fletcher | Very lightweight Javascript module loader

House Number Street First Name Surname Age Relationship to Head of House Marital Status Gender Occupation Infirmity Religion 0 1 Smith Radial Grace Patel 46 Head Widowed Female Petroleum engineer None Catholic 1 1 Smith Radial Ian Nixon 24 Lodger Single Male Publishing rights manager None Christian 2 2 Smith Radial Frederick Read 87 Head Divorced Male Retired TEFL teacher None Catholic 3 3 Smith Radial Daniel Adams 58 Head Divorced Male Therapist, music None Catholic 4 3 Smith Radial Matthew Hall 13 Grandson NaN Male Student None NaN 5 3 Smith Radial Steven Fletcher 9 Grandson NaN Male Student None NaN 6 4 Smith Radial Alison Jenkins 38 Head Single Female Physiotherapist None Catholic 7 4 Smith Radial Kelly Jenkins 12 Daughter NaN Female Student None NaN 8 5 Smith Radial Kim Browne 69 Head Married Female Retired Estate manager/land agent None Christian 9 5 Smith Radial Oliver Browne 69 Husband Married Male Retired Merchandiser, retail None None

I have a dataset which you can see up the side of the question. I want to convert all these datasets to integers and strings from objects.

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House Number Street First Name Surname Age Relationship to Head of House Marital Status Gender Occupation Infirmity Religion 0 1 Smith Radial Grace Patel 46 Head Widowed Female Petroleum engineer None Catholic 1 1 Smith Radial Ian Nixon 24 Lodger Single Male Publishing rights manager None Christian 2 2 Smith Radial Frederick Read 87 Head Divorced Male Retired TEFL teacher None Catholic 3 3 Smith Radial Daniel Adams 58 Head Divorced Male Therapist, music None Catholic 4 3 Smith Radial Matthew Hall 13 Grandson NaN Male Student None NaN 5 3 Smith Radial Steven Fletcher 9 Grandson NaN Male Student None NaN 6 4 Smith Radial Alison Jenkins 38 Head Single Female Physiotherapist None Catholic 7 4 Smith Radial Kelly Jenkins 12 Daughter NaN Female Student None NaN 8 5 Smith Radial Kim Browne 69 Head Married Female Retired Estate manager/land agent None Christian 9 5 Smith Radial Oliver Browne 69 Husband Married Male Retired Merchandiser, retail None None

Hello,

I have a dataset that you can see below. When I tried to convert Age to int. I got that error: ValueError: invalid literal for int() with base 10: '43.54302670766108'

This means there is float data inside that data. I tried to replace '.' to '0' then tried to convert but I failed. Could you help me to do that?

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I have POJO class:

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It seems to me that Pandas ExtensionArrays would be one of the cases where a simple example to get one started would really help. However, I have not found a simple enough example anywhere.

Creating an ExtensionArray

To create an ExtensionArray, you need to

• Create an ExtensionDtype and register it
• Create an ExtensionArray by implementing the required methods.

There is also a section in the Pandas documentation with a brief overview.

Example implementations

There are many examples of implementations:

• Pandas' own internal extension arrays
• Geopandas' GeometryArray
• Pandas documentation has a list of projects with extension data types
  • e.g. CyberPandas' IPArray
• Many others around the web, for example Fletcher's StringSupportingExtensionArray

Question

Despite having studied all of the above, I still find extension arrays difficult to understand. All of the examples have a lot of specifics and custom functionality that makes it difficult to work out what is actually necessary. I suspect many have faced a similar problem.

I am thus asking for a simple and minimal example of a working ExtensionArray. The class should pass all the tests Pandas have provided to check that the ExtensionArray behaves as expected. I've provided an example implementation of the tests below.

To have a concrete example, let's say I want to extend ExtensionArray to obtain an integer array that is able to hold NA values. That is essentially IntegerArray, but stripped of any actual functionality beyond the basics of ExtensionArray.

Testing the solution

I have used the following fixtures & tests to test the validity of the solution. These are based on the directions in the Pandas documentation

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I want to know the best way of getting indexes from huge (array length is nearly 150 000) JSON Array of JSON objects that matches with array of JSON objects.

Currently I'm using for loop to do this job but it is taking 3 to 4 minutes of time. Is there any best way that I can improve the performance? Below is an example that illustrate my requirement.

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I'm trying to implement the checksum from the WinHex program, in WinHex and in 010 editor its called

Checksum Uint 32bit

For example, we calculate the checksum of 32byte (represented in HEX) data:

1122334455667788991122334455667711223344556677889911223344556677

The WinHex gives us checksum32 value:

EE65DE86

I was thinking it's just about summing each byte to the previous one:

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I am being asked to do a checksum on this text with the bit size being 16: "AAAAAAAAAA\nX"

At first the description seemed like it wanted the Fletcher-16 checksum. But the output of Fletcher's checksum performed on the above text yielded 8aee in hex. The example file says that the modular sum algorithm (minus the two's complement) should output 509d in hex.

The only other info is the standard "every two characters should be added to the checksum."

Besides using the generic Fletcher-16 checksum provided on the corresponding Wikipedia page, I have tried using this solution found here: calculating-a-16-bit-checksum to no avail. This code produced the hex value of 4f27.

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I just uploaded images via multer which created a URL path stored in MongoDB. However, the URL comes with a double backslash like so in Postman :

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