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Commit 61c17ad1 authored by 8645139112c21501d9914ade8e4a034f's avatar 8645139112c21501d9914ade8e4a034f
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module3/exo3/exercice.ipynb
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...@@ -3,8 +3,8 @@ ...@@ -3,8 +3,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"source": [ "source": [
"# Travail pratique avec évaluation par les pairs\n", "# Travail pratique avec évaluation par les pairs\n",
...@@ -37,8 +37,8 @@ ...@@ -37,8 +37,8 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -51,8 +51,8 @@ ...@@ -51,8 +51,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"source": [ "source": [
"Après inspection visuelle, les premières lignes du fichier CSV sont un commentaire, que nous ignorons en précisant **skiprows=43**.\n", "Après inspection visuelle, les premières lignes du fichier CSV sont un commentaire, que nous ignorons en précisant **skiprows=43**.\n",
...@@ -65,10 +65,10 @@ ...@@ -65,10 +65,10 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 2,
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"outputs": [ "outputs": [
{ {
...@@ -474,7 +474,7 @@ ...@@ -474,7 +474,7 @@
"[3156 rows x 2 columns]" "[3156 rows x 2 columns]"
] ]
}, },
"execution_count": 32, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -490,19 +490,19 @@ ...@@ -490,19 +490,19 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"source": [ "source": [
"Y a-t-il des points manquants dans ce jeux de données ?" "Y a-t-il des points manquants dans ce jeux de données ? Après une inspection visuelle on vérifie avec la ligne suivante."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {
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"hidePrompt": true "hidePrompt": false
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"outputs": [ "outputs": [
{ {
...@@ -537,7 +537,7 @@ ...@@ -537,7 +537,7 @@
], ],
"text/plain": [ "text/plain": [
"Empty DataFrame\n", "Empty DataFrame\n",
"Columns: [Date, Concentration]\n", "Columns: [Date, Concentration]\n",
"Index: []" "Index: []"
] ]
}, },
...@@ -553,8 +553,8 @@ ...@@ -553,8 +553,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"source": [ "source": [
"Pas de données manquante, on continue l'analyse.\n", "Pas de données manquante, on continue l'analyse.\n",
...@@ -564,8 +564,11 @@ ...@@ -564,8 +564,11 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 4,
"metadata": {}, "metadata": {
"hideCode": false,
"hidePrompt": false
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"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
...@@ -582,27 +585,217 @@ ...@@ -582,27 +585,217 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hidePrompt": true "hidePrompt": false
}, },
"source": [ "source": [
"La concentration est donc bien un nombre on doit pas changer. Les dates sont des chaînes de caractère, on odit odnc traiter pour avoir des nombres." "La concentration est donc bien un nombre on doit pas changer. Les dates sont des chaînes de caractère, on doit donc traiter pour avoir des nombres."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2020-02-01'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[\"Date\"][3155]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "time data '1958-03-29' does not match format '%y/%m/%d'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-32-88c935b5635e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdatetime_object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-32-88c935b5635e>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdatetime_object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-32-88c935b5635e>\u001b[0m in \u001b[0;36mconvert_week\u001b[0;34m(year_and_week_str)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear_and_week_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdatetime_object\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear_and_week_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%y/%m/%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdatetime_object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/_strptime.py\u001b[0m in \u001b[0;36m_strptime_datetime\u001b[0;34m(cls, data_string, format)\u001b[0m\n\u001b[1;32m 563\u001b[0m \"\"\"Return a class cls instance based on the input string and the\n\u001b[1;32m 564\u001b[0m format string.\"\"\"\n\u001b[0;32m--> 565\u001b[0;31m \u001b[0mtt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfraction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_strptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 566\u001b[0m \u001b[0mtzname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgmtoff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfraction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/_strptime.py\u001b[0m in \u001b[0;36m_strptime\u001b[0;34m(data_string, format)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 361\u001b[0m raise ValueError(\"time data %r does not match format %r\" %\n\u001b[0;32m--> 362\u001b[0;31m (data_string, format))\n\u001b[0m\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_string\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise ValueError(\"unconverted data remains: %s\" %\n",
