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making-sense-of-data-graphics.ipynb
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"text": [
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"{'data_mining_making_sense.csv': b'Customer,Store,Product category,Product description,Sale price ($),Profit ($)\\r\\nJ. Smith,SD Mall,PS Game,Last of Us,49,5\\r\\nJ. Smith,L.A. Store,Online Game,Celeste,45,3\\r\\nJ. Smith,SD Mall,PS Game,Sniper Elite,35,4\\r\\nJ. Smith,SD Mall,NS Game,Zelda: BoW,45,5\\r\\nE. Robinson,SD Mall,PS Game,Last of Us,49,5\\r\\nE. Robinson,L.A. Store,PS Game,Speed 4,39,3\\r\\nF. Tyle,SD Mall,Online Game,Celeste,46,1\\r\\nK. Jonhs,SD Mall,Online Game,Limbo,15,1\\r\\nK. Jonhs,L.A. Store,Xbox Game,Overcooked 2,20,3\\r\\nK. Jonhs,L.A. Store,Xbox Game,Last of Us,40,5\\r\\nK. Jonhs,SD Mall,Online Game,Pool House,45,2\\r\\nK. Jonhs,L.A. Store,Xbox Game,Pokemon Sword,39,5\\r\\nK. Hike,SD Mall,Xbox Game,Pokemon Sword,39,5\\r\\nS.Lee,L.A. Store,NS Game,Pikmin 3,49,4\\r\\nP. Gales,L.A. Store,Online Game,Overcooked 2,20,3\\r\\nP. Gales,SD Mall,NS Game,Zelda: BoW,45,5\\r\\nY. Vert,SD Mall,Online Game,Pokemon Shield,39,5\\r\\nS. Kim,SD Mall,NS Game,Unravel two,20,2\\r\\nS. Kim,L.A. Store,Xbox Game,NFS Ultimate,56,4\\r\\nW. Wilson,SD Mall,Xbox Game,Overcooked 2,20,3\\r\\n'}"
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"data"
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" Customer Store Product category Product description \\\n",
"0 J. Smith SD Mall PS Game Last of Us \n",
"1 J. Smith L.A. Store Online Game Celeste \n",
"2 J. Smith SD Mall PS Game Sniper Elite \n",
"3 J. Smith SD Mall NS Game Zelda: BoW \n",
"4 E. Robinson SD Mall PS Game Last of Us \n",
"5 E. Robinson L.A. Store PS Game Speed 4 \n",
"6 F. Tyle SD Mall Online Game Celeste \n",
"7 K. Jonhs SD Mall Online Game Limbo \n",
"8 K. Jonhs L.A. Store Xbox Game Overcooked 2 \n",
"9 K. Jonhs L.A. Store Xbox Game Last of Us \n",
"10 K. Jonhs SD Mall Online Game Pool House \n",
"11 K. Jonhs L.A. Store Xbox Game Pokemon Sword \n",
"12 K. Hike SD Mall Xbox Game Pokemon Sword \n",
"13 S.Lee L.A. Store NS Game Pikmin 3 \n",
"14 P. Gales L.A. Store Online Game Overcooked 2 \n",
"15 P. Gales SD Mall NS Game Zelda: BoW \n",
"16 Y. Vert SD Mall Online Game Pokemon Shield \n",
"17 S. Kim SD Mall NS Game Unravel two \n",
"18 S. Kim L.A. Store Xbox Game NFS Ultimate \n",
"19 W. Wilson SD Mall Xbox Game Overcooked 2 \n",
"\n",
" Sale price ($) Profit ($) \n",
"0 49 5 \n",
"1 45 3 \n",
"2 35 4 \n",
"3 45 5 \n",
"4 49 5 \n",
"5 39 3 \n",
"6 46 1 \n",
"7 15 1 \n",
"8 20 3 \n",
"9 40 5 \n",
"10 45 2 \n",
"11 39 5 \n",
"12 39 5 \n",
"13 49 4 \n",
"14 20 3 \n",
"15 45 5 \n",
"16 39 5 \n",
"17 20 2 \n",
"18 56 4 \n",
"19 20 3 "
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" const element = document.querySelector('#df-f9caa9e8-b3a9-444d-8dd5-489df68f183f');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"source": [
"data.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "__6xr9v5GGwp",
"outputId": "80e56da7-c706-4b29-a4a6-e367d11d0fbd"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 20 entries, 0 to 19\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Customer 20 non-null object\n",
" 1 Store 20 non-null object\n",
" 2 Product category 20 non-null object\n",
" 3 Product description 20 non-null object\n",
" 4 Sale price ($) 20 non-null int64 \n",
" 5 Profit ($) 20 non-null int64 \n",
"dtypes: int64(2), object(4)\n",
"memory usage: 1.1+ KB\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Contingency Table"
],
"metadata": {
"id": "_h5mvMIUFDoJ"
}
},
{
"cell_type": "code",
"source": [
"data_crosstab = pd.crosstab(data['Store'],\n",
" data['Product category'], \n",
" margins = False)\n",
"data_crosstab"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 144
},
"id": "09sV9FbgFxXe",
"outputId": "a0c3ffe6-6d85-4a3e-c0b8-0d487a21a235"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Product category NS Game Online Game PS Game Xbox Game\n",
"Store \n",
"L.A. Store 1 2 1 4\n",
"SD Mall 3 4 3 2"
],
"text/html": [
"\n",
" <div id=\"df-4d65150e-12c4-441b-a793-6b2030b7c4fa\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Product category</th>\n",
" <th>NS Game</th>\n",
" <th>Online Game</th>\n",
" <th>PS Game</th>\n",
" <th>Xbox Game</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Store</th>\n",
" <th></th>\n",
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" <th>L.A. Store</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD Mall</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d65150e-12c4-441b-a793-6b2030b7c4fa')\"\n",
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" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
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" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
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" fill: #174EA6;\n",
" }\n",
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" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-4d65150e-12c4-441b-a793-6b2030b7c4fa button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-4d65150e-12c4-441b-a793-6b2030b7c4fa');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"source": [
"# Summary tables"
],
"metadata": {
"id": "bjuVOxOhGttA"
}
},
{
"cell_type": "markdown",
"source": [
"Grouping by Customer and the showing a count of the number of observations and the sum of Sale price ($) for each row."
],
"metadata": {
"id": "5lmM6nD9GvgA"
}
},
{
"cell_type": "code",
"source": [
"data_crosstab = pd.crosstab(index=[],\n",
" values=data['Sale price ($)'], \n",
" columns=data['Customer'],\n",
" margins = False,\n",
" aggfunc=[np.size, np.sum]).stack().loc['__dummy__'].\\\n",
" rename(columns={\"size\": \"Observations\", \"sum\": \"Total Sale price ($)\"},errors=\"raise\", inplace=False)\n",
"\n",
"data_crosstab"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 395
},
"id": "1rSLPlpbHBXi",
"outputId": "335843d8-b0cc-4aab-d6d9-52e7224f3b95"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Observations Total Sale price ($)\n",
"Customer \n",
"E. Robinson 2 88\n",
"F. Tyle 1 46\n",
"J. Smith 4 174\n",
"K. Hike 1 39\n",
"K. Jonhs 5 159\n",
"P. Gales 2 65\n",
"S. Kim 2 76\n",
"S.Lee 1 49\n",
"W. Wilson 1 20\n",
"Y. Vert 1 39"
],
"text/html": [
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" <th>Total Sale price ($)</th>\n",
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" <tr>\n",
" <th>Customer</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <td>2</td>\n",
" <td>88</td>\n",
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" <th>F. Tyle</th>\n",
" <td>1</td>\n",
" <td>46</td>\n",
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" <td>4</td>\n",
" <td>174</td>\n",
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" <td>1</td>\n",
" <td>39</td>\n",
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" <td>5</td>\n",
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" <th>P. Gales</th>\n",
" <td>2</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>S. Kim</th>\n",
" <td>2</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>S.Lee</th>\n",
" <td>1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>W. Wilson</th>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Y. Vert</th>\n",
" <td>1</td>\n",
" <td>39</td>\n",
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" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"source": [
"Grouping by Store and showing a count of the number of observations and the mean Sale price ($) for each row."
