Numpy 2D array for HTML table?

Question

Here is my numpy array:

num = np.array([[ 0.17899619 0.33093259 0.2076353 0.06130814] [ 0.20392888 0.42653105 0.33325891 0.10473969] [ 0.17038247 0.19081956 0.10119709 0.09032416] [-0.10606583 -0.13680513 -0.13129103 -0.03684349] [ 0.20319428 0.28340985 0.20994867 0.11728491] [ 0.04396872 0.23703525 0.09359683 0.11486036] [ 0.27801304 -0.05769304 -0.06202813 0.04722761]]) 

Here is my title bar:

 days = ['5 days', '10 days', '20 days', '60 days'] 

And here is my first column:

 prices = ['AAPL', 'ADBE', 'AMD', 'AMZN', 'CRM', 'EXPE', 'FB'] 

I want to put all this in one HTML table as follows:

 <table border=1> <tr> <th>Prices</th><th>5 days</th><th>10 days</th><th>20 days</th><th>60 days</th> </tr> <tr> <td>APPL</td><td>0.17899619</td><td>0.33093259</td><td>0.2076353</td><td>0.06130814</td> </tr> <tr> <td>ADBE</td><td>0.20392888</td><td>0.42653105</td><td>0.33325891</td><td>0.10473969</td> </tr> <tr> <td>AMD</td><td>0.17038247</td><td>0.19081956</td><td>0.10119709</td><td>0.09032416</td> </tr> <tr> <td>AMZN</td><td>-0.10606583</td><td>-0.13680513</td><td>-0.13129103</td><td>-0.03684349</td> </tr> <tr> <td>CRM</td><td>0.20319428</td><td>0.28340985</td><td>0.20994867</td><td>0.11728491</td> </tr> <tr> <td>EXPE</td><td>0.04396872</td><td>0.23703525</td><td>0.09359683</td><td>0.11486036</td> </tr> <tr> <td>FB</td><td>0.27801304</td><td>-0.05769304</td><td>-0.06202813</td><td>0.04722761</td> </tr> </table> 

Is there any pythonic way to do this? Or is there any module I can use (in Python 3)?

Thanks.

Answer 1

pandas DataFrame has a to_html method:

 import numpy as np import pandas as pd num = np.array([[ 0.17899619, 0.33093259, 0.2076353, 0.06130814,], [ 0.20392888, 0.42653105, 0.33325891, 0.10473969,], [ 0.17038247, 0.19081956, 0.10119709, 0.09032416,], [-0.10606583, -0.13680513, -0.13129103, -0.03684349,], [ 0.20319428, 0.28340985, 0.20994867, 0.11728491,], [ 0.04396872, 0.23703525, 0.09359683, 0.11486036,], [ 0.27801304, -0.05769304, -0.06202813, 0.04722761,],]) days = ['5 days', '10 days', '20 days', '60 days'] prices = ['AAPL', 'ADBE', 'AMD', 'AMZN', 'CRM', 'EXPE', 'FB'] df = pd.DataFrame(num, index=prices, columns=days) html = df.to_html() print html 

Output:

 <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>5 days</th> <th>10 days</th> <th>20 days</th> <th>60 days</th> </tr> </thead> <tbody> <tr> <th>AAPL</th> <td> 0.178996</td> <td> 0.330933</td> <td> 0.207635</td> <td> 0.061308</td> </tr> <tr> <th>ADBE</th> <td> 0.203929</td> <td> 0.426531</td> <td> 0.333259</td> <td> 0.104740</td> </tr> <tr> <th>AMD</th> <td> 0.170382</td> <td> 0.190820</td> <td> 0.101197</td> <td> 0.090324</td> </tr> <tr> <th>AMZN</th> <td>-0.106066</td> <td>-0.136805</td> <td>-0.131291</td> <td>-0.036843</td> </tr> <tr> <th>CRM</th> <td> 0.203194</td> <td> 0.283410</td> <td> 0.209949</td> <td> 0.117285</td> </tr> <tr> <th>EXPE</th> <td> 0.043969</td> <td> 0.237035</td> <td> 0.093597</td> <td> 0.114860</td> </tr> <tr> <th>FB</th> <td> 0.278013</td> <td>-0.057693</td> <td>-0.062028</td> <td> 0.047228</td> </tr> </tbody> </table> 

Answer 2

Jupyter laptop users should find this helpful. Let me know in the comments if anyone ever implements this as the right module!

 import numpy as np from html import escape class PrettyArray(np.ndarray): def _repr_html_(self): """Numpy array HTML representation function.""" # Fallbacks for cases where we cannot format HTML tables if self.size > 10_000: return f"Large numpy array {self.shape} of {self.dtype}" if self.ndim != 2: return f"<pre>{escape(str(self))}</pre>" # Table format html = [f"<table><tr><th>{self.dtype}"] rows, columns = self.shape html += (f"<th>{j}" for j in range(columns)) for i in range(rows): html.append(f"<tr><th>{i}") for j in range(columns): val = self[i, j] html.append("<td>") html.append(escape(f"{val:.2f}" if self.dtype == float else f"{val}")) html.append("</table>") return "".join(html) 

We use the Numpy view function to enable this interface:

 np.array([[1, 2], [3, 4]]).view(PrettyArray) 

The trick is that this type adheres, and as long as one of your arrays has an established view, the result will always be PrettyArray. For example, while still printing an HTML table with the result of the second line:

 arr = np.random.normal(size=(4, 8)).view(PrettyArray) np.eye(4) @ arr**2 @ np.eye(8)