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Google Colab comes with some sample data files. I am using the file path of the file I want to use and trying to access it with pandas.
The iris and tips sample data sets are also available in the pandas github repo here. R sample datasets. Since any dataset can be read via pd.read_csv(), it is possible to access all R's sample data sets by copying the URLs from this R data set repository. Additional ways of loading the R sample data sets include statsmodel
Deploy Sample Data from Composer Repository. In the Magento root directory, run following command: # bin/magento sampledata:deploy This command collects the dependencies from the suggest sections of the composer.json files of modules, which suggest to install sample data . To deploy sample data from the Magento composer repository without ...
It can handle various data types, you can give it strings to use for text, and it handles foreign keys. You can configure large numbers of rows (e.g. 900,000) for insert, and it is also DB agnostic so it should work for mySQL, SQL Server, etc.
Microsoft has published a "document generator" tool as a sample. This is an article that describes the architecture and operation of the sample app in some detail. If you just want to run the sample generation tool, click here and install the MSI. It's free. The source is available. Requires the .NET Framework to run. Works only with XSDs.
@JorisMeys: Agreed, except for the "as expected" part. Just because a data frame is implemented as a list internally, it doesn't mean it should behave as one. The [operator for data frames is a counterexample. Also, please tell me: Have you ever, just one single time, used sample to sample columns from a data frame? –
The SAMPLE clause will give you a random sample percentage of all rows in a table. For example, here we obtain 25% of the rows: SELECT * FROM emp SAMPLE(25) The following SQL (using one of the analytical functions) will give you a random sample of a specific number of each occurrence of a particular value (similar to a GROUP BY) in a table.
R sample datasets. Since any dataset can be read via pd.read_csv(), it is possible to access all R's sample data sets by copying the URLs from this R data set repository. Additional ways of loading the R sample data sets include statsmodel. import statsmodels.api as sm iris = sm.datasets.get_rdataset('iris').data and PyDataset
mysql_random_data_loader is a utility that connects to the mysql database and fills the specified table with random data. If foreign keys are present in the table, they will also be correctly filled. This utility has a cool feature, the speed of data generation can be limited.
You will have to run a df0.sample(n=5000) and df1.sample(n=5000) and then combine df0 and df1 into a dfsample dataframe. You can create df0 and df1 by df.filter() with some logic. If you provide sample data I can help you construct that logic.