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Lamia-Mihoubi/PY0101EN-4.3_notebook_quizz_pandas.ipynb

Created September 18, 2019 17:09
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PY0101EN-4.3_notebook_quizz_pandas.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Get to Know a numpy Array </h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will use the dataframe <code>df</code> for the following:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>City</th>\n",
" <th>Sport</th>\n",
" <th>Discipline</th>\n",
" <th>NOC</th>\n",
" <th>Event</th>\n",
" <th>Event gender</th>\n",
" <th>Medal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1924</td>\n",
" <td>Chamonix</td>\n",
" <td>Skating</td>\n",
" <td>Figure skating</td>\n",
" <td>AUT</td>\n",
" <td>individual</td>\n",
" <td>M</td>\n",
" <td>Silver</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1924</td>\n",
" <td>Chamonix</td>\n",
" <td>Skating</td>\n",
" <td>Figure skating</td>\n",
" <td>AUT</td>\n",
" <td>individual</td>\n",
" <td>W</td>\n",
" <td>Gold</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1924</td>\n",
" <td>Chamonix</td>\n",
" <td>Skating</td>\n",
" <td>Figure skating</td>\n",
" <td>AUT</td>\n",
" <td>pairs</td>\n",
" <td>X</td>\n",
" <td>Gold</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1924</td>\n",
" <td>Chamonix</td>\n",
" <td>Bobsleigh</td>\n",
" <td>Bobsleigh</td>\n",
" <td>BEL</td>\n",
" <td>four-man</td>\n",
" <td>M</td>\n",
" <td>Bronze</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1924</td>\n",
" <td>Chamonix</td>\n",
" <td>Ice Hockey</td>\n",
" <td>Ice Hockey</td>\n",
" <td>CAN</td>\n",
" <td>ice hockey</td>\n",
" <td>M</td>\n",
" <td>Gold</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2309</td>\n",
" <td>2006</td>\n",
" <td>Turin</td>\n",
" <td>Skiing</td>\n",
" <td>Snowboard</td>\n",
" <td>USA</td>\n",
" <td>Snowboard Cross</td>\n",
" <td>M</td>\n",
" <td>Gold</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2310</td>\n",
" <td>2006</td>\n",
" <td>Turin</td>\n",
" <td>Skiing</td>\n",
" <td>Snowboard</td>\n",
" <td>USA</td>\n",
" <td>Snowboard Cross</td>\n",
" <td>W</td>\n",
" <td>Silver</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2311</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2312</td>\n",
" <td>SOURCE</td>\n",
" <td>IOC</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2313</td>\n",
" <td>DATALINK</td>\n",
" <td>http://www.olympic.org/</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2314 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Year City Sport Discipline NOC \\\n",
"0 1924 Chamonix Skating Figure skating AUT \n",
"1 1924 Chamonix Skating Figure skating AUT \n",
"2 1924 Chamonix Skating Figure skating AUT \n",
"3 1924 Chamonix Bobsleigh Bobsleigh BEL \n",
"4 1924 Chamonix Ice Hockey Ice Hockey CAN \n",
"... ... ... ... ... ... \n",
"2309 2006 Turin Skiing Snowboard USA \n",
"2310 2006 Turin Skiing Snowboard USA \n",
"2311 NaN NaN NaN NaN NaN \n",
"2312 SOURCE IOC NaN NaN NaN \n",
"2313 DATALINK http://www.olympic.org/ NaN NaN NaN \n",
"\n",
" Event Event gender Medal \n",
"0 individual M Silver \n",
"1 individual W Gold \n",
"2 pairs X Gold \n",
"3 four-man M Bronze \n",
"4 ice hockey M Gold \n",
"... ... ... ... \n",
"2309 Snowboard Cross M Gold \n",
"2310 Snowboard Cross W Silver \n",
"2311 NaN NaN NaN \n",
"2312 NaN NaN NaN \n",
"2313 NaN NaN NaN \n",
"\n",
"[2314 rows x 8 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"#df=pd.DataFrame({'a':[11,21,31],'b':[21,22,23]})\n",
"myDf=pd.read_csv(\"/resources/data/samples/olympic-medals/medals.csv\")\n",
"myDf\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) plot the first three rows:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Year 2313\n",
"City 2313\n",
"Sport 2311\n",
"Discipline 2311\n",
"NOC 2311\n",
"Event 2311\n",
"Event gender 2311\n",
"Medal 2311\n",
"dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myDf.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) obtain column <code> 'a' </code>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 11\n",
"1 21\n",
"2 31\n",
"Name: a, dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['a']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>\n",
"<small>Copyright &copy; 2018 IBM Cognitive Class. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).</small>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-6e1c88e76d1d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmyDf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[0m",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object is not callable"
]
}
],
"source": [
"myDf.shape()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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