bob | Minimalist-omakase build tool for node.js projects | Runtime Evironment library

You can create a DeepReplace utility that would recursively check and replace keys. Also I'd strongly suggest to only replace value and make sure the key will stay same.

From this toy Dataframe :

Read the child table's primary and foreign keys into a map where the keys are the foreign keys and the values are the primary keys. Keep in mind that one foreign key can map to multiple primary keys if this is a one to many relationship.

Now iterate over the primary keys of the mother table and for each primary key check whether it exists in the map. If so, you add a tuple of the primary keys of the rows that have a relation to the array (or however you want to save it).

The time complexity is O(n + m). Iterate over the rows of each table once. Since the lookup in the map is constant, we don't need to add it.

Space complexity is O(m) where m is the number of rows in the child table. This is some additional space you use in comparison to the naive solution to improve the time complexity.

One work around would be to use your map2_dfr code, but then bind the columns that are needed to the map2_dfr output. Though you still have to drop the names column.

First, handle the ranges and select the lower value, then create two boolean masks for k and % separately and then apply all the related logic. For example:

One way I can think of is to make a mutable copy of a, and try to remove every element of b, then check if all elements of b can be removed:

It seems that CryptoZombies are emulating a deprecated version of web3 (I'm guessing also by their use of sendAsync - which is in the CZ tutorial, not in your code), where this could have worked.

But, as documented in this GitHub issue, web3.currentProvider.send() now expects a callback param, and isn't able to resolve using await.

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:

you have to lock the operation and dont release it before insertion ends.

you can use a file on filesystem, but as @chris Hass suggested you can use symfony's package like this:

install symfony lock: