text 熊猫两列数据帧到10x10 seaborn热图
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{
"cells": [
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.uniform(0, 100, (100, 2)), columns=['x', 'y'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2 columns, 100 rows, values from [0, 100)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>32.088376</td>\n",
" <td>19.166888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>92.858241</td>\n",
" <td>63.693262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>62.786370</td>\n",
" <td>30.688734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>24.895171</td>\n",
" <td>5.133105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>36.205176</td>\n",
" <td>12.559904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>63.786683</td>\n",
" <td>3.549472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>88.458586</td>\n",
" <td>57.985039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>27.994611</td>\n",
" <td>73.838943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>62.141875</td>\n",
" <td>68.162163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>23.145051</td>\n",
" <td>68.755691</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x y\n",
"0 32.088376 19.166888\n",
"1 92.858241 63.693262\n",
"2 62.786370 30.688734\n",
"3 24.895171 5.133105\n",
"4 36.205176 12.559904\n",
"5 63.786683 3.549472\n",
"6 88.458586 57.985039\n",
"7 27.994611 73.838943\n",
"8 62.141875 68.162163\n",
"9 23.145051 68.755691"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create bins with labels"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"df['xbin'] = pd.cut(df.x, 10, labels=range(10))"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"df['ybin'] = pd.cut(df.y, 10, labels=range(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now value in each cell represents interval number (1 for [0,10), 2 for [10,20), etc.)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>xbin</th>\n",
" <th>ybin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>32.088376</td>\n",
" <td>19.166888</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>92.858241</td>\n",
" <td>63.693262</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>62.786370</td>\n",
" <td>30.688734</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>24.895171</td>\n",
" <td>5.133105</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>36.205176</td>\n",
" <td>12.559904</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>63.786683</td>\n",
" <td>3.549472</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>88.458586</td>\n",
" <td>57.985039</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>27.994611</td>\n",
" <td>73.838943</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>62.141875</td>\n",
" <td>68.162163</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>23.145051</td>\n",
" <td>68.755691</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x y xbin ybin\n",
"0 32.088376 19.166888 3 1\n",
"1 92.858241 63.693262 9 6\n",
"2 62.786370 30.688734 6 3\n",
"3 24.895171 5.133105 2 0\n",
"4 36.205176 12.559904 3 1\n",
"5 63.786683 3.549472 6 0\n",
"6 88.458586 57.985039 8 5\n",
"7 27.994611 73.838943 2 7\n",
"8 62.141875 68.162163 6 6\n",
"9 23.145051 68.755691 2 6"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"# We can remove cells with float values\n",
"del df['x']\n",
"del df['y']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One-hot encoding of `y` column to set of 10 columns. We leave `x` *as-is*."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"ndf = pd.get_dummies(df, prefix=['ybin'], columns=['ybin'])"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
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" <tr>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>1</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>23</th>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>24</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>25</th>\n",
" <td>7</td>\n",
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" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
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" <tr>\n",
" <th>93</th>\n",
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" <tr>\n",
" <th>95</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <th>96</th>\n",
" <td>0</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>98</th>\n",
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" <tr>\n",
" <th>99</th>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" xbin ybin_0 ybin_1 ybin_2 ybin_3 ybin_4 ybin_5 ybin_6 ybin_7 \\\n",
"0 3 0 1 0 0 0 0 0 0 \n",
"1 9 0 0 0 0 0 0 1 0 \n",
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"\n",
" ybin_8 ybin_9 \n",
"0 0 0 \n",
"1 0 0 \n",
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"3 0 0 \n",
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"\n",
"[100 rows x 11 columns]"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ndf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Group by `x` bins and sum one-hot encoded values"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ybin_0 ybin_1 ybin_2 ybin_3 ybin_4 ybin_5 ybin_6 ybin_7 ybin_8 \\\n",
"xbin \n",
"0 1 1 2 2 2 1 0 1 1 \n",
"1 0 0 1 1 0 0 1 1 1 \n",
"2 1 0 0 0 1 2 2 2 1 \n",
"3 1 4 1 0 1 3 1 2 3 \n",
"4 1 0 0 3 1 0 1 1 2 \n",
"5 0 1 0 1 1 0 1 1 0 \n",
"6 1 1 2 1 0 1 2 1 0 \n",
"7 2 3 0 0 0 0 0 0 2 \n",
"8 0 2 1 0 0 3 3 0 3 \n",
"9 0 0 1 1 0 0 5 1 0 \n",
"\n",
" ybin_9 \n",
"xbin \n",
"0 0 \n",
"1 2 \n",
"2 1 \n",
"3 1 \n",
"4 0 \n",
"5 1 \n",
"6 1 \n",
"7 2 \n",
"8 0 \n",
"9 1 "
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf = ndf.groupby('xbin').agg(np.sum)\n",
"gdf"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f289706c898>"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f28970d22b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(gdf, annot=True, fmt=\"d\", cmap='summer')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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