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sitc/ml2/3_2_Pandas.ipynb
Carlos A. Iglesias 7577dfa754 Fix error while importing numpy
2016-04-05 15:33:47 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
"\n",
"* [Introduction to Pandas](#Introduction-to-Pandas)\n",
"* [Series](#Series)\n",
"* [DataFrame](#DataFrame)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Pandas\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook provides an overview of the *pandas* library. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Pandas](http://pandas.pydata.org/) is a Python library that provides easy-to-use data structures and data analysis tools.\n",
"\n",
"The main advantage of *Pandas* is that provides extensive facilities for grouping, merging and querying pandas data structures, and also includes facilities for time series analysis, as well as i/o and visualisation facilities.\n",
"\n",
"Pandas in built on top of *NumPy*, so we will have usually to import both libraries.\n",
"\n",
"Pandas provides two main data structures:\n",
"* **Series** is a one dimensional labelled object, capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.).. It is similar to an array, a list, a dictionary or a column in a table. Every value in a Series object has an index.\n",
"* **DataFrame** is a two dimensional labelled object with columns of potentially different types. It is similar to a database table, or a spreadsheet. It can be seen as a dictionary of Series that share the same index.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are not going to use Series objects directly as frequently as DataFrames. Here we provide a short introduction"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 5\n",
"1 10\n",
"2 15\n",
"dtype: int64"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
"\n",
"# create series object from an array\n",
"s = Series([5, 10, 15])\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see each value has an associated label starting with 0 if no index is specified when the Series object is created. \n",
"\n",
"It is similar to a dictionary. In fact, we can also create a Series object from a dictionary as follows. In this case, the indexes are the keys of the dictionary."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 5\n",
"b 10\n",
"c 15\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = {'a': 5, 'b': 10, 'c': 15}\n",
"s = Series(d)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['a', 'b', 'c'], dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can get the list of indexes\n",
"s.index"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5, 10, 15])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# and the values\n",
"s.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another option is to create the Series object from two lists, for values and indexes."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3141991\n",
"Barcelona 1604555\n",
"Valencia 786189\n",
"Sevilla 693878\n",
"Zaragoza 664953\n",
"Malaga 569130\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Series with population in 2015 of more populated cities in Spain\n",
"s = Series([3141991, 1604555, 786189, 693878, 664953, 569130], index=['Madrid', 'Barcelona', 'Valencia', 'Sevilla', \n",
" 'Zaragoza', 'Malaga'])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3141991"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Population of Madrid\n",
"s['Madrid']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Indexing and slicing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Until now, we have not seen any advantage in using Panda Series. we are going to show now some examples of their possibilities."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid True\n",
"Barcelona True\n",
"Valencia False\n",
"Sevilla False\n",
"Zaragoza False\n",
"Malaga False\n",
"dtype: bool"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Boolean condition\n",
"s > 1000000"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3141991\n",
"Barcelona 1604555\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cities with population greater than 1.000.000\n",
"s[s > 1000000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observe that (s > 1000000) returns a Series object. We can use this boolean vector as a filter to get a *slice* of the original series that contains only the elements where the value of the filter is True. The original Series s is not modified. This selection is called *boolean indexing*."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3141991\n",
"Barcelona 1604555\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cities with population greater than the mean\n",
"s[s > s.mean()]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3141991\n",
"Barcelona 1604555\n",
"Valencia 786189\n",
"dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cities with population greater than the median\n",
"s[s > s.median()]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid True\n",
"Barcelona True\n",
"Valencia True\n",
"Sevilla False\n",
"Zaragoza False\n",
"Malaga False\n",
"dtype: bool"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check cities with a population greater than 700.000\n",
"s > 700000"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3141991\n",
"Barcelona 1604555\n",
"Valencia 786189\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# List cities with a population greater than 700.000\n",
"s[s > 700000]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid True\n",
"Barcelona True\n",
"Valencia True\n",
"Sevilla False\n",
"Zaragoza False\n",
"Malaga False\n",
"dtype: bool"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Another way to write the same boolean indexing selection\n",
"bigger_than_700000 = s > 700000\n",
"bigger_than_700000"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3141991\n",
"Barcelona 1604555\n",
"Valencia 786189\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Cities with population > 700000\n",
"s[bigger_than_700000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operations on series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also carry out other mathematical operations."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 1570995.5\n",
"Barcelona 802277.5\n",
"Valencia 393094.5\n",
"Sevilla 346939.0\n",
"Zaragoza 332476.5\n",
"Malaga 284565.0\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Divide population by 2\n",
"s / 2"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1243449.3333333333"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the average population\n",
"s.mean()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3141991"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the highest population\n",
"s.max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Item assignment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also change values directly or based on a condition. You can consult additional feautures in the manual."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3320000\n",
"Barcelona 1604555\n",
"Valencia 786189\n",
"Sevilla 693878\n",
"Zaragoza 664953\n",
"Malaga 569130\n",
"dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Change population of one city\n",
"s['Madrid'] = 3320000\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Madrid 3652000.0\n",
"Barcelona 1765010.5\n",
"Valencia 864807.9\n",
"Sevilla 693878.0\n",
"Zaragoza 664953.0\n",
"Malaga 569130.0\n",
"dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Increase by 10% cities with population greater than 700000\n",
"s[s > 700000] = 1.1 * s[s > 700000]\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we said previously, **DataFrames** are two-dimensional data structures. You can see like a dict of Series that share the index."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"a 1.0 1.0\n",
"b 2.0 2.0\n",
"c 3.0 3.0\n",
"d NaN 4.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We are going to create a DataFrame from a dict of Series\n",
"d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
" 'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n",
"df = DataFrame(d)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this dataframe, the *indexes* (row labels) are *a*, *b*, *c* and *d* and the *columns* (column labels) are *one* and *two*.\n",
"\n",
"We see that the resulting DataFrame is the union of indexes, and missing values are included as NaN (to write this value we will use *np.nan*).\n",
"\n",
"If we specify an index, the dictionary is filtered."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"d NaN 4.0\n",
"b 2.0 2.0\n",
"a 1.0 1.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can filter\n",
"df = DataFrame(d, index=['d', 'b', 'a'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another option is to use the constructor with *index* and *columns*."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" two three\n",
"d 4.0 NaN\n",
"b 2.0 NaN\n",
"a 1.0 NaN"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next notebook we are going to learn more about dataframes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Pandas](http://pandas.pydata.org/)\n",
"* [Learning Pandas, Michael Heydt, Packt Publishing, 2015](http://proquest.safaribooksonline.com/book/programming/python/9781783985128)\n",
"* [Pandas. Introduction to Data Structures](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro)\n",
"* [Introducing Pandas Objects](https://www.oreilly.com/learning/introducing-pandas-objects)\n",
"* [Boolean Operators in Pandas](http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-operators)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Licence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
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