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,PENNSYLVANIA,,jbo,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,PENNSYLVANIA,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,PENNSYLVANIA,,med,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,160,160,160,VIRGINIA,,jbo,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,160,150,160,VIRGINIA,,xlge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,VIRGINIA,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,130,130,130,130,VIRGINIA,,sml,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,MARYLAND,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,PENNSYLVANIA,,jbo,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,PENNSYLVANIA,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,PENNSYLVANIA,,med,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,160,160,160,VIRGINIA,,jbo,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,VIRGINIA,,xlge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,VIRGINIA,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,130,130,130,130,VIRGINIA,,sml,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,MARYLAND,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,PENNSYLVANIA,,jbo,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,PENNSYLVANIA,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,PENNSYLVANIA,,med,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,160,160,160,160,VIRGINIA,,jbo,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,VIRGINIA,,xlge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,VIRGINIA,,lge,,,,,,,,,N,,, -COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,130,130,130,130,VIRGINIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/2/17,200,200,200,200,PENNSYLVANIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/9/17,200,200,200,200,PENNSYLVANIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/16/17,200,200,200,200,PENNSYLVANIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/16/17,200,200,200,200,VIRGINIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/23/17,200,200,200,200,PENNSYLVANIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/23/17,200,200,200,200,VIRGINIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/17,200,200,200,200,PENNSYLVANIA,,sml,,,,,,,,,N,,, -COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/17,200,200,200,200,VIRGINIA,,sml,,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,PENNSYLVANIA,,jbo,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,160,160,160,VIRGINIA,,jbo,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,160,150,160,VIRGINIA,,xlge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,VIRGINIA,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,130,130,130,130,VIRGINIA,,sml,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,PENNSYLVANIA,,jbo,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,160,160,160,VIRGINIA,,jbo,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,VIRGINIA,,xlge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,VIRGINIA,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,130,130,130,130,VIRGINIA,,sml,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,MARYLAND,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,PENNSYLVANIA,,jbo,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,160,160,160,160,VIRGINIA,,jbo,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,VIRGINIA,,xlge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,VIRGINIA,,lge,ORANGE,,,,,,,,N,,, +COLUMBIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,130,130,130,130,VIRGINIA,,sml,ORANGE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/2/17,200,200,200,200,PENNSYLVANIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/9/17,200,200,200,200,PENNSYLVANIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/16/17,200,200,200,200,PENNSYLVANIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/16/17,200,200,200,200,VIRGINIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/23/17,200,200,200,200,PENNSYLVANIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/23/17,200,200,200,200,VIRGINIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/17,200,200,200,200,PENNSYLVANIA,,sml,WHITE,,,,,,,,N,,, +COLUMBIA,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/17,200,200,200,200,VIRGINIA,,sml,WHITE,,,,,,,,N,,, COLUMBIA,,24 inch bins,CINDERELLA,,,9/24/16,170,170,170,170,NORTH CAROLINA,,med,,,,,,,,,N,,, COLUMBIA,,24 inch bins,CINDERELLA,,,10/1/16,170,170,170,170,NORTH CAROLINA,,med,,,,,,,,,N,,, COLUMBIA,,24 inch bins,CINDERELLA,,,10/1/16,175,175,175,175,PENNSYLVANIA,,lge,,,,,,,,,N,,, @@ -1072,32 +1072,32 @@ COLUMBIA,,bushel cartons,MINIATURE,FLAT TYPE,,10/22/16,28,28,28,28,PENNSYLVANIA, COLUMBIA,,bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,30,30,30,30,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, COLUMBIA,,bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,28,28,28,28,PENNSYLVANIA,,sml,ORANGE,,,,,,,,N,,, COLUMBIA,,bushel cartons,MINIATURE,FLAT