From 73e8120d73f0143cab497441a8a0006a3a8724da Mon Sep 17 00:00:00 2001 From: Jim Bennett Date: Tue, 14 Feb 2023 17:16:30 -0800 Subject: [PATCH 1/5] Correcting the column used in the scatter plot The notebook plots the `Month` column, but should be the `DayOfYear` column to align with the text. This change fixes that plot. It also clears the output, as this feels like good practice, and is necessary as the plot will be different with the correct column being used. --- 2-Regression/3-Linear/notebook.ipynb | 345 +-------------------------- 1 file changed, 7 insertions(+), 338 deletions(-) diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb index 2da56e5b6..f0aa556f1 100644 --- a/2-Regression/3-Linear/notebook.ipynb +++ b/2-Regression/3-Linear/notebook.ipynb @@ -16,209 +16,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "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 × 26 columns

\n", - "
" - ], - "text/plain": [ - " City Name Type Package Variety Sub Variety Grade Date \\\n", - "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", - "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", - "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", - "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", - "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", - "\n", - " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", - "0 270.0 280.0 270.0 ... NaN NaN NaN \n", - "1 270.0 280.0 270.0 ... NaN NaN NaN \n", - "2 160.0 160.0 160.0 ... NaN NaN NaN \n", - "3 160.0 160.0 160.0 ... NaN NaN NaN \n", - "4 90.0 100.0 90.0 ... NaN NaN NaN \n", - "\n", - " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n", - "0 NaN NaN NaN E NaN NaN NaN \n", - "1 NaN NaN NaN E NaN NaN NaN \n", - "2 NaN NaN NaN N NaN NaN NaN \n", - "3 NaN NaN NaN N NaN NaN NaN \n", - "4 NaN NaN NaN N NaN NaN NaN \n", - "\n", - "[5 rows x 26 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -232,115 +32,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "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", - "
MonthVarietyCityPackageLow PriceHigh PricePrice
709PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
719PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7210PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7310PIE TYPEBALTIMORE1 1/9 bushel cartons17.017.015.454545
7410PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
\n", - "
" - ], - "text/plain": [ - " Month Variety City Package Low Price High Price \\\n", - "70 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 15.0 \n", - "71 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 18.0 \n", - "72 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 18.0 \n", - "73 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 17.0 17.0 \n", - "74 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 15.0 \n", - "\n", - " Price \n", - "70 13.636364 \n", - "71 16.363636 \n", - "72 16.363636 \n", - "73 15.454545 \n", - "74 13.636364 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", "\n", @@ -377,37 +71,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 7.5, 8. , 8.5, 9. , 9.5, 10. , 10.5, 11. , 11.5, 12. , 12.5]),\n", - " )" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "plt.scatter('Month','Price',data=new_pumpkins)" + "plt.scatter('DayOfYear','Price',data=new_pumpkins)" ] }, { From 306d938edc09aaf0db81964ae5b556b361cc9b16 Mon Sep 17 00:00:00 2001 From: Jim Bennett Date: Tue, 14 Feb 2023 17:28:19 -0800 Subject: [PATCH 2/5] Update notebook.ipynb --- 2-Regression/3-Linear/notebook.ipynb | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb index f0aa556f1..b01f1ee88 100644 --- a/2-Regression/3-Linear/notebook.ipynb +++ b/2-Regression/3-Linear/notebook.ipynb @@ -76,7 +76,7 @@ "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "plt.scatter('DayOfYear','Price',data=new_pumpkins)" + "plt.scatter('Month','Price',data=new_pumpkins)" ] }, { @@ -84,7 +84,10 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\n", + "plt.scatter('DayOfYear','Price',data=new_pumpkins)" + ] } ], "metadata": { From 6334166d923274ba38d80b31bf0e20f0700112f6 Mon Sep 17 00:00:00 2001 From: Jim Bennett Date: Tue, 14 Feb 2023 17:39:48 -0800 Subject: [PATCH 3/5] Adding the call to the `corr` function The README is missing the call to the `corr` function that is in the final notebook. --- 2-Regression/3-Linear/README.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index 63596de49..8601abde3 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -103,7 +103,14 @@ This suggests that there should be some correlation, and we can try training lin Scatter plot of Price vs. Day of Year -It looks like there are different clusters of prices corresponding to different pumpkin varieties. To confirm this hypothesis, let's plot each pumpkin category using a different color. By passing an `ax` parameter to the `scatter` plotting function we can plot all points on the same graph: +Let's see if there is a correlation using the `corr` function: + +```python +print(new_pumpkins['Month'].corr(new_pumpkins['Price'])) +print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price'])) +``` + +It looks like the correlation is pretty small, -0.15 by `Month` and -0.17 by the `DayOfMonth`, but there could be another important relationship. It looks like there are different clusters of prices corresponding to different pumpkin varieties. To confirm this hypothesis, let's plot each pumpkin category using a different color. By passing an `ax` parameter to the `scatter` plotting function we can plot all points on the same graph: ```python ax=None From ecac35d44972a431d080cd3bb1979a234934ab8c Mon Sep 17 00:00:00 2001 From: Jim Bennett Date: Tue, 14 Feb 2023 18:03:34 -0800 Subject: [PATCH 4/5] Adding bar chart by price --- 2-Regression/3-Linear/README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index 8601abde3..7012eb622 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -122,7 +122,15 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): Scatter plot of Price vs. Day of Year -Our investigation suggests that variety has more effect on the overall price than the actual selling date. So let us focus for the moment only on one pumpkin variety, and see what effect the date has on the price: +Our investigation suggests that variety has more effect on the overall price than the actual selling date. We can see this with a bar graph: + +```python +new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') +``` + +Scatter plot of Price vs. Day of Year + +Let us focus for the moment only on one pumpkin variety, the 'pie type', and see what effect the date has on the price: ```python pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] From 0ae91d5f8bfff756eae68a54778b248b4440fc4d Mon Sep 17 00:00:00 2001 From: Jim Bennett Date: Tue, 14 Feb 2023 18:05:31 -0800 Subject: [PATCH 5/5] Update README.md --- 2-Regression/3-Linear/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index 7012eb622..d213a7b43 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -128,7 +128,7 @@ Our investigation suggests that variety has more effect on the overall price tha new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -Scatter plot of Price vs. Day of Year +Bar graph of price vs variety Let us focus for the moment only on one pumpkin variety, the 'pie type', and see what effect the date has on the price: