Open-Data | Using HashData to analyze a series | Time Series Database library

Not 100% sure this is what you're after, but you can simply mosaic() the 3 images into one image, and then combine the two datasets into a new ImageCollection. UPDATE: Use addBands() instead:

I found that link super useful and figured it's worth breaking out from the comments and summarizing:

Reducing Pandas memory usage #1: lossless compression

1. Load only columns of interest with usecols

Using PRAGMA table_info(Metal) will not fail if the table doesn't exist. Instead it will show no rows. Therefore it could be misleading and it probably is misleading you.

What it is showing is that the database itself exists. However, just because the database exists does not mean that the copy itself worked. As you are only showing the database helper and not what use is made of an instance there are various issues that could be the cause.

I suspect that you have had issues and have inadvertently created a database that is empty (as far as user defined tables). This would allow you to invoke the getAllMetal and get the result you have said.

Another issue is that DB_PATH as per using this.DB_PATH = context.getApplicationInfo().dataDir + "/"; will not be the same as File databasePath = myContext.getDatabasePath(DB_NAME);. The they will be

• /data/data//ChiffrageBDD.db and
• /data/data//databases/ChiffrageBDD.db
  • reflecting the package name used

Accidentally creating the database, and it being empty, is quite easy with Android and hence why I suspect the existence of an empty database is your issue. Once created the database persists(always exists unless you specifically delete it) which may also be part of the issue(s).

Without knowing all that you have done to date and also without knowing how you utilise the DataBaseHelper instance it is only a guess as to the cause.

The DataBaseHelper you have does have an issue in that it does not cater for a fresh install of the App. When an App is installed the /data/data/ directory exists BUT the databases directory does not. The copyDataBase method will then fail because the databases directory does not exist.

Here's a working database helper, you may wish to compare the differences between it an what you are using. They are subtle but important:-

pd.json_normalize(data, 'lineup') should do what you want. If you want team_id and team_name, put pd.json_normalize(data, 'lineup', ['team_id', 'team_name']).

Check the json_normalize documentation examples for more info.

You have few small errors in your code:

1. You check only if Open-Close is smaller then 0.02, and forget to check for absolute value (if open=5 and close=8 and still smaller then 0.02)
2. "openclosehigh" and "opencloselow" are different in your code from what you say they are suppose to be. To take into consideration only close price.

I personally prefer to work with pandas directly instead of where since it's unneeded - you have a simple condition.

Check the following example:

Let's clarify this part, somehow you cannot access to the database engine.

The SQL Server supports two authentication modes:

• Windows Authentication
• SQL Server Authentication

First, you tried to used Windows Authentication, and then SQL Server Authentication.

In the connection string Trusted_Connection=True or Integrated Security=true means that connection will use Windows Authentication. So, if there is one of these two parameters then you don't need to specify User and Password.

To check your server name connect to the SQL Engine through SQL Server Management Studio, and execute the following query:

Working with your example, you can create dfs to store the mean and standard deviation using .groupby, then access these in a lambda function:

This error is related with the bug that affected pandas version 1.1.0 and some versions prior to 1.0.5. It has been fixed in version 1.1.3.

Therefore, to make it go away it is recommended to upgrade pandas to version 1.1.3.

The bug does not manifest in smaller datasets (or the ones not loaded from CSV).

Looks like you are using quanteda, so get rid of the tm part in your code, the corpus line.

You can use dfm_remove to get rid of the stopwords.