<jupyter_start><jupyter_text>CMU Dataset First Analysis    <NAME>   First, we'll try to get some first thoughts on the available data.  In this notebook, we will refer to the data provided by the CMU on https://covidcast.cmu.edu/surveys.html. The documentation for the related module can be found at: https://cmu-delphi.github.io/covidcast/covidcast-py/html/.  Downloading the dataset<jupyter_code>import covidcast
from datetime import date
from matplotlib import pyplot as plt
from IPython.display import Video<jupyter_output><empty_output><jupyter_text>**NB: We will use the American format for dates, ie "year, month, day".** Let's get smoothed estimates of COVID-like illness from our symptom survey, distributed through Facebook, for every county in the United States between 2020-05-01 and 2020-05-07:<jupyter_code>data = covidcast.signal("fb-survey", 
                        "smoothed_cli", 
                        date(2020, 5, 1), 
                        date(2020, 5, 7), 
                        geo_type="county")

data.head()<jupyter_output><empty_output><jupyter_text>Exploring data<jupyter_code>len(data)
data.columns
data.describe()<jupyter_output><empty_output><jupyter_text>Plotting with covidcast First plot by county:<jupyter_code>covidcast.plot_choropleth(data)
plt.show()<jupyter_output><empty_output><jupyter_text>Second plot by state:<jupyter_code>data_by_state = covidcast.signal("fb-survey", "smoothed_cli", start_day = date(2020,8,4), end_day = date(2020,8,4), geo_type = "state")
covidcast.plot_choropleth(data_by_state)
plt.show()<jupyter_output><empty_output><jupyter_text>Animations with covidplast<jupyter_code>data_by_county = covidcast.signal("fb-survey",
                         "smoothed_cli",
                         date(2020, 8, 1),
                         date(2020, 8, 7),
                         geo_type = "county")
covidcast.animate(data_by_county, "test_plot.mp4")
Video("test_plot.mp4", width=480, height=360, html_attributes="controls muted autoplay loop")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1053921568627451, 'fraction_numerical': 0.029411764705882353, 'mean_word_length': 3.9902200488997557, 'pattern_counts': {'":': 0, '<': 26, '<?xml version=': 0, '>': 26, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '214783', 'license': 'MIT', 'n_tokens_mistral': 727, 'n_tokens_neox': 658, 'n_words': 152, 'path': 'Part3-Article-NLP/notebooks_for_testing/CMU Dataset First Analysis.ipynb', 'repo_name': 'tonywu71/Excess-Mortality-Covid', 'size': '2041'}