TYPE,,10/29/16,28,28,28,28,PENNSYLVANIA,,sml,WHITE,,,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,9/24/16,120,130,120,130,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,9/24/16,120,130,120,130,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/1/16,120,130,120,130,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/1/16,120,130,120,130,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/8/16,120,130,120,130,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/8/16,120,130,120,130,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,130,120,130,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,130,120,130,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,130,120,130,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,130,120,130,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/29/16,85,130,85,130,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/29/16,85,130,85,130,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,11/5/16,80,90,80,90,CALIFORNIA,,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,11/5/16,80,90,80,90,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/16/17,120,120,120,120,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,125,120,125,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,160,180,160,180,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med-lge,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,235,235,235,235,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,275,375,275,375,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,sml,,,PER BIN,,,,,,N,,, -LOS ANGELES,,bins,HOWDEN TYPE,,,9/24/16,125,150,125,150,CALIFORNIA,,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/9/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,,,PER LB,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/16/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,,,PER LB,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/23/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,,,PER LB,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/30/17,250,250,250,250,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,,,PER BIN,,,,,,N,,, -LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/30/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,,,PER LB,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,9/24/16,120,130,120,130,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,9/24/16,120,130,120,130,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/1/16,120,130,120,130,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/1/16,120,130,120,130,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/8/16,120,130,120,130,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/8/16,120,130,120,130,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,130,120,130,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,130,120,130,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,130,120,130,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,130,120,130,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/29/16,85,130,85,130,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,10/29/16,85,130,85,130,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,11/5/16,80,90,80,90,CALIFORNIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,24 inch bins,HOWDEN TYPE,,,11/5/16,80,90,80,90,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/16/17,120,120,120,120,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,125,120,125,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,160,180,160,180,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med-lge,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,235,235,235,235,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN TYPE,,,9/30/17,275,375,275,375,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,sml,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,bins,HOWDEN TYPE,,,9/24/16,125,150,125,150,CALIFORNIA,,med,ORANGE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/9/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,WHITE,,PER LB,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/16/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,WHITE,,PER LB,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/23/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,WHITE,,PER LB,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/30/17,250,250,250,250,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,WHITE,,PER BIN,,,,,,N,,, +LOS ANGELES,,36 inch bins,HOWDEN WHITE TYPE,,,9/30/17,0.5,0.5,0.5,0.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med,WHITE,,PER LB,,,,,,N,,, LOS ANGELES,,24 inch bins,CINDERELLA,,,8/19/17,0.3,0.3,0.3,0.3,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med-lge,,,PER LB,,,,,,N,,, LOS ANGELES,,24 inch bins,CINDERELLA,,,8/26/17,0.3,0.3,0.3,0.3,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med-lge,,,PER LB,,,,,,N,,, LOS ANGELES,,24 inch bins,CINDERELLA,,,9/2/17,0.3,0.3,0.3,0.3,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,med-lge,,,PER LB,,,,,,N,,, @@ -1134,45 +1134,45 @@ LOS ANGELES,,35 lb cartons,MINIATURE,FLAT TYPE,,9/23/17,24.5,24.5,24.5,24.5,CALI LOS ANGELES,,35 lb cartons,MINIATURE,FLAT TYPE,,9/23/17,24.5,24.5,24.5,24.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,,ORANGE,,,,,,,,N,,, LOS ANGELES,,35 lb cartons,MINIATURE,FLAT TYPE,,9/30/17,24.5,24.5,24.5,24.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,,ORANGE,,,,,,,,N,,, LOS ANGELES,,35 lb cartons,MINIATURE,FLAT TYPE,,9/30/17,24.5,24.5,24.5,24.5,CALIFORNIA,CENTRAL SAN JOAQUIN VALLEY CALIFORNIA,,WHITE,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,170,150,170,MICHIGAN,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,170,150,170,MICHIGAN,,lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,130,150,130,150,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,130,150,130,150,NEW JERSEY,,lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,120,140,120,140,NEW YORK,,med-lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,170,150,170,PENNSYLVANIA,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,120,170,120,170,PENNSYLVANIA,,lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,180,190,180,190,PENNSYLVANIA,,med,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,150,170,150,170,MICHIGAN,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,150,170,150,170,MICHIGAN,,lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,NEW