],
"metadata": {
"id": "CAcRZm_XGyTw"
}
},
{
"cell_type": "code",
"source": [
"data_crosstab = pd.crosstab(index=[],\n",
" values=data['Sale price ($)'], \n",
" columns=data['Store'],\n",
" margins = False,\n",
" aggfunc=[np.size, np.mean]).stack().loc['__dummy__'].\\\n",
" rename(columns={\"size\": \"Observations\", \"mean\": \"Mean Sale price ($)\"},errors=\"raise\", inplace=False)\n",
"data_crosstab"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 144
},
"id": "JiVQ3MdloFaf",
"outputId": "ded9108c-92d3-4c1d-855f-17634fd6f94b"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Observations Mean Sale price ($)\n",
"Store \n",
"L.A. Store 8 38.50\n",
"SD Mall 12 37.25"
],
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" <th></th>\n",
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" <tr>\n",
" <th>SD Mall</th>\n",
" <td>12</td>\n",
" <td>37.25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"\n",
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"\n",
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" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"source": [
"Grouping by Product category and showing a count of the number of observations and the sum of the Profit ($) for each row."
],
"metadata": {
"id": "lIMHumfmG0Gg"
}
},
{
"cell_type": "code",
"source": [
"data_crosstab = pd.crosstab(index=[],\n",
" values=data['Profit ($)'], \n",
" columns=data['Product category'],\n",
" margins = False,\n",
" aggfunc=[np.size, np.sum]).stack().loc['__dummy__'].\\\n",
" rename(columns={\"size\": \"Observations\", \"sum\": \"Total Profit ($)\"},errors=\"raise\", inplace=False)\n",
"data_crosstab"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 207
},
"id": "QPvoI0PupWfB",
"outputId": "86a18c98-7944-4931-93a5-78e3ec349de2"
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Observations Total Profit ($)\n",
"Product category \n",
"NS Game 4 16\n",
"Online Game 6 15\n",
"PS Game 4 17\n",
"Xbox Game 6 25"
],
"text/html": [
"\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Observations</th>\n",
" <th>Total Profit ($)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Product category</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>NS Game</th>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Online Game</th>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PS Game</th>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xbox Game</th>\n",
" <td>6</td>\n",
" <td>25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6685714-d379-49c2-8200-9987dc32f190')\"\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
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"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-d6685714-d379-49c2-8200-9987dc32f190 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-d6685714-d379-49c2-8200-9987dc32f190');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
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" const docLink = document.createElement('div');\n",
