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module2/exo4/exercice.ipynb
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...@@ -2,40 +2,77 @@ ...@@ -2,40 +2,77 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"df = pd.read_csv(\"./arry.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "FileNotFoundError", "name": "stdout",
"evalue": "File b'./arry.csv' does not exist", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "14.114141414141415 4.356135951594896 2.8 14.6 23.4\n"
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-625870c426dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./arry.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 816\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 818\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1047\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1048\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1050\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1051\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1693\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1697\u001b[0m \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: File b'./arry.csv' does not exist"
] ]
} }
], ],
"source": [ "source": [
"import pandas as pd\n", "print(np.mean(df.iloc[:,1]),\n",
"\n", " np.std(df.iloc[:,1],ddof=1),\n",
"df = pd.read_csv(\"./arry.csv\")" " np.min(df.iloc[:,1]),\n",
" np.median(df.iloc[:,1]),\n",
" np.max(df.iloc[:,1]))"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"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": [
"import matplotlib.pyplot as plt\n",
"plt.figure()\n",
"plt.plot(df.iloc[:,1],color = 'b')\n",
"plt.grid()\n",
"\n",
"plt.figure()\n",
"plt.hist(df.iloc[:,1])\n",
"plt.grid()\n",
"plt.show()"
]
} }
], ],
"metadata": { "metadata": {
......
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