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Virtual Memory Usage from Java under Linux, too much memory used
...bytes! Why does it need that?" Well, here's a way to make Hello World on a 64-bit JVM claim to take over 4 gigabytes ... at least by one form of measurement.
java -Xms1024m -Xmx4096m com.example.Hello
Different Ways to Measure Memory
On Linux, the top command gives you several different numbers...
Change column type from string to float in Pandas
...g to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64
As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:
# convert Series
my_series = pd.to_numeric(my_series)
# convert column "a" of a DataFrame
df["a"...
MQTT物联网协议完全实践指南 · App Inventor 2 中文网
...源
MQTT扩展下载：
de.ullisroboterseite.ursai2pahomqtt.aix
完整示例项目：
mqtt_demo.aia
MQTT协议深度解析
1. 协议架构与通信模式
MQTT采用客户端-服务器架构，基于发布/订阅（Publish/Subscribe）...
NumPy or Pandas: Keeping array type as integer while having a NaN value
...ferred way to keep the data type of a numpy array fixed as int (or int64 or whatever), while still having an element inside listed as numpy.NaN ?
...
How to printf uint64_t? Fails with: “spurious trailing ‘%’ in format”
I wrote a very simple test code of printf uint64_t:
3 Answers
3
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how to get program files x86 env variable?
...the location of Program Files (x86) in command prompt. I'm using Windows 7 64bit.
4 Answers
...
What is the equivalent of bigint in C#?
...
That corresponds to the long (or Int64), a 64-bit integer.
Although if the number from the database happens to be small enough, and you accidentally use an Int32, etc., you'll be fine. But the Int64 will definitely hold it.
And the error you get if you use so...
Load RSA public key from file
...hods with this answer for getting bytes from files: stackoverflow.com/a/21264593/3680466
– cloudsurfin
Mar 15 '16 at 0:21
2
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How can I set Image source with base64
I want to set the Image source to a base64 source but it does not work:
4 Answers
4
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get list of pandas dataframe columns based on data type
...1 2.3456 c d 78
[1 rows x 5 columns]
>>> df.dtypes
A int64
B float64
C object
D object
E int64
dtype: object
>>> g = df.columns.to_series().groupby(df.dtypes).groups
>>> g
{dtype('int64'): ['A', 'E'], dtype('float64'): ['B'], dtype('O'): ['C', 'D'...