YORK,,med-lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,150,170,150,170,PENNSYLVANIA,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,170,160,170,PENNSYLVANIA,,lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,180,160,180,MICHIGAN,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,175,175,175,175,MICHIGAN,,lge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,140,150,140,150,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,160,160,160,NEW YORK,ORANGE COUNTY NEW YORK,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,120,140,120,140,PENNSYLVANIA,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/15/16,120,130,120,130,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/15/16,160,160,160,160,NEW YORK,ORANGE COUNTY NEW YORK,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/22/16,120,130,120,130,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/22/16,160,160,160,160,NEW YORK,ORANGE COUNTY NEW YORK,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/29/16,120,130,120,130,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,130,130,130,OHIO,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,11/5/16,100,150,100,150,NEW JERSEY,,xlge,,,,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,140,150,140,150,NEW JERSEY,,xlge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,140,160,140,160,NEW JERSEY,,lge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,NEW YORK,WESTERN SECTION,lge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,180,160,180,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,180,160,180,PENNSYLVANIA,,lge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,150,145,150,NEW JERSEY,,xlge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,NEW YORK,WESTERN SECTION,lge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,180,160,180,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,180,160,180,PENNSYLVANIA,,lge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,130,150,130,150,NEW JERSEY,,xlge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,NEW YORK,WESTERN SECTION,lge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,180,140,180,PENNSYLVANIA,,xlge,,,PER BIN,,,,,,N,,, -NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,180,140,180,PENNSYLVANIA,,lge,,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,170,150,170,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,170,150,170,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,130,150,130,150,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,130,150,130,150,NEW JERSEY,,lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,120,140,120,140,NEW YORK,,med-lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,170,150,170,PENNSYLVANIA,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,120,170,120,170,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/24/16,180,190,180,190,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,150,170,150,170,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,150,170,150,170,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,NEW YORK,,med-lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,150,170,150,170,PENNSYLVANIA,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,170,160,170,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,180,160,180,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,175,175,175,175,MICHIGAN,,lge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,140,150,140,150,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,160,160,160,NEW YORK,ORANGE COUNTY NEW YORK,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/8/16,120,140,120,140,PENNSYLVANIA,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/15/16,120,130,120,130,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/15/16,160,160,160,160,NEW YORK,ORANGE COUNTY NEW YORK,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/22/16,120,130,120,130,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/22/16,160,160,160,160,NEW YORK,ORANGE COUNTY NEW YORK,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/29/16,120,130,120,130,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,130,130,130,OHIO,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,11/5/16,100,150,100,150,NEW JERSEY,,xlge,ORANGE,,,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,140,150,140,150,NEW JERSEY,,xlge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,140,160,140,160,NEW JERSEY,,lge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,NEW YORK,WESTERN SECTION,lge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,180,160,180,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/16/17,160,180,160,180,PENNSYLVANIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,150,145,150,NEW JERSEY,,xlge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,NEW YORK,WESTERN SECTION,lge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,180,160,180,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/23/17,160,180,160,180,PENNSYLVANIA,,lge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,130,150,130,150,NEW JERSEY,,xlge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,NEW YORK,WESTERN SECTION,lge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,180,140,180,PENNSYLVANIA,,xlge,ORANGE,,PER BIN,,,,,,N,,, +NEW YORK,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,180,140,180,PENNSYLVANIA,,lge,ORANGE,,PER BIN,,,,,,N,,, NEW YORK,,1 1/9 bushel cartons,PIE TYPE,,,9/24/16,16,18,16,18,OHIO,,med,,,,,,,,,N,,, NEW