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Syntax for creating a two-dimensional array
...ti[0] = new int[10];
multi[1] = new int[10];
multi[2] = new int[10];
multi[3] = new int[10];
multi[4] = new int[10];
Note that every element will be initialized to the default value for int, 0, so the above are also equivalent to:
int[][] multi = new int[][]{
{ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
...
Selecting pandas column by location
... mind:
>>> df
A B C D
0 0.424634 1.716633 0.282734 2.086944
1 -1.325816 2.056277 2.583704 -0.776403
2 1.457809 -0.407279 -1.560583 -1.316246
3 -0.757134 -1.321025 1.325853 -2.513373
4 1.366180 -1.265185 -2.184617 0.881514
>>> df.iloc[:, ...
Searching for UUIDs in text with regex
...IDs), there are five equivalent string representations for a GUID:
"ca761232ed4211cebacd00aa0057b223"
"CA761232-ED42-11CE-BACD-00AA0057B223"
"{CA761232-ED42-11CE-BACD-00AA0057B223}"
"(CA761232-ED42-11CE-BACD-00AA0057B223)"
"{0xCA761232, 0xED42, 0x11CE, {0xBA, 0xCD, 0x00, 0xAA, 0x00, 0x57, 0...
pandas DataFrame: replace nan values with average of columns
...
283
You can simply use DataFrame.fillna to fill the nan's directly:
In [27]: df
Out[27]:
...
How to validate IP address in Python? [duplicate]
...
answered Nov 25 '08 at 23:50
DustinDustin
78.2k1717 gold badges103103 silver badges131131 bronze badges
...
Why is it slower to iterate over a small string than a small list?
...n a list of small single character strings. Any explanation? It's almost 1.35 times as much time.
3 Answers
...
cartesian product in pandas
...import DataFrame, merge
df1 = DataFrame({'key':[1,1], 'col1':[1,2],'col2':[3,4]})
df2 = DataFrame({'key':[1,1], 'col3':[5,6]})
merge(df1, df2,on='key')[['col1', 'col2', 'col3']]
Output:
col1 col2 col3
0 1 3 5
1 1 3 6
2 2 4 5
3 2 4 6
See here...
Convert pandas dataframe to NumPy array
...50
cs95
231k6060 gold badges390390 silver badges455455 bronze badges
answered May 5 '16 at 5:29
User456898User...
Matplotlib scatter plot with different text at each data point
... could use annotate() while iterating over the values in n.
y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
z = [0.15, 0.3, 0.45, 0.6, 0.75]
n = [58, 651, 393, 203, 123]
fig, ax = plt.subplots()
ax.scatter(z, y)
for i, txt in enumerate(n):
ax.annotate(txt, (z[i], y[i]))
There are a lot of...
Get operating system info
...t 10/i' => 'Windows 10',
'/windows nt 6.3/i' => 'Windows 8.1',
'/windows nt 6.2/i' => 'Windows 8',
'/windows nt 6.1/i' => 'Windows 7',
'/windows nt 6.0/i' =>...