"\u001b[0;31mValueError\u001b[0m: time data '1958-03-29' does not match format '%y/%m/%d'"
]
}
],
"source": [
"from datetime import datetime\n",
"\n",
"def convert_week(year_and_week_str):\n",
" datetime_object = datetime.strptime(year_and_week_str, '%y/%m/%d')\n",
" return datetime_object\n",
"\n",
"raw_data['period'] = [convert_week(yw) for yw in raw_data['Date']]\n",
"print(raw_data)\n",
"\n",
"#def convert_week(year_and_week_int):\n",
" #year_and_week_str = str(year_and_week_int)\n",
" # datestr=year_and_week_int[:4]+year_and_week_int[5:7]+year_and_week_int[8:]\n",
" # dateint=int(datestr)\n",
" # return dateint\n",
" #year = int(year_and_week_int[:4])\n",
" #week = int(year_and_week_int[5:7])\n",
" #w = isoweek.Week(year, week)\n",
" #return pd.Period(w.day(0), 'W')\n",
"\n",
"#raw_data['period'] = [convert_week(yw) for yw in raw_data['Date']]\n",
"#print(raw_data)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fed124f9550>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raw_data[-100:].plot(\"period\",\"Concentration\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
" sorted_data = raw_data.set_index('period').sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": { "metadata": {
"hideCode": true, "hideCode": false,
"hideOutput": true, "hidePrompt": false
"hidePrompt": true
}, },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1958-01-27/1958-02-02 1958-02-10/1958-02-16\n",
"1958-02-24/1958-03-02 1958-03-10/1958-03-16\n",
"1958-03-17/1958-03-23 1958-12-29/1959-01-04\n",
"1959-03-16/1959-03-22 1960-01-04/1960-01-10\n",
"1960-03-21/1960-03-27 1961-01-02/1961-01-08\n",
"1961-03-20/1961-03-26 1962-01-01/1962-01-07\n",
"1962-03-19/1962-03-25 1962-12-31/1963-01-06\n",
"1963-03-18/1963-03-24 1963-12-30/1964-01-05\n",
"1963-12-30/1964-01-05 1964-01-27/1964-02-02\n",
"1964-03-16/1964-03-22 1965-01-04/1965-01-10\n",
"1965-03-22/1965-03-28 1966-01-03/1966-01-09\n",
"1966-03-21/1966-03-27 1967-01-02/1967-01-08\n",
"1967-03-20/1967-03-26 1968-01-01/1968-01-07\n",
"1968-03-18/1968-03-24 1968-12-30/1969-01-05\n",
"1969-03-17/1969-03-23 1969-12-29/1970-01-04\n",
"1970-03-16/1970-03-22 1971-01-04/1971-01-10\n",
"1971-03-22/1971-03-28 1972-01-03/1972-01-09\n",
"1972-03-20/1972-03-26 1973-01-01/1973-01-07\n",
"1973-03-19/1973-03-25 1973-12-31/1974-01-06\n",
"1974-03-18/1974-03-24 1974-12-30/1975-01-05\n",
"1975-03-17/1975-03-23 1975-12-29/1976-01-04\n",
"1976-03-15/1976-03-21 1977-01-03/1977-01-09\n",
"1977-03-21/1977-03-27 1978-01-02/1978-01-08\n",
"1978-03-20/1978-03-26 1979-01-01/1979-01-07\n",
"1979-03-19/1979-03-25 1979-12-31/1980-01-06\n",
"1980-03-17/1980-03-23 1980-12-29/1981-01-04\n",
"1981-03-16/1981-03-22 1982-01-04/1982-01-10\n",
"1982-03-22/1982-03-28 1983-01-03/1983-01-09\n",
"1983-03-21/1983-03-27 1984-01-02/1984-01-08\n",
"1984-03-19/1984-03-25 1984-12-31/1985-01-06\n",
"1985-03-18/1985-03-24 1985-12-30/1986-01-05\n",
"1986-03-17/1986-03-23 1986-12-29/1987-01-04\n",
"1987-03-16/1987-03-22 1988-01-04/1988-01-10\n",
"1988-03-21/1988-03-27 1989-01-02/1989-01-08\n",
"1989-03-20/1989-03-26 1990-01-01/1990-01-07\n",
"1990-03-19/1990-03-25 1990-12-31/1991-01-06\n",
"1991-03-18/1991-03-24 1991-12-30/1992-01-05\n",
"1992-03-16/1992-03-22 1993-01-04/1993-01-10\n",
"1993-03-22/1993-03-28 1994-01-03/1994-01-09\n",
"1994-03-21/1994-03-27 1995-01-02/1995-01-08\n",
"1995-03-20/1995-03-26 1996-01-01/1996-01-07\n",
"1996-03-18/1996-03-24 1996-12-30/1997-01-05\n",
"1997-03-17/1997-03-23 1997-12-29/1998-01-04\n",
"1998-03-16/1998-03-22 1999-01-04/1999-01-10\n",
"1999-03-22/1999-03-28 2000-01-03/2000-01-09\n",
"2000-03-20/2000-03-26 2001-01-01/2001-01-07\n",
"2001-03-19/2001-03-25 2001-12-31/2002-01-06\n",
"2002-03-18/2002-03-24 2002-12-30/2003-01-05\n",
"2003-03-17/2003-03-23 2003-12-29/2004-01-04\n",
"2004-03-15/2004-03-21 2005-01-03/2005-01-09\n",
"2005-03-21/2005-03-27 2006-01-02/2006-01-08\n",
"2006-03-20/2006-03-26 2007-01-01/2007-01-07\n",
"2007-03-19/2007-03-25 2007-12-31/2008-01-06\n",
"2008-03-17/2008-03-23 2008-12-29/2009-01-04\n",
"2009-03-16/2009-03-22 2010-01-04/2010-01-10\n",
"2010-03-22/2010-03-28 2011-01-03/2011-01-09\n",
"2011-03-21/2011-03-27 2012-01-02/2012-01-08\n",
"2012-03-19/2012-03-25 2012-12-31/2013-01-06\n",
"2013-03-18/2013-03-24 2013-12-30/2014-01-05\n",
"2014-03-17/2014-03-23 2014-12-29/2015-01-04\n",
"2015-03-16/2015-03-22 2016-01-04/2016-01-10\n",
"2016-03-21/2016-03-27 2017-01-02/2017-01-08\n",
"2017-03-20/2017-03-26 2018-01-01/2018-01-07\n",
"2018-03-19/2018-03-25 2018-12-31/2019-01-06\n",
"2019-03-18/2019-03-24 2019-12-30/2020-01-05\n"
]
}
],
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
} }
], ],
"metadata": { "metadata": {
"hide_code_all_hidden": true, "hide_code_all_hidden": false,
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
"language": "python", "language": "python",
......
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