alex | Automata Learning EXperience | Machine Learning library

MongoDB Compass has known issues on exporting an importing data for long time and it seems they are not willing to improve it!

When you try to export data using compass, it uses some sample documents to select the fields and if you are unlucky enough, you will miss some fields.

SOLUTION:

1. Use the Mongo DB Compass Aggregation tab to find all the existing fields in all documents:

   [{$project: { arrayofkeyvalue: { $objectToArray: '$$ROOT'} }},
   {$unwind: '$arrayofkeyvalue'},
   {$group: { _id: null, allkeys: { $addToSet: '$arrayofkeyvalue.k' } }}]

2. Add the fields from the 1st step to the Export Full Collection (Select Fields).

3. Export it!

From the node-fetch package readme:

node-fetch is an ESM-only module - you are not able to import it with require. We recommend you stay on v2 which is built with CommonJS unless you use ESM yourself. We will continue to publish critical bug fixes for it.

If you want to require it, then downgrade to v2.

The other option you have is to use async import('node-fetch').then(...)

What I've done here is remove the borders from the table and collapsed the space for them.

I've then used more semantic elements for both table headings and your actual content with semantic class names. This included adding a new element for the elements you want at the bottom of the cell. Finally, the teacher and codes are floated left and right respectively.

I am not much familiar with Jinja, so this answer might not be the most efficient one.

By using basic hard coding in your Template.HTML file, I was able to achieve the results you are trying to. For this, I used the same code that was given by D-E-N in your previous question.

I combined all the attributes of your object into a string

• An attribute is differentiated from another with @ (like Course and Teacher)
• Instead of using space character, I used a _ character to represent space character in the attributes.
• If one slot contains multiple objects, they are differentiated with space character (just like in the code provided by D-E-N)

Here's the updated code of yours with these changes,

App Service runs your apps in a sandbox and most PDF libraries will fail. Looking at the IronPDF documentation, they say that you can run it in a VM or a container. Since you already are using App Service, simply package your app in a container, publish it to a container registry and configure App Service to run it.

Use add_prefix. This function is applied to the whole dataframe. To hide columns you dont need renamed, set them as index. After renaming you can reset back the index. Code below

What you are looking for is actually called a cross merge, available from Pandas 1.2.0+:

You need to convert what you have into list of flat tuples or flat list before feeding into writerow, for example

Most of the solutions here would be used with melt, so to know the method melt, see the documentaion explanation

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

And the parameters are:

Parameters

• id_vars : tuple, list, or ndarray, optional

  Column(s) to use as identifier variables.

• value_vars : tuple, list, or ndarray, optional

  Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

• var_name : scalar

  Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

• value_name : scalar, default ‘value’

  Name to use for the ‘value’ column.

• col_level : int or str, optional

  If columns are a MultiIndex then use this level to melt.

• ignore_index : bool, default True

  If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

  New in version 1.1.0.

Melting merges multiple columns and converts the dataframe from wide to long, for the solution to Problem 1 (see below), the steps are:

1. First we got the original dataframe.

2. Then the melt firstly merges the Math and English columns and makes the dataframe replicated (longer).

3. Then finally adds the column Subject which is the subject of the Grades columns value respectively.

This is the simple logic to what the melt function does.

I will solve my own questions.

Problem 1 could be solve using pd.DataFrame.melt with the following code: