{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ " %matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Caractéristiques de l'installation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hideCode": false, "hideOutput": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uname_result(system='Linux', node='e38cee50978d', release='4.4.0-164-generic', version='#192-Ubuntu SMP Fri Sep 13 12:02:50 UTC 2019', machine='x86_64', processor='x86_64')\n" ] } ], "source": [ "import platform\n", "print(platform.uname())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alembic==1.3.3\n", "asn1crypto==1.3.0\n", "async-generator==1.10\n", "attrs==19.3.0\n", "backcall==0.1.0\n", "beautifulsoup4==4.6.3\n", "bleach==3.1.0\n", "blinker==1.4\n", "bokeh==0.12.16\n", "certifi==2020.4.5.1\n", "certipy==0.1.3\n", "cffi==1.13.2\n", "chardet==3.0.4\n", "cloudpickle==0.5.6\n", "conda==4.8.2\n", "conda-package-handling==1.6.0\n", "cryptography==2.5\n", "cycler==0.10.0\n", "Cython==0.28.5\n", "cytoolz==0.10.1\n", "dask==2.11.0\n", "decorator==4.4.1\n", "defusedxml==0.6.0\n", "dill==0.2.9\n", "entrypoints==0.3\n", "fastcache==1.1.0\n", "gmplot==1.2.0\n", "gmpy2==2.1.0b1\n", "h5py==2.7.1\n", "hide-code==0.5.6\n", "idna==2.9\n", "imageio==2.8.0\n", "inflect==4.0.0\n", "ipykernel==5.1.4\n", "ipython==7.12.0\n", "ipython-genutils==0.2.0\n", "ipywidgets==7.2.1\n", "isoweek==1.3.3\n", "jaraco.itertools==5.0.0\n", "jedi==0.16.0\n", "Jinja2==2.11.0\n", "json5==0.8.5\n", "jsonschema==3.0.2\n", "jupyter==1.0.0\n", "jupyter-client==6.0.0\n", "jupyter-console==6.1.0\n", "jupyter-core==4.6.3\n", "jupyter-telemetry==0.0.4\n", "jupyterhub==0.8.1\n", "jupyterlab==1.2.5\n", "jupyterlab-server==1.0.6\n", "kiwisolver==1.1.0\n", "llvmlite==0.23.0\n", "Mako==1.1.0\n", "MarkupSafe==1.1.1\n", "matplotlib==2.2.3\n", "mistune==0.8.4\n", "more-itertools==8.2.0\n", "mpmath==1.1.0\n", "nbconvert==5.6.1\n", "nbformat==5.0.4\n", "nbgit==0.0.1\n", "networkx==2.4\n", "notebook==6.0.3\n", "numba==0.38.1\n", "numexpr==2.6.9\n", "numpy==1.15.2\n", "oauthlib==3.0.1\n", "olefile==0.46\n", "packaging==20.1\n", "pamela==1.0.0\n", "pandas==0.22.0\n", "pandocfilters==1.4.2\n", "parso==0.6.0\n", "patsy==0.5.1\n", "pdfkit==0.6.1\n", "pexpect==4.8.0\n", "pickleshare==0.7.5\n", "Pillow==7.0.0\n", "prometheus-client==0.7.1\n", "prompt-toolkit==3.0.3\n", "protobuf==3.11.3\n", "ptyprocess==0.6.0\n", "pycosat==0.6.3\n", "pycparser==2.19\n", "pycurl==7.43.0.1\n", "Pygments==2.5.2\n", "PyJWT==1.7.1\n", "pyOpenSSL==19.0.0\n", "pyparsing==2.4.6\n", "pyrsistent==0.15.7\n", "PySocks==1.7.1\n", "python-dateutil==2.8.1\n", "python-editor==1.0.4\n", "python-json-logger==0.1.11\n", "python-oauth2==1.1.1\n", "pytz==2019.3\n", "PyWavelets==1.1.1\n", "PyYAML==5.3\n", "pyzmq==17.1.2\n", "qtconsole==4.6.0\n", "requests==2.23.0\n", "rpy2==2.9.4\n", "ruamel-yaml==0.15.80\n", "scikit-image==0.14.3\n", "scikit-learn==0.19.2\n", "scipy==1.1.0\n", "seaborn==0.8.1\n", "Send2Trash==1.5.0\n", "sh==1.12.14\n", "simplegeneric==0.8.1\n", "six==1.14.0\n", "SQLAlchemy==1.2.18\n", "statsmodels==0.9.0\n", "sympy==1.1.1\n", "terminado==0.8.3\n", "testpath==0.4.4\n", "toolz==0.10.0\n", "tornado==6.0.3\n", "tqdm==4.42.0\n", "traitlets==4.3.3\n", "tzlocal==2.0.0\n", "urllib3==1.25.7\n", "vincent==0.4.4\n", "wcwidth==0.1.8\n", "webencodings==0.5.1\n", "widgetsnbextension==3.2.1\n", "xlrd==1.2.0\n", "zipp==2.1.0\n" ] } ], "source": [ "%%sh\n", "pip freeze" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name: matplotlib\n", "Version: 2.2.3\n", "Summary: Python plotting package\n", "Home-page: http://matplotlib.org\n", "Author: John D. Hunter, Michael Droettboom\n", "Author-email: matplotlib-users@python.org\n", "License: BSD\n", "Location: /opt/conda/lib/python3.6/site-packages\n", "Requires: numpy, cycler, pyparsing, python-dateutil, pytz, six, kiwisolver\n", "Required-by: seaborn, scikit-image\n", " \n", "Name: pandas\n", "Version: 0.22.0\n", "Summary: Powerful data structures for data analysis, time series,and statistics\n", "Home-page: http://pandas.pydata.org\n", "Author: None\n", "Author-email: None\n", "License: BSD\n", "Location: /opt/conda/lib/python3.6/site-packages\n", "Requires: python-dateutil, pytz, numpy\n", "Required-by: vincent, seaborn\n", " \n", "Name: isoweek\n", "Version: 1.3.3\n", "Summary: Objects representing a week\n", "Home-page: http://github.com/gisle/isoweek\n", "Author: Gisle Aas\n", "Author-email: gisle@aas.no\n", "License: BSD\n", "Location: /opt/conda/lib/python3.6/site-packages\n", "Requires: \n", "Required-by: \n" ] } ], "source": [ "%%sh\n", "pip show matplotlib\n", "echo \" \"\n", "pip show pandas\n", "echo \" \"\n", "pip show isoweek" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hideCode": false, "hideOutput": false, "hidePrompt": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IPython 7.12.0\n", "IPython.core (unknown version)\n", "IPython.core.alias (unknown version)\n", "IPython.core.application (unknown version)\n", "IPython.core.async_helpers (unknown version)\n", "IPython.core.autocall (unknown version)\n", "IPython.core.builtin_trap (unknown version)\n", "IPython.core.compilerop (unknown version)\n", "IPython.core.completer (unknown version)\n", "IPython.core.completerlib (unknown version)\n", "IPython.core.crashhandler (unknown version)\n", "IPython.core.debugger (unknown version)\n", "IPython.core.display (unknown version)\n", "IPython.core.display_trap (unknown version)\n", "IPython.core.displayhook (unknown version)\n", "IPython.core.displaypub (unknown version)\n", "IPython.core.error (unknown version)\n", "IPython.core.events (unknown version)\n", "IPython.core.excolors (unknown version)\n", "IPython.core.extensions (unknown version)\n", "IPython.core.formatters (unknown version)\n", "IPython.core.getipython (unknown version)\n", "IPython.core.history (unknown version)\n", "IPython.core.hooks (unknown version)\n", "IPython.core.inputtransformer2 (unknown version)\n", "IPython.core.interactiveshell (unknown version)\n", "IPython.core.latex_symbols (unknown version)\n", "IPython.core.logger (unknown version)\n", "IPython.core.macro (unknown version)\n", "IPython.core.magic (unknown version)\n", "IPython.core.magic_arguments (unknown version)\n", "IPython.core.magics (unknown version)\n", "IPython.core.magics.auto (unknown version)\n", "IPython.core.magics.basic (unknown version)\n", "IPython.core.magics.code (unknown version)\n", "IPython.core.magics.config (unknown version)\n", "IPython.core.magics.display (unknown version)\n", "IPython.core.magics.execution (unknown version)\n", "IPython.core.magics.extension (unknown version)\n", "IPython.core.magics.history (unknown version)\n", "IPython.core.magics.logging (unknown version)\n", "IPython.core.magics.namespace (unknown version)\n", "IPython.core.magics.osm (unknown version)\n", "IPython.core.magics.packaging (unknown version)\n", "IPython.core.magics.pylab (unknown version)\n", "IPython.core.magics.script (unknown version)\n", "IPython.core.oinspect (unknown version)\n", "IPython.core.page (unknown version)\n", "IPython.core.payload (unknown version)\n", "IPython.core.payloadpage (unknown version)\n", "IPython.core.prefilter (unknown version)\n", "IPython.core.profiledir (unknown version)\n", "IPython.core.pylabtools (unknown version)\n", "IPython.core.release 7.12.0\n", "IPython.core.shellapp (unknown version)\n", "IPython.core.splitinput (unknown version)\n", "IPython.core.ultratb (unknown version)\n", "IPython.core.usage (unknown version)\n", "IPython.display (unknown version)\n", "IPython.extensions (unknown version)\n", "IPython.extensions.storemagic (unknown version)\n", "IPython.lib (unknown version)\n", "IPython.lib.backgroundjobs (unknown version)\n", "IPython.lib.clipboard (unknown version)\n", "IPython.lib.display (unknown version)\n", "IPython.lib.pretty (unknown version)\n", "IPython.lib.security (unknown version)\n", "IPython.paths (unknown version)\n", "IPython.terminal (unknown version)\n", "IPython.terminal.debugger (unknown version)\n", "IPython.terminal.embed (unknown version)\n", "IPython.terminal.interactiveshell (unknown version)\n", "IPython.terminal.ipapp (unknown version)\n", "IPython.terminal.magics (unknown version)\n", "IPython.terminal.prompts (unknown version)\n", "IPython.terminal.pt_inputhooks (unknown version)\n", "IPython.terminal.ptutils (unknown version)\n", "IPython.terminal.shortcuts (unknown version)\n", "IPython.testing (unknown version)\n", "IPython.testing.skipdoctest (unknown version)\n", "IPython.utils (unknown version)\n", "IPython.utils.PyColorize (unknown version)\n", "IPython.utils._process_common (unknown version)\n", "IPython.utils._process_posix (unknown version)\n", "IPython.utils._sysinfo (unknown version)\n", "IPython.utils.capture (unknown version)\n", "IPython.utils.colorable (unknown version)\n", "IPython.utils.coloransi (unknown version)\n", "IPython.utils.contexts (unknown version)\n", "IPython.utils.data (unknown version)\n", "IPython.utils.decorators (unknown version)\n", "IPython.utils.dir2 (unknown version)\n", "IPython.utils.encoding (unknown version)\n", "IPython.utils.frame (unknown version)\n", "IPython.utils.generics (unknown version)\n", "IPython.utils.importstring (unknown version)\n", "IPython.utils.io (unknown version)\n", "IPython.utils.ipstruct (unknown version)\n", "IPython.utils.module_paths (unknown version)\n", "IPython.utils.openpy (unknown version)\n", "IPython.utils.path (unknown version)\n", "IPython.utils.process (unknown version)\n", "IPython.utils.py3compat (unknown version)\n", "IPython.utils.sentinel (unknown version)\n", "IPython.utils.strdispatch (unknown version)\n", "IPython.utils.sysinfo (unknown version)\n", "IPython.utils.syspathcontext (unknown version)\n", "IPython.utils.tempdir (unknown version)\n", "IPython.utils.terminal (unknown version)\n", "IPython.utils.text (unknown version)\n", "IPython.utils.timing (unknown version)\n", "IPython.utils.tokenutil (unknown version)\n", "IPython.utils.wildcard (unknown version)\n", "PIL 7.0.0\n", "PIL.Image 7.0.0\n", "PIL.ImageMode (unknown version)\n", "PIL.TiffTags (unknown version)\n", "PIL._binary (unknown version)\n", "PIL._imaging (unknown version)\n", "PIL._util (unknown version)\n", "PIL._version 7.0.0\n", "__future__ (unknown version)\n", "__main__ (unknown version)\n", "__main__ (unknown version)\n", "_ast (unknown version)\n", "_asyncio (unknown version)\n", "_bisect (unknown version)\n", "_blake2 (unknown version)\n", "_bootlocale (unknown version)\n", "_bz2 (unknown version)\n", "_codecs (unknown version)\n", "_collections (unknown version)\n", "collections.abc (unknown version)\n", "_compat_pickle (unknown version)\n", "_compression (unknown version)\n", "_csv 1.0\n", "_ctypes 1.1.0\n", "_curses b'2.2'\n", "_cython_0_27_2 (unknown version)\n", "_cython_0_28_5 (unknown version)\n", "_datetime (unknown version)\n", "decimal 1.70\n", "importlib._bootstrap (unknown version)\n", "importlib._bootstrap_external (unknown version)\n", "_functools (unknown version)\n", "_hashlib (unknown version)\n", "_heapq (unknown version)\n", "_imp (unknown version)\n", "io (unknown version)\n", "_json (unknown version)\n", "_locale (unknown version)\n", "_lsprof (unknown version)\n", "_lzma (unknown version)\n", "_multiprocessing (unknown version)\n", "_opcode (unknown version)\n", "_operator (unknown version)\n", "_pickle (unknown version)\n", "_posixsubprocess (unknown version)\n", "_random (unknown version)\n", "_sha3 (unknown version)\n", "_signal (unknown version)\n", "_sitebuiltins (unknown version)\n", "_socket (unknown version)\n", "_sqlite3 (unknown version)\n", "_sre (unknown version)\n", "_ssl (unknown version)\n", "_stat (unknown version)\n", "_string (unknown version)\n", "_strptime (unknown version)\n", "_struct (unknown version)\n", "_sysconfigdata_m_linux_x86_64-linux-gnu (unknown version)\n", "_thread (unknown version)\n", "_warnings (unknown version)\n", "_weakref (unknown version)\n", "_weakrefset (unknown version)\n", "abc (unknown version)\n", "argparse 1.1\n", "array (unknown version)\n", "ast (unknown version)\n", "asyncio (unknown version)\n", "asyncio.base_events (unknown version)\n", "asyncio.base_futures (unknown version)\n", "asyncio.base_subprocess (unknown version)\n", "asyncio.base_tasks (unknown version)\n", "asyncio.compat (unknown version)\n", "asyncio.constants (unknown version)\n", "asyncio.coroutines (unknown version)\n", "asyncio.events (unknown version)\n", "asyncio.futures (unknown version)\n", "asyncio.locks (unknown version)\n", "asyncio.log (unknown version)\n", "asyncio.protocols (unknown version)\n", "asyncio.queues (unknown version)\n", "asyncio.selector_events (unknown version)\n", "asyncio.sslproto (unknown version)\n", "asyncio.streams (unknown version)\n", "asyncio.subprocess (unknown version)\n", "asyncio.tasks (unknown version)\n", "asyncio.transports (unknown version)\n", "asyncio.unix_events (unknown version)\n", "atexit (unknown version)\n", "backcall 0.1.0\n", "backcall.backcall (unknown version)\n", "base64 (unknown version)\n", "bdb (unknown version)\n", "binascii (unknown version)\n", "bisect (unknown version)\n", "builtins (unknown version)\n", "bz2 (unknown version)\n", "cProfile (unknown version)\n", "calendar (unknown version)\n", "cffi 1.13.2\n", "cffi.api (unknown version)\n", "cffi.error (unknown version)\n", "cffi.lock (unknown version)\n", "cffi.model (unknown version)\n", "cmd (unknown version)\n", "code (unknown version)\n", "codecs (unknown version)\n", "codeop (unknown version)\n", "collections (unknown version)\n", "collections.abc (unknown version)\n", "colorsys (unknown version)\n", "concurrent (unknown version)\n", "concurrent.futures (unknown version)\n", "concurrent.futures._base (unknown version)\n", "concurrent.futures.process (unknown version)\n", "concurrent.futures.thread (unknown version)\n", "contextlib (unknown version)\n", "copy (unknown version)\n", "copyreg (unknown version)\n", "csv 1.0\n", "ctypes 1.1.0\n", "ctypes._endian (unknown version)\n", "ctypes.util (unknown version)\n", "curses (unknown version)\n", "cycler 0.10.0\n", "cython_runtime (unknown version)\n", "datetime (unknown version)\n", "dateutil 2.8.1\n", "dateutil._common (unknown version)\n", "dateutil._version (unknown version)\n", "dateutil.easter (unknown version)\n", "dateutil.parser (unknown version)\n", "dateutil.parser._parser (unknown version)\n", "dateutil.parser.isoparser (unknown version)\n", "dateutil.relativedelta (unknown version)\n", "dateutil.rrule (unknown version)\n", "dateutil.tz (unknown version)\n", "dateutil.tz._common (unknown version)\n", "dateutil.tz._factories (unknown version)\n", "dateutil.tz.tz (unknown version)\n", "decimal 1.70\n", "decorator 4.4.1\n", "difflib (unknown version)\n", "dis (unknown version)\n", "distutils 3.6.4\n", "distutils.debug (unknown version)\n", "distutils.dep_util (unknown version)\n", "distutils.errors (unknown version)\n", "distutils.log (unknown version)\n", "distutils.spawn (unknown version)\n", "distutils.util (unknown version)\n", "distutils.version (unknown version)\n", "email (unknown version)\n", "email._encoded_words (unknown version)\n", "email._parseaddr (unknown version)\n", "email._policybase (unknown version)\n", "email.base64mime (unknown version)\n", "email.charset (unknown version)\n", "email.encoders (unknown version)\n", "email.errors (unknown version)\n", "email.feedparser (unknown version)\n", "email.header (unknown version)\n", "email.iterators (unknown version)\n", "email.message (unknown version)\n", "email.parser (unknown version)\n", "email.quoprimime (unknown version)\n", "email.utils (unknown version)\n", "encodings (unknown version)\n", "encodings.aliases (unknown version)\n", "encodings.idna (unknown version)\n", "encodings.latin_1 (unknown version)\n", "encodings.utf_8 (unknown version)\n", "enum (unknown version)\n", "errno (unknown version)\n", "faulthandler (unknown version)\n", "fcntl (unknown version)\n", "filecmp (unknown version)\n", "fnmatch (unknown version)\n", "functools (unknown version)\n", "gc (unknown version)\n", "genericpath (unknown version)\n", "getopt (unknown version)\n", "getpass (unknown version)\n", "gettext (unknown version)\n", "glob (unknown version)\n", "google (unknown version)\n", "grp (unknown version)\n", "gzip (unknown version)\n", "hashlib (unknown version)\n", "heapq (unknown version)\n", "hmac (unknown version)\n", "html (unknown version)\n", "html.entities (unknown version)\n", "http (unknown version)\n", "http.client (unknown version)\n", "imp (unknown version)\n", "importlib (unknown version)\n", "importlib._bootstrap (unknown version)\n", "importlib._bootstrap_external (unknown version)\n", "importlib.abc (unknown version)\n", "importlib.machinery (unknown version)\n", "importlib.util (unknown version)\n", "inspect (unknown version)\n", "io (unknown version)\n", "ipaddress 1.0\n", "ipykernel 5.1.4\n", "ipykernel._version 5.1.4\n", "ipykernel.codeutil (unknown version)\n", "ipykernel.comm (unknown version)\n", "ipykernel.comm.comm (unknown version)\n", "ipykernel.comm.manager (unknown version)\n", "ipykernel.connect (unknown version)\n", "ipykernel.datapub (unknown version)\n", "ipykernel.displayhook (unknown version)\n", "ipykernel.eventloops (unknown version)\n", "ipykernel.heartbeat (unknown version)\n", "ipykernel.iostream (unknown version)\n", "ipykernel.ipkernel (unknown version)\n", "ipykernel.jsonutil (unknown version)\n", "ipykernel.kernelapp (unknown version)\n", "ipykernel.kernelbase (unknown version)\n", "ipykernel.parentpoller (unknown version)\n", "ipykernel.pickleutil (unknown version)\n", "ipykernel.pylab (unknown version)\n", "ipykernel.pylab.backend_inline (unknown version)\n", "ipykernel.pylab.config (unknown version)\n", "ipykernel.serialize (unknown version)\n", "ipykernel.zmqshell (unknown version)\n", "ipython_genutils 0.2.0\n", "ipython_genutils._version 0.2.0\n", "ipython_genutils.encoding (unknown version)\n", "ipython_genutils.importstring (unknown version)\n", "ipython_genutils.path (unknown version)\n", "ipython_genutils.py3compat (unknown version)\n", "ipython_genutils.text (unknown version)\n", "isoweek (1, 3, 3)\n", "itertools (unknown version)\n", "jedi 0.16.0\n", "jedi._compatibility (unknown version)\n", "jedi.api (unknown version)\n", "jedi.api.classes (unknown version)\n", "jedi.api.completion (unknown version)\n", "jedi.api.completion_cache (unknown version)\n", "jedi.api.environment (unknown version)\n", "jedi.api.exceptions (unknown version)\n", "jedi.api.file_name (unknown version)\n", "jedi.api.helpers (unknown version)\n", "jedi.api.interpreter (unknown version)\n", "jedi.api.keywords (unknown version)\n", "jedi.api.project (unknown version)\n", "jedi.api.strings (unknown version)\n", "jedi.cache (unknown version)\n", "jedi.common (unknown version)\n", "jedi.common.utils (unknown version)\n", "jedi.common.value (unknown version)\n", "jedi.debug (unknown version)\n", "jedi.file_io (unknown version)\n", "jedi.inference (unknown version)\n", "jedi.inference.analysis (unknown version)\n", "jedi.inference.arguments (unknown version)\n", "jedi.inference.base_value (unknown version)\n", "jedi.inference.cache (unknown version)\n", "jedi.inference.compiled (unknown version)\n", "jedi.inference.compiled.access (unknown version)\n", "jedi.inference.compiled.getattr_static (unknown version)\n", "jedi.inference.compiled.mixed (unknown version)\n", 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"prompt_toolkit.key_binding.key_bindings (unknown version)\n", "prompt_toolkit.key_binding.key_processor (unknown version)\n", "prompt_toolkit.key_binding.vi_state (unknown version)\n", "prompt_toolkit.keys (unknown version)\n", "prompt_toolkit.layout (unknown version)\n", "prompt_toolkit.layout.containers (unknown version)\n", "prompt_toolkit.layout.controls (unknown version)\n", "prompt_toolkit.layout.dimension (unknown version)\n", "prompt_toolkit.layout.dummy (unknown version)\n", "prompt_toolkit.layout.layout (unknown version)\n", "prompt_toolkit.layout.margins (unknown version)\n", "prompt_toolkit.layout.menus (unknown version)\n", "prompt_toolkit.layout.mouse_handlers (unknown version)\n", "prompt_toolkit.layout.processors (unknown version)\n", "prompt_toolkit.layout.screen (unknown version)\n", "prompt_toolkit.layout.utils (unknown version)\n", "prompt_toolkit.lexers (unknown version)\n", "prompt_toolkit.lexers.base (unknown version)\n", "prompt_toolkit.lexers.pygments (unknown version)\n", "prompt_toolkit.mouse_events (unknown version)\n", "prompt_toolkit.output (unknown version)\n", "prompt_toolkit.output.base (unknown version)\n", "prompt_toolkit.output.color_depth (unknown version)\n", "prompt_toolkit.output.defaults (unknown version)\n", "prompt_toolkit.output.vt100 (unknown version)\n", "prompt_toolkit.patch_stdout (unknown version)\n", "prompt_toolkit.renderer (unknown version)\n", "prompt_toolkit.search (unknown version)\n", "prompt_toolkit.selection (unknown version)\n", "prompt_toolkit.shortcuts (unknown version)\n", "prompt_toolkit.shortcuts.dialogs (unknown version)\n", "prompt_toolkit.shortcuts.progress_bar (unknown version)\n", "prompt_toolkit.shortcuts.progress_bar.base (unknown version)\n", "prompt_toolkit.shortcuts.progress_bar.formatters (unknown version)\n", "prompt_toolkit.shortcuts.prompt (unknown version)\n", "prompt_toolkit.shortcuts.utils (unknown version)\n", "prompt_toolkit.styles (unknown version)\n", "prompt_toolkit.styles.base (unknown version)\n", "prompt_toolkit.styles.defaults (unknown version)\n", "prompt_toolkit.styles.named_colors (unknown version)\n", "prompt_toolkit.styles.pygments (unknown version)\n", "prompt_toolkit.styles.style (unknown version)\n", "prompt_toolkit.styles.style_transformation (unknown version)\n", "prompt_toolkit.utils (unknown version)\n", "prompt_toolkit.validation (unknown version)\n", "prompt_toolkit.widgets (unknown version)\n", "prompt_toolkit.widgets.base (unknown version)\n", "prompt_toolkit.widgets.dialogs (unknown version)\n", "prompt_toolkit.widgets.menus (unknown version)\n", "prompt_toolkit.widgets.toolbars (unknown version)\n", "pstats (unknown version)\n", "pty (unknown version)\n", "ptyprocess 0.6.0\n", "ptyprocess.ptyprocess (unknown version)\n", "ptyprocess.util (unknown version)\n", "pwd (unknown version)\n", "pydoc (unknown version)\n", "pydoc_data (unknown version)\n", "pydoc_data.topics (unknown version)\n", "pygments 2.5.2\n", "pygments.filter (unknown version)\n", "pygments.filters (unknown version)\n", "pygments.formatter (unknown version)\n", "pygments.formatters (unknown version)\n", "pygments.formatters._mapping (unknown version)\n", "pygments.formatters.html (unknown version)\n", "pygments.lexer (unknown version)\n", "pygments.lexers (unknown version)\n", "pygments.lexers._mapping (unknown version)\n", "pygments.lexers.python (unknown version)\n", "pygments.modeline (unknown version)\n", "pygments.plugin (unknown version)\n", "pygments.regexopt (unknown version)\n", "pygments.style (unknown version)\n", "pygments.styles (unknown version)\n", "pygments.token (unknown version)\n", "pygments.unistring (unknown version)\n", "pygments.util (unknown version)\n", "pyparsing 2.4.6\n", "pytz 2019.3\n", "pytz.exceptions (unknown version)\n", "pytz.lazy (unknown version)\n", "pytz.tzfile (unknown version)\n", "pytz.tzinfo (unknown version)\n", "queue (unknown version)\n", "quopri (unknown version)\n", "random (unknown version)\n", "re 2.2.1\n", "reprlib (unknown version)\n", "resource (unknown version)\n", "ruamel (unknown version)\n", "runpy (unknown version)\n", "select (unknown version)\n", "selectors (unknown version)\n", "shlex (unknown version)\n", "shutil (unknown version)\n", "signal (unknown version)\n", "site (unknown version)\n", "six 1.14.0\n", "six.moves (unknown version)\n", "six.moves.urllib (unknown version)\n", "six.moves.urllib_parse (unknown version)\n", "six.moves.urllib.request (unknown version)\n", "socket (unknown version)\n", "sqlite3 (unknown version)\n", "sqlite3.dbapi2 (unknown version)\n", "sre_compile (unknown version)\n", "sre_constants (unknown version)\n", "sre_parse (unknown version)\n", "ssl (unknown version)\n", "stat (unknown version)\n", "storemagic (unknown version)\n", "string (unknown version)\n", "stringprep (unknown version)\n", "struct (unknown version)\n", "subprocess (unknown version)\n", "sys (unknown version)\n", "sysconfig (unknown version)\n", "tempfile (unknown version)\n", "termios (unknown version)\n", "textwrap (unknown version)\n", "threading (unknown version)\n", "time (unknown version)\n", "timeit (unknown version)\n", "token (unknown version)\n", "tokenize (unknown version)\n", "tornado (unknown version)\n", "tornado.concurrent (unknown version)\n", "tornado.escape (unknown version)\n", "tornado.gen (unknown version)\n", "tornado.ioloop (unknown version)\n", "tornado.locks (unknown version)\n", "tornado.log (unknown version)\n", "tornado.platform (unknown version)\n", "tornado.platform.asyncio (unknown version)\n", "tornado.queues (unknown version)\n", "tornado.speedups (unknown version)\n", "tornado.util (unknown version)\n", "traceback (unknown version)\n", "traitlets 4.3.3\n", "traitlets._version 4.3.3\n", "traitlets.config (unknown version)\n", "traitlets.config.application (unknown version)\n", "traitlets.config.configurable (unknown version)\n", "traitlets.config.loader (unknown version)\n", "traitlets.log (unknown version)\n", "traitlets.traitlets (unknown version)\n", "traitlets.utils (unknown version)\n", "traitlets.utils.bunch (unknown version)\n", "traitlets.utils.getargspec (unknown version)\n", "traitlets.utils.importstring (unknown version)\n", "traitlets.utils.sentinel (unknown version)\n", "tty (unknown version)\n", "types (unknown version)\n", "typing (unknown version)\n", "typing.io (unknown version)\n", "typing.re (unknown version)\n", "unicodedata (unknown version)\n", "unittest (unknown version)\n", "unittest.case (unknown version)\n", "unittest.loader (unknown version)\n", "unittest.main (unknown version)\n", "unittest.result (unknown version)\n", "unittest.runner (unknown version)\n", "unittest.signals (unknown version)\n", "unittest.suite (unknown version)\n", "unittest.util (unknown version)\n", "urllib (unknown version)\n", "urllib.error (unknown version)\n", "urllib.parse (unknown version)\n", "urllib.request 3.6\n", "urllib.response (unknown version)\n", "uu (unknown version)\n", "uuid (unknown version)\n", "warnings (unknown version)\n", "wcwidth (unknown version)\n", "wcwidth.table_wide (unknown version)\n", "wcwidth.table_zero (unknown version)\n", "wcwidth.wcwidth (unknown version)\n", "weakref (unknown version)\n", "xml (unknown version)\n", "xml.dom (unknown version)\n", "xml.dom.NodeFilter (unknown version)\n", "xml.dom.domreg (unknown version)\n", "xml.dom.minicompat (unknown version)\n", "xml.dom.minidom (unknown version)\n", "xml.dom.xmlbuilder (unknown version)\n", "zipimport (unknown version)\n", "zlib 1.0\n", "zmq 17.1.2\n", "zmq._future (unknown version)\n", "zmq.asyncio (unknown version)\n", "zmq.backend (unknown version)\n", "zmq.backend.cython (unknown version)\n", "zmq.backend.cython._device (unknown version)\n", "zmq.backend.cython._poll (unknown version)\n", "zmq.backend.cython._version (unknown version)\n", "zmq.backend.cython.constants (unknown version)\n", "zmq.backend.cython.context (unknown version)\n", "zmq.backend.cython.error (unknown version)\n", "zmq.backend.cython.message (unknown version)\n", "zmq.backend.cython.socket (unknown version)\n", "zmq.backend.cython.utils (unknown version)\n", "zmq.backend.select (unknown version)\n", "zmq.error (unknown version)\n", "zmq.eventloop (unknown version)\n", "zmq.eventloop.ioloop (unknown version)\n", "zmq.eventloop.minitornado (unknown version)\n", "zmq.eventloop.minitornado.stack_context (unknown version)\n", "zmq.eventloop.minitornado.util (unknown version)\n", "zmq.eventloop.zmqstream (unknown version)\n", "zmq.sugar 17.1.2\n", "zmq.sugar.attrsettr (unknown version)\n", "zmq.sugar.constants (unknown version)\n", "zmq.sugar.context (unknown version)\n", "zmq.sugar.frame (unknown version)\n", "zmq.sugar.poll (unknown version)\n", "zmq.sugar.socket (unknown version)\n", "zmq.sugar.stopwatch (unknown version)\n", "zmq.sugar.tracker (unknown version)\n", "zmq.sugar.version 17.1.2\n", "zmq.utils (unknown version)\n", "zmq.utils.constant_names (unknown version)\n", "zmq.utils.jsonapi (unknown version)\n", "zmq.utils.strtypes (unknown version)\n" ] } ], "source": [ "def print_imported_modules():\n", " import sys\n", " for name, val in sorted(sys.modules.items()):\n", " if(hasattr(val, '__version__')): \n", " print(val.__name__, val.__version__)\n", " else:\n", " print(val.__name__, \"(unknown version)\")\n", "\n", "print_imported_modules()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Début de l'analyse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chargement du fichier" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas l'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_file = \"data_varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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.................................
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1687 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202313 7 12675 8518 16832 19 13 \n", "1 202312 7 10252 7063 13441 15 10 \n", "2 202311 7 4919 2880 6958 7 4 \n", "3 202310 7 4854 2731 6977 7 4 \n", "4 202309 7 7004 4548 9460 11 7 \n", "5 202308 7 8175 5316 11034 12 8 \n", "6 202307 7 6595 3782 9408 10 6 \n", "7 202306 7 9595 6017 13173 14 9 \n", "8 202305 7 6237 3907 8567 9 5 \n", "9 202304 7 6299 3973 8625 9 6 \n", "10 202303 7 6063 3798 8328 9 6 \n", "11 202302 7 6576 3060 10092 10 5 \n", "12 202301 7 8153 5470 10836 12 8 \n", "13 202252 7 5171 2717 7625 8 4 \n", "14 202251 7 6226 3822 8630 9 5 \n", "15 202250 7 6590 3100 10080 10 5 \n", "16 202249 7 5095 3212 6978 8 5 \n", "17 202248 7 4985 3043 6927 8 5 \n", "18 202247 7 6087 3733 8441 9 5 \n", "19 202246 7 3033 1392 4674 5 3 \n", "20 202245 7 3827 1720 5934 6 3 \n", "21 202244 7 4271 2231 6311 6 3 \n", "22 202243 7 5863 3302 8424 9 5 \n", "23 202242 7 3770 1950 5590 6 3 \n", "24 202241 7 4177 2219 6135 6 3 \n", "25 202240 7 4883 1472 8294 7 2 \n", "26 202239 7 2041 331 3751 3 0 \n", "27 202238 7 1771 419 3123 3 1 \n", "28 202237 7 1725 499 2951 3 1 \n", "29 202236 7 1069 178 1960 2 1 \n", "... ... ... ... ... ... ... ... \n", "1657 199126 7 17608 11304 23912 31 20 \n", "1658 199125 7 16169 10700 21638 28 18 \n", "1659 199124 7 16171 10071 22271 28 17 \n", "1660 199123 7 11947 7671 16223 21 13 \n", "1661 199122 7 15452 9953 20951 27 17 \n", "1662 199121 7 14903 8975 20831 26 16 \n", "1663 199120 7 19053 12742 25364 34 23 \n", "1664 199119 7 16739 11246 22232 29 19 \n", "1665 199118 7 21385 13882 28888 38 25 \n", "1666 199117 7 13462 8877 18047 24 16 \n", "1667 199116 7 14857 10068 19646 26 18 \n", "1668 199115 7 13975 9781 18169 25 18 \n", "1669 199114 7 12265 7684 16846 22 14 \n", "1670 199113 7 9567 6041 13093 17 11 \n", "1671 199112 7 10864 7331 14397 19 13 \n", "1672 199111 7 15574 11184 19964 27 19 \n", "1673 199110 7 16643 11372 21914 29 20 \n", "1674 199109 7 13741 8780 18702 24 15 \n", "1675 199108 7 13289 8813 17765 23 15 \n", "1676 199107 7 12337 8077 16597 22 15 \n", "1677 199106 7 10877 7013 14741 19 12 \n", "1678 199105 7 10442 6544 14340 18 11 \n", "1679 199104 7 7913 4563 11263 14 8 \n", "1680 199103 7 15387 10484 20290 27 18 \n", "1681 199102 7 16277 11046 21508 29 20 \n", "1682 199101 7 15565 10271 20859 27 18 \n", "1683 199052 7 19375 13295 25455 34 23 \n", "1684 199051 7 19080 13807 24353 34 25 \n", "1685 199050 7 11079 6660 15498 20 12 \n", "1686 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 25 FR France \n", "1 20 FR France \n", "2 10 FR France \n", "3 10 FR France \n", "4 15 FR France \n", "5 16 FR France \n", "6 14 FR France \n", "7 19 FR France \n", "8 13 FR France \n", "9 12 FR France \n", "10 12 FR France \n", "11 15 FR France \n", "12 16 FR France \n", "13 12 FR France \n", "14 13 FR France \n", "15 15 FR France \n", "16 11 FR France \n", "17 11 FR France \n", "18 13 FR France \n", "19 7 FR France \n", "20 9 FR France \n", "21 9 FR France \n", "22 13 FR France \n", "23 9 FR France \n", "24 9 FR France \n", "25 12 FR France \n", "26 6 FR France \n", "27 5 FR France \n", "28 5 FR France \n", "29 3 FR France \n", "... ... ... ... \n", "1657 42 FR France \n", "1658 38 FR France \n", "1659 39 FR France \n", "1660 29 FR France \n", "1661 37 FR France \n", "1662 36 FR France \n", "1663 45 FR France \n", "1664 39 FR France \n", "1665 51 FR France \n", "1666 32 FR France \n", "1667 34 FR France \n", "1668 32 FR France \n", "1669 30 FR France \n", "1670 23 FR France \n", "1671 25 FR France \n", "1672 35 FR France \n", "1673 38 FR France \n", "1674 33 FR France \n", "1675 31 FR France \n", "1676 29 FR France \n", "1677 26 FR France \n", "1678 25 FR France \n", "1679 20 FR France \n", "1680 36 FR France \n", "1681 38 FR France \n", "1682 36 FR France \n", "1683 45 FR France \n", "1684 43 FR France \n", "1685 28 FR France \n", "1686 5 FR France \n", "\n", "[1687 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Examen des données" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202313712675851816832191325FRFrance
