<jupyter_start><jupyter_text>Generate dataThis is a minimal example notebook that generates some data and writes it to an output file that has been specified as a parameter.  Defining parametersLet's first create a cell that defines the parametersfor this notebook (in our case, the `output_file`). To this end, simply define a variable in a cell and add the tag `parameters` to the cell metadata. This is documented on the [papermill website](https://papermill.readthedocs.io/en/latest/usage-parameterize.html)The declared value serves as default parameter, and will be overwritten if the parameter is specified from the command line.<jupyter_code>output_file = "data.csv"<jupyter_output><empty_output><jupyter_text>This parameter can be manipulated from the `reportsrender` call: ```reportsrender 01_generate_data.ipynb 01_generate_data.html --params="output_file=results/data.csv"```  Generate dataIn this case, we will just download the *iris* example dataset and write it to a `csv` file.<jupyter_code>import pandas as pd
iris = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv")
iris.to_csv(output_file)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08091993185689948, 'fraction_numerical': 0.0034071550255536627, 'mean_word_length': 6.253086419753086, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '243985', 'license': 'MIT', 'n_tokens_mistral': 347, 'n_tokens_neox': 331, 'n_words': 125, 'path': 'analyses/01_generate_data.ipynb', 'repo_name': 'grst/universal_analysis_pipeline', 'size': '1174'}