YORK,,1 1/9 bushel cartons,PIE TYPE,,,10/1/16,16,18,16,18,OHIO,,med,,,,,,,,,N,,, NEW YORK,,1 1/9 bushel cartons,PIE TYPE,,,10/8/16,16,18,16,18,OHIO,,med,,,,,,,,,N,,, @@ -1293,66 +1293,66 @@ DETROIT,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,10/29/16,135,135,135,135,MICHIG DALLAS,,24 inch bins,,,,9/16/17,160,225,160,225,TEXAS,,lge,,,,,,,,,N,,, DALLAS,,24 inch bins,,,,9/23/17,160,225,160,225,TEXAS,,lge,,,,,,,,,N,,, DALLAS,,24 inch bins,,,,9/30/17,160,225,160,225,TEXAS,,lge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/1/16,150,160,150,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/1/16,125,150,125,150,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/1/16,125,150,125,150,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/8/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/8/16,125,150,125,150,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/8/16,125,125,125,125,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/15/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/15/16,125,150,125,150,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/15/16,125,125,125,125,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/22/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/22/16,125,150,125,150,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/22/16,125,125,125,125,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/29/16,115,120,115,120,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/29/16,100,120,100,120,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,10/29/16,120,120,120,120,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,11/5/16,115,115,115,115,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,11/5/16,100,100,100,100,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,11/5/16,120,120,120,120,TEXAS,,sml,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/16/17,160,160,160,160,TEXAS,,lge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/16/17,150,160,150,160,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/23/17,170,170,170,170,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/23/17,160,160,160,160,TEXAS,,lge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/23/17,150,160,150,160,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,170,170,170,170,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,170,170,170,170,TEXAS,,xlge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,125,160,125,160,TEXAS,,lge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,125,160,125,160,TEXAS,,med-lge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,125,160,125,160,TEXAS,,med,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/24/16,170,170,170,170,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,170,160,170,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,10/15/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,10/22/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,10/29/16,120,140,120,140,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,11/5/16,120,120,120,120,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/16/17,165,165,165,165,MEXICO,,lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,MEXICO,,med-lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/23/17,165,165,165,165,MEXICO,,lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,MEXICO,,med-lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,TEXAS,,med-lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/30/17,165,165,165,165,MEXICO,,lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,MEXICO,,med-lge,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,TEXAS,,med-lge,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,9/24/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/1/16,150,160,150,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/8/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/15/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/22/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/29/16,120,120,120,120,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,11/5/16,120,120,120,120,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,9/24/16,170,170,170,170,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/1/16,160,170,160,170,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/8/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/15/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/22/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/29/16,140,140,140,140,TEXAS,,jbo,,,,,,,,,N,,, -DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,11/5/16,140,140,140,140,TEXAS,,jbo,,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/24/16,160,160,160,160,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/1/16,150,160,150,160,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/1/16,125,150,125,150,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/1/16,125,150,125,150,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/8/16,150,150,150,150,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/8/16,125,150,125,150,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/8/16,125,125,125,125,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/15/16,150,150,150,150,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/15/16,125,150,125,150,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/15/16,125,125,125,125,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/22/16,150,150,150,150,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/22/16,125,150,125,150,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/22/16,125,125,125,125,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/29/16,115,120,115,120,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/29/16,100,120,100,120,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,10/29/16,120,120,120,120,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,11/5/16,115,115,115,115,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,11/5/16,100,100,100,100,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,11/5/16,120,120,120,120,TEXAS,,sml,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/16/17,160,160,160,160,TEXAS,,lge,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/16/17,150,160,150,160,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/23/17,170,170,170,170,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/23/17,160,160,160,160,TEXAS,,lge,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/23/17,150,160,150,160,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,170,170,170,170,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,170,170,170,170,TEXAS,,xlge,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,125,160,125,160,TEXAS,,lge,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,125,160,125,160,TEXAS,,med-lge,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN TYPE,,,9/30/17,125,160,125,160,TEXAS,,med,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/24/16,170,170,170,170,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,10/1/16,160,170,160,170,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,10/8/16,160,160,160,160,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,10/15/16,160,160,160,160,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,10/22/16,160,160,160,160,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,10/29/16,120,140,120,140,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,11/5/16,120,120,120,120,TEXAS,,jbo,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/16/17,165,165,165,165,MEXICO,,lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/16/17,150,150,150,150,MEXICO,,med-lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/23/17,165,165,165,165,MEXICO,,lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,MEXICO,,med-lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,TEXAS,,med-lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/30/17,165,165,165,165,MEXICO,,lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,MEXICO,,med-lge,ORANGE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN TYPE,,,9/30/17,150,150,150,150,TEXAS,,med-lge,ORANGE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,9/24/16,160,160,160,160,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/1/16,150,160,150,160,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/8/16,150,150,150,150,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/15/16,150,150,150,150,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/22/16,150,150,150,150,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,10/29/16,120,120,120,120,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,24 inch bins,HOWDEN WHITE TYPE,,,11/5/16,120,120,120,120,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,9/24/16,170,170,170,170,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/1/16,160,170,160,170,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/8/16,160,160,160,160,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/15/16,160,160,160,160,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/22/16,160,160,160,160,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,10/29/16,140,140,140,140,TEXAS,,jbo,WHITE,,,,,,,,N,,, +DALLAS,,36 inch bins,HOWDEN WHITE TYPE,,,11/5/16,140,140,140,140,TEXAS,,jbo,WHITE,,,,,,,,N,,, DALLAS,,24 inch bins,CINDERELLA,,,9/24/16,160,160,160,160,TEXAS,,jbo,,,,,,,,,N,,, DALLAS,,24 inch bins,CINDERELLA,,,10/1/16,150,160,150,160,TEXAS,,jbo,,,,,,,,,N,,, DALLAS,,24 inch bins,CINDERELLA,,,10/8/16,150,150,150,150,TEXAS,,jbo,,,,,,,,,N,,, @@ -1427,45 +1427,45 @@ DALLAS,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,10/15/16,150,150,150,150,TEXAS,, DALLAS,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,10/22/16,150,150,150,150,TEXAS,,med,,,,,,,,,N,,, DALLAS,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,10/29/16,120,120,120,120,TEXAS,,med,,,,,,,,,N,,, DALLAS,,24 inch bins,MIXED HEIRLOOM VARIETIES,,,11/5/16,120,120,120,120,TEXAS,,med,,,,,,,,,N,,, -MIAMI,,36 inch bins,HOWDEN TYPE,,,11/29/14,120,120,120,120,MICHIGAN,,jbo,,,PER BIN,,,,,,N,,, -MIAMI,,36 inch bins,HOWDEN TYPE,,,11/29/14,160,160,160,160,MICHIGAN,,lge,,,PER BIN,,,,,,N,,, +MIAMI,,36 inch bins,HOWDEN TYPE,,,11/29/14,120,120,120,120,MICHIGAN,,jbo,ORANGE,,PER BIN,,,,,,N,,, +MIAMI,,36 inch bins,HOWDEN TYPE,,,11/29/14,160,160,160,160,MICHIGAN,,lge,ORANGE,,PER BIN,,,,,,N,,, MIAMI,,36 inch bins,PIE TYPE,,,11/29/14,230,230,230,230,MICHIGAN,,,,,PER BIN,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,9/24/16,155,155,155,155,CALIFORNIA,,,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/1/16,155,155,155,155,CALIFORNIA,,,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/1/16,155,155,155,155,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/1/16,135,135,135,135,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/8/16,135,135,135,135,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,150,120,150,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,150,120,150,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,145,120,145,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,145,120,145,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,150,120,150,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,145,120,145,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/29/16,120,145,120,145,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/29/16,110,150,110,150,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/29/16,100,145,100,145,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/5/16,120,120,120,120,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/5/16,110,120,110,120,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/5/16,100,120,100,120,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/12/16,120,120,120,120,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/12/16,110,120,110,120,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,Organic,24 inch bins,HOWDEN TYPE,,,10/22/16,440,440,440,440,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,jbo,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/15/16,135,150,135,150,CALIFORNIA,,jbo,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/15/16,135,150,135,150,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/15/16,135,150,135,150,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/9/17,140,140,140,140,CALIFORNIA,,xlge,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/16/17,140,175,140,175,CALIFORNIA,,xlge,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,175,175,175,CALIFORNIA,,xlge,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,,sml,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,135,120,135,CALIFORNIA,,lge,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,135,120,135,CALIFORNIA,,med,,,,,,,,,N,,, -SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,200,200,200,200,CALIFORNIA,,med,WHITE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,9/24/16,155,155,155,155,CALIFORNIA,,,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/1/16,155,155,155,155,CALIFORNIA,,,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/1/16,155,155,155,155,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/1/16,135,135,135,135,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/8/16,135,135,135,135,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,150,120,150,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,150,120,150,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/15/16,120,145,120,145,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,145,120,145,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,150,120,150,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/22/16,120,145,120,145,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/29/16,120,145,120,145,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/29/16,110,150,110,150,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,10/29/16,100,145,100,145,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/5/16,120,120,120,120,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/5/16,110,120,110,120,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/5/16,100,120,100,120,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/12/16,120,120,120,120,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,24 inch bins,HOWDEN TYPE,,,11/12/16,110,120,110,120,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,Organic,24 inch bins,HOWDEN TYPE,,,10/22/16,440,440,440,440,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,jbo,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/8/16,135,150,135,150,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/15/16,135,150,135,150,CALIFORNIA,,jbo,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/15/16,135,150,135,150,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,10/15/16,135,150,135,150,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/9/17,140,140,140,140,CALIFORNIA,,xlge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/16/17,140,175,140,175,CALIFORNIA,,xlge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,175,175,175,175,CALIFORNIA,,xlge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,120,120,120,CALIFORNIA,,sml,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,135,120,135,CALIFORNIA,,lge,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,135,120,135,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, +SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,200,200,200,200,CALIFORNIA,,med,ORANGE,,,,,,,,N,,, SAN FRANCISCO,,36 inch bins,HOWDEN TYPE,,,9/30/17,120,120,120,120,CALIFORNIA,,sml,,,,,,,,,N,,, SAN FRANCISCO,,36 inch bins,CINDERELLA,,,9/16/17,255,255,255,255,CALIFORNIA,,,,,,,,,,,N,,, SAN FRANCISCO,,36 inch bins,CINDERELLA,,,9/23/17,185,255,185,255,CALIFORNIA,,,,,,,,,,,N,,, @@ -1596,34 +1596,34 @@ SAN FRANCISCO,,40 lb cartons,MINIATURE,ROUND TYPE,,10/15/16,30,35,30,35,CALIFORN SAN FRANCISCO,,40 lb cartons,MINIATURE,ROUND TYPE,,10/22/16,30,35,30,35,CALIFORNIA,,,,,,,,,,,N,,, SAN FRANCISCO,,40 lb cartons,MINIATURE,ROUND TYPE,,10/29/16,30,30,30,30,CALIFORNIA,,,,,,,,,,,N,,, SAN FRANCISCO,,40 lb cartons,MINIATURE,ROUND TYPE,,11/5/16,30,30,30,30,CALIFORNIA,,,,,,,,,,,N,,, -PHILADELPHIA,,24 inch bins,HOWDEN TYPE,,,9/16/17,140,150,140,150,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,NEW YORK,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,NEW YORK,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,160,150,160,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,NEW JERSEY,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,PENNSYLVANIA,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/1/16,120,120,120,120,PENNSYLVANIA,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/8/16,140,155,140,155,NEW JERSEY,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/8/16,120,120,120,120,NEW JERSEY,,sml,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/15/16,120,125,120,125,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/15/16,100,125,100,125,PENNSYLVANIA,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,140,130,140,NEW