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Why am I getting a “401 Unauthorized” error in Maven?
Why am I getting a "401 Unauthorized" error in Maven?
21 Answers
21
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Is there a way to detect if an image is blurry?
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answered Oct 14 '11 at 10:01
Simon BergotSimon Bergot
9,08866 gold badges3131 silver badges5353 bronze badges
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SSH library for Java [closed]
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answered Jun 15 '09 at 14:27
David RabinowitzDavid Rabinowitz
27.2k1313 gold badges8585 silver badges123123 bronze badges
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Algorithm for Determining Tic Tac Toe Game Over
...;
//check end conditions
//check col
for(int i = 0; i < n; i++){
if(board[x][i] != s)
break;
if(i == n-1){
//report win for s
}
}
//check row
for(int i = 0; i < n; i++){
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Java: parse int value from a char
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edited Apr 30 '18 at 4:24
Neuron
3,54333 gold badges2323 silver badges4040 bronze badges
a...
CSS triangle custom border color
...th a 1px border (around the angled sides of the triangle) with color #CAD5E0. Is this possible? Here's what I have so far:
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Minimizing NExpectation for a custom distribution in Mathematica
...f we plot pdf2 it looks exactly as your Plot
Plot[pdf2[3.77, 1.34, -2.65, 0.40, x], {x, 0, .3}]
Now to the expected value. If I understand it correctly we have to integrate x * pdf[x] from -inf to +inf for a normal expected value.
x * pdf[x] looks like
Plot[pdf2[3.77, 1.34, -2.65, 0.40, x]*x...
Logical operators for boolean indexing in Pandas
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When you say
(a['x']==1) and (a['y']==10)
You are implicitly asking Python to convert (a['x']==1) and (a['y']==10) to boolean values.
NumPy arrays (of length greater than 1) and Pandas objects such as Series do not have a boolean value -- in other words, they ...
How does this CSS produce a circle?
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How does a border of 180 pixels with height/width-> 0px become a circle with a radius of 180 pixels?
Let's reformulate that into two questions:
Where do width and height actually apply?
Let's have a look at the areas of a typical box (source...
How to filter Pandas dataframe using 'in' and 'not in' like in SQL
...where)
As a worked example:
import pandas as pd
>>> df
country
0 US
1 UK
2 Germany
3 China
>>> countries_to_keep
['UK', 'China']
>>> df.country.isin(countries_to_keep)
0 False
1 True
2 False
3 True
Name: country, dtype: bool
>>&gt...