1202312710252706313441151020FRFrance
220231174919288069587410FRFrance
320231074854273169777410FRFrance
4202309770044548946011715FRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202313 7 12675 8518 16832 19 13 25 \n", "1 202312 7 10252 7063 13441 15 10 20 \n", "2 202311 7 4919 2880 6958 7 4 10 \n", "3 202310 7 4854 2731 6977 7 4 10 \n", "4 202309 7 7004 4548 9460 11 7 15 \n", "\n", " geo_insee geo_name \n", "0 FR France \n", "1 FR France \n", "2 FR France \n", "3 FR France \n", "4 FR France " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
12191999467654401507411026FRFrance
707200937719893623616306FRFrance
152022507659031001008010515FRFrance
12072000067901753911264315921FRFrance
5472012407917553271302314820FRFrance
16821991017155651027120859271836FRFrance
1212200001710986652115451191127FRFrance
15931992387284213064378528FRFrance
16631991207190531274225364342345FRFrance
147202023755811115102FRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1219 199946 7 6544 0 15074 11 0 \n", "707 200937 7 1989 362 3616 3 0 \n", "15 202250 7 6590 3100 10080 10 5 \n", "1207 200006 7 9017 5391 12643 15 9 \n", "547 201240 7 9175 5327 13023 14 8 \n", "1682 199101 7 15565 10271 20859 27 18 \n", "1212 200001 7 10986 6521 15451 19 11 \n", "1593 199238 7 2842 1306 4378 5 2 \n", "1663 199120 7 19053 12742 25364 34 23 \n", "147 202023 7 558 1 1115 1 0 \n", "\n", " inc100_up geo_insee geo_name \n", "1219 26 FR France \n", "707 6 FR France \n", "15 15 FR France \n", "1207 21 FR France \n", "547 20 FR France \n", "1682 36 FR France \n", "1212 27 FR France \n", "1593 8 FR France \n", "1663 45 FR France \n", "147 2 FR France " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.sample(10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1687 entries, 0 to 1686\n", "Data columns (total 10 columns):\n", "week 1687 non-null int64\n", "indicator 1687 non-null int64\n", "inc 1687 non-null int64\n", "inc_low 1687 non-null int64\n", "inc_up 1687 non-null int64\n", "inc100 1687 non-null int64\n", "inc100_low 1687 non-null int64\n", "inc100_up 1687 non-null int64\n", "geo_insee 1687 non-null object\n", "geo_name 1687 non-null object\n", "dtypes: int64(8), object(2)\n", "memory usage: 131.9+ KB\n" ] } ], "source": [ "raw_data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "Résultat de l'examen des données : \n", "- pas de donnée abérante\n", "- rien de remarquable \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202313712675851816832191325FRFrance
1202312710252706313441151020FRFrance
220231174919288069587410FRFrance
320231074854273169777410FRFrance
4202309770044548946011715FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202313 7 12675 8518 16832 19 13 25 \n", "1 202312 7 10252 7063 13441 15 10 20 \n", "2 202311 7 4919 2880 6958 7 4 10 \n", "3 202310 7 4854 2731 6977 7 4 10 \n", "4 202309 7 7004 4548 9460 11 7 15 \n", "\n", " geo_insee geo_name \n", "0 FR France \n", "1 FR France \n", "2 FR France \n", "3 FR France \n", "4 FR France " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.copy()\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Traitement des dates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans le sens chronologique." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Pas de problème relevé" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualtisations\n", "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre \n", "- un minimum au moment du mois de septembre (rentrée scolaire ?)\n", "- l'impact des confinements COVID en 2020." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-60:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le minimum de l'épidémie se situe en septembre, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1],\n", " first_sept_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2022 641397\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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