JERSEY,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,120,120,120,120,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,140,130,140,PENNSYLVANIA,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,100,120,100,120,PENNSYLVANIA,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,140,130,140,NEW JERSEY,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,120,120,120,120,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,140,130,140,PENNSYLVANIA,,med-lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,100,120,120,120,PENNSYLVANIA,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,120,120,120,120,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,120,150,120,150,PENNSYLVANIA,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,140,140,140,DELAWARE,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,DELAWARE,,med,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,150,120,150,NEW YORK,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,150,140,150,PENNSYLVANIA,,xlge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,150,140,150,PENNSYLVANIA,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,140,140,140,DELAWARE,,lge,,,,,,,,,N,,, -PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,140,140,140,DELAWARE,,med,,,,,,,,,N,,, +PHILADELPHIA,,24 inch bins,HOWDEN TYPE,,,9/16/17,140,150,140,150,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,NEW YORK,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,150,150,150,NEW YORK,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/24/16,150,160,150,160,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,NEW JERSEY,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/1/16,140,140,140,140,PENNSYLVANIA,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/1/16,120,120,120,120,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/8/16,140,155,140,155,NEW JERSEY,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/8/16,120,120,120,120,NEW JERSEY,,sml,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/15/16,120,125,120,125,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/15/16,100,125,100,125,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,140,130,140,NEW JERSEY,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,120,120,120,120,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,130,140,130,140,PENNSYLVANIA,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/22/16,100,120,100,120,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,140,130,140,NEW JERSEY,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,120,120,120,120,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,130,140,130,140,PENNSYLVANIA,,med-lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,10/29/16,100,120,120,120,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,120,120,120,120,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/16/17,120,150,120,150,PENNSYLVANIA,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,140,140,140,DELAWARE,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,150,150,150,150,DELAWARE,,med,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,120,150,120,150,NEW YORK,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,150,140,150,PENNSYLVANIA,,xlge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/23/17,140,150,140,150,PENNSYLVANIA,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,140,140,140,DELAWARE,,lge,ORANGE,,,,,,,,N,,, +PHILADELPHIA,,36 inch bins,HOWDEN TYPE,,,9/30/17,140,140,140,140,DELAWARE,,med,ORANGE,,,,,,,,N,,, PHILADELPHIA,,1 1/9 bushel cartons,PIE TYPE,,,10/15/16,12,14,12,14,OHIO,,med,,,,,,,,,N,,, PHILADELPHIA,,1 1/9 bushel cartons,PIE TYPE,,,10/22/16,12,12,12,12,OHIO,,med,,,,,,,,,N,,, PHILADELPHIA,,1 1/9 bushel cartons,PIE TYPE,,,10/29/16,12,12,12,12,OHIO,,med,,,,,,,,,N,,, @@ -1693,11 +1693,11 @@ ST. LOUIS,,36 inch bins,HOWDEN TYPE,,,9/30/16,115,125,,,ILLINOIS,,med,ORANGE,,,, ST. LOUIS,,36 inch bins,HOWDEN TYPE,,,9/30/16,115,115,,,MICHIGAN,,xlge,ORANGE,,,,,,,,N,,,LOWER. ST. LOUIS,,36 inch bins,HOWDEN TYPE,,,9/30/16,115,115,,,MICHIGAN,,lge,ORANGE,,,,,,,,N,,,LOWER. ST. LOUIS,,36 inch bins,HOWDEN TYPE,,,9/30/16,115,115,,,MICHIGAN,,med,ORANGE,,,,,,,,N,,,LOWER. -ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/26/16,150,150,,,ILLINOIS,,xlge,,,,,,,,,N,,,LOWER. -ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/27/16,150,150,,,ILLINOIS,,xlge,,,,,,,,,N,,,STEADY. -ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/28/16,150,150,,,ILLINOIS,,xlge,,,,,,,,,N,,,ABOUT STEADY. -ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/29/16,150,150,,,ILLINOIS,,xlge,,,,,,,,,N,,,"MINIATURE LOWER, OTHERS STEADY." -ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/16,150,150,,,ILLINOIS,,xlge,,,,,,,,,N,,,LOWER. +ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/26/16,150,150,,,ILLINOIS,,xlge,WHITE,,,,,,,,N,,,LOWER. +ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/27/16,150,150,,,ILLINOIS,,xlge,WHITE,,,,,,,,N,,,STEADY. +ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/28/16,150,150,,,ILLINOIS,,xlge,WHITE,,,,,,,,N,,,ABOUT STEADY. +ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/29/16,150,150,,,ILLINOIS,,xlge,WHITE,,,,,,,,N,,,"MINIATURE LOWER, OTHERS STEADY." +ST. LOUIS,,24 inch bins,HOWDEN WHITE TYPE,,,9/30/16,150,150,,,ILLINOIS,,xlge,WHITE,,,,,,,,N,,,LOWER. ST. LOUIS,,24 inch bins,PIE TYPE,,,9/26/16,175,175,,,ILLINOIS,,,ORANGE,,,,,,,,N,,,LOWER. ST. LOUIS,,24 inch bins,PIE TYPE,,,9/27/16,175,175,,,ILLINOIS,,,ORANGE,,,,,,,,N,,,STEADY. ST. LOUIS,,24 inch bins,PIE TYPE,,,9/28/16,175,175,,,ILLINOIS,,,ORANGE,,,,,,,,N,,,ABOUT STEADY.