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" element.appendChild(docLink);\n",
" }\n",
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" "
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"source": [
"# Histogram\n",
"Create a histogram of Sales price ($) using the following intervals 0 to less than 20, 20 to less than 30, 30 to less than 40, 40 to less than 50."
],
"metadata": {
"id": "mk9xwrXjG4bm"
}
},
{
"cell_type": "code",
"source": [
"data['Sale price ($)'].tolist()"
],
"metadata": {
"id": "87VmOet01GXx",
"outputId": "7443b2be-067e-44c8-e29d-5a36b539483a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 36,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[49,\n",
" 45,\n",
" 35,\n",
" 45,\n",
" 49,\n",
" 39,\n",
" 46,\n",
" 15,\n",
" 20,\n",
" 40,\n",
" 45,\n",
" 39,\n",
" 39,\n",
" 49,\n",
" 20,\n",
" 45,\n",
" 39,\n",
" 20,\n",
" 56,\n",
" 20]"
]
},
"metadata": {},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.hist(data['Sale price ($)'].tolist(), bins=[20,30,40,50])\n",
"plt.show()"
],
"metadata": {
"id": "c1bBf_Aj0taL",
"outputId": "bc6d090c-876c-4388-db5d-1409f2ac6c2d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
}
},
"execution_count": 39,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALdElEQVR4nO3dX4yld13H8feH3SK0NBbspKnd1mmUaBoSWzJisYbERU2lBDQhpsQSNCSrCWgxRNh6gyaaQKL8uTAkawGbUK2klEBagzS0XHizMtuulu5CRFigdaFDIgJeUCtfL84zdRx3dp7dnWfP98y+X8lkz59nTr6//LLvOfvMOWdTVUiS+nrOvAeQJJ2eoZak5gy1JDVnqCWpOUMtSc3tneJBL7/88lpeXp7ioSVpVzpy5Mi3qmrpVPdNEurl5WVWV1eneGhJ2pWSfHWr+zz1IUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDVnqCWpOUMtSc1N8s5ESdNbPvjAvEfQJifedcskj+szaklqzlBLUnOGWpKaM9SS1JyhlqTmDLUkNWeoJak5Qy1JzRlqSWrOUEtSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmhsV6iS/n+TxJJ9P8jdJnjf1YJKkmW1DneQq4PeAlap6CbAHuHXqwSRJM2NPfewFnp9kL3Ax8G/TjSRJ2mjbUFfVk8CfAV8DTgL/UVWf3nxckgNJVpOsrq2t7fykknSBGnPq44XAa4FrgR8FLkly2+bjqupQVa1U1crS0tLOTypJF6gxpz5+EfhKVa1V1X8B9wE/N+1YkqR1Y0L9NeDGJBcnCfBK4Pi0Y0mS1o05R30YuBd4BHhs+J5DE88lSRrsHXNQVb0TeOfEs0iSTsF3JkpSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDVnqCWpOUMtSc0ZaklqzlBLUnOGWpKaM9SS1JyhlqTmDLUkNWeoJak5Qy1JzRlqSWrOUEtSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDU3KtRJLktyb5IvJDme5OVTDyZJmtk78rj3A5+qqtcleS5w8YQzSZI22DbUSX4YeAXwmwBV9TTw9LRjSZLWjTn1cS2wBnw4yaNJ7kxyyeaDkhxIsppkdW1tbccHlaQL1ZhQ7wVeCnygqm4A/hM4uPmgqjpUVStVtbK0tLTDY0rShWtMqJ8Anqiqw8P1e5mFW5J0Hmwb6qr6BvD1JD853PRK4NikU0mSnjX2VR+/C9w9vOLjy8BvTTeSJGmjUaGuqqPAysSzSJJOwXcmSlJzhlqSmjPUktScoZak5gy1JDVnqCWpOUMtSc0ZaklqzlBLUnOGWpKaM9SS1JyhlqTmDLUkNWeoJak5Qy1JzRlqSWrOUEtSc2P/Ky5dwJYPPjDvEaQLms+oJak5Qy1JzRlqSWrOUEtSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDVnqCWpOUMtSc0ZaklqbnSok+xJ8miS+6ccSJL0f53JM+rbgeNTDSJJOrVRoU6yD7gFuHPacSRJm+0dedz7gLcDl251QJIDwAGAa6655qwHWj74wFl/ryTtRts+o07yauCpqjpyuuOq6lBVrVTVytLS0o4NKEkXujGnPm4CXpPkBHAPsD/JRyadSpL0rG1DXVV3VNW+qloGbgUeqqrbJp9MkgT4OmpJam/sLxMBqKrPAp+dZBJJ0in5jFqSmjPUktScoZak5gy1JDVnqCWpOUMtSc0ZaklqzlBLUnOGWpKaM9SS1JyhlqTmDLUkNWeoJak5Qy1JzRlqSWrOUEtSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDVnqCWpOUMtSc0ZaklqzlBLUnOGWpKaM9SS1JyhlqTmDLUkNWeoJam5bUOd5OokDyc5luTxJLefj8EkSTN7RxzzDPC2qnokyaXAkSQPVtWxiWeTJDHiGXVVnayqR4bL3wWOA1dNPZgkaeaMzlEnWQZuAA6f4r4DSVaTrK6tre3MdJKk8aFO8gLgY8Bbq+o7m++vqkNVtVJVK0tLSzs5oyRd0EaFOslFzCJ9d1XdN+1IkqSNxrzqI8AHgeNV9Z7pR5IkbTTmGfVNwBuA/UmODl+vmnguSdJg25fnVdU/ADkPs0iSTsF3JkpSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDVnqCWpOUMtSc0ZaklqzlBLUnOGWpKaM9SS1JyhlqTmDLUkNWeoJak5Qy1JzRlqSWrOUEtSc4Zakpoz1JLUnKGWpOYMtSQ1Z6glqTlDLUnNGWpJas5QS1JzhlqSmjPUktScoZak5gy1JDU3KtRJbk7yxSRfSnJw6qEkSf9r21An2QP8BfArwHXA65NcN/VgkqSZMc+oXwZ8qaq+XFVPA/cAr512LEnSur0jjrkK+PqG608AP7v5oCQHgAPD1e8l+eJZznQ58K2z/N5udstadss6wLV0tFvWQd59Tmv5sa3uGBPqUarqEHDoXB8nyWpVrezASHO3W9ayW9YBrqWj3bIOmG4tY059PAlcveH6vuE2SdJ5MCbUnwNenOTaJM8FbgU+Oe1YkqR12576qKpnkrwF+HtgD/Chqnp8wpnO+fRJI7tlLbtlHeBaOtot64CJ1pKqmuJxJUk7xHcmSlJzhlqSmptbqJNcneThJMeSPJ7k9uH2FyV5MMm/DH++cF4zjnWatfxRkieTHB2+XjXvWbeT5HlJ/jHJPw1r+ePh9muTHB4+RuBvh18st3WadfxVkq9s2JPr5z3rWEn2JHk0yf3D9YXak41OsZaF3JckJ5I8Nsy8Oty24w2b5zPqZ4C3VdV1wI3Am4e3ph8EPlNVLwY+M1zvbqu1ALy3qq4fvv5ufiOO9n1gf1X9NHA9cHOSG4F3M1vLTwD/DrxpjjOOsdU6AP5gw54cnd+IZ+x24PiG64u2JxttXgss7r78wjDz+uund7xhcwt1VZ2sqkeGy99ltmlXMXt7+l3DYXcBvzqfCcc7zVoWTs18b7h60fBVwH7g3uH29vtymnUspCT7gFuAO4frYcH2ZN3mtexCO96wFueokywDNwCHgSuq6uRw1zeAK+Y01lnZtBaAtyT55yQfWoTTOPDsP0uPAk8BDwL/Cny7qp4ZDnmCBfhBtHkdVbW+J3867Ml7k/zQHEc8E+8D3g78YLj+Iyzgngw2r2XdIu5LAZ9OcmT4GA2YoGFzD3WSFwAfA95aVd/ZeF/NXju4MM+CTrGWDwA/zuyf3ieBP5/jeKNV1X9X1fXM3oX6MuCn5jzSWdm8jiQvAe5gtp6fAV4EvGOOI46S5NXAU1V1ZN6znKvTrGXh9mXw81X1UmafLvrmJK/YeOdONWyuoU5yEbOw3V1V9w03fzPJlcP9VzJ7NtTeqdZSVd8cYvED4C+ZRW9hVNW3gYeBlwOXJVl/g9RCfYzAhnXcPJymqqr6PvBhFmNPbgJek+QEs0+v3A+8n8Xck/+3liQfWdB9oaqeHP58Cvg4s7l3vGHzfNVHgA8Cx6vqPRvu+iTwxuHyG4FPnO/ZztRWa1nfrMGvAZ8/37OdqSRLSS4bLj8f+CVm59wfBl43HNZ+X7ZYxxc2/AUKs3OH7fekqu6oqn1VtczsIxweqqrfYMH2BLZcy22LuC9JLkly6fpl4JeZzb3jDduxT887CzcBbwAeG84jAvwh8C7go0neBHwV+PU5zXcmtlrL64eXGRVwAvjt+Yx3Rq4E7srsP4x4DvDRqro/yTHgniR/AjzK7AdTZ1ut46EkS0CAo8DvzHPIc/QOFmtPTufuBdyXK4CPz362sBf466r6VJLPscMN8y3kktTc3H+ZKEk6PUMtSc0ZaklqzlBLUnOGWpKaM9SS1JyhlqTm/gebtgd76hRCUgAAAABJRU5ErkJggg==\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Scatterplots\n",
"Create a scatterplot showing Sales price (\\$) against Profit (\\$)"
],
"metadata": {
"id": "QBH7GUYhG9Q2"
}
},
{
"cell_type": "code",
"source": [
"data.plot.scatter(x='Sale price ($)',\n",
" y='Profit ($)')"
],
"metadata": {
"id": "mF9F2mV_GtQV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"outputId": "a2f2d8d0-0d87-4341-9e11-04cee1635264"
},
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f58825c1bd0>"
]
},
"metadata": {},
"execution_count": 44
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Yxy4KL8Y2Heg"
},
"execution_count": null,
"outputs": []
}
]
}
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