{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandas Dataframe - Basic Operativity"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import addutils.toc ; addutils.toc.js(ipy_notebook=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from addutils import css_notebook\n",
"css_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See [pandas documentation]() for more information and examples. Run the code at the end of the Notebook to generate the example files."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from numpy import NaN\n",
"from addutils import side_by_side2\n",
"from addutils import css_notebook\n",
"from addutils import read_txt\n",
"from IPython.core.display import HTML\n",
"from faker import Factory\n",
"css_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 File I/O and DataFrame Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas reads and format data from many different file formats: `txt, csv, web, xls, mat`. In this case we use `read_csv` to read two textual data files.\n",
"\n",
"First have a look to the file in its original form:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Date,AAPL,GOOG,JNJ,XOM\n",
"\n",
"2015-09-21,115.209999,635.440002,93.129997,73.389999\n",
"\n",
"2015-09-22,113.400002,622.690002,93.239998,72.739998\n",
"\n",
"2015-09-23,114.32,622.359985,92.989998,72.300003\n",
"\n",
"2015-09-24,115.0,625.799988,92.480003,72.730003\n",
"\n"
]
}
],
"source": [
"read_txt('temp/p01_prices.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 Create DataFrames with read_csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This file can be read and formatted at the same time using `read_csv`. Lets read the two files `p01_prices.txt` and `p01_volumes.txt`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AAPL | \n",
" GOOG | \n",
" JNJ | \n",
" XOM | \n",
"
\n",
" \n",
" Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015-09-21 | \n",
" 115.209999 | \n",
" 635.440002 | \n",
" 93.129997 | \n",
" 73.389999 | \n",
"
\n",
" \n",
" 2015-09-22 | \n",
" 113.400002 | \n",
" 622.690002 | \n",
" 93.239998 | \n",
" 72.739998 | \n",
"
\n",
" \n",
" 2015-09-23 | \n",
" 114.320000 | \n",
" 622.359985 | \n",
" 92.989998 | \n",
" 72.300003 | \n",
"
\n",
" \n",
" 2015-09-24 | \n",
" 115.000000 | \n",
" 625.799988 | \n",
" 92.480003 | \n",
" 72.730003 | \n",
"
\n",
" \n",
" 2015-09-25 | \n",
" 114.709999 | \n",
" 611.969971 | \n",
" 91.000000 | \n",
" 73.230003 | \n",
"
\n",
" \n",
" 2015-09-28 | \n",
" 112.440002 | \n",
" 594.890015 | \n",
" 91.370003 | \n",
" 72.599998 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" AAPL | \n",
" JNJ | \n",
" XOM | \n",
"
\n",
" \n",
" Date | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015-09-21 | \n",
" 46554300 | \n",
" 6971400 | \n",
" 10585500 | \n",
"
\n",
" \n",
" 2015-09-22 | \n",
" 49809000 | \n",
" 10607800 | \n",
" 14104200 | \n",
"
\n",
" \n",
" 2015-09-23 | \n",
" 35645700 | \n",
" 5597300 | \n",
" 13777500 | \n",
"
\n",
" \n",
" 2015-09-24 | \n",
" 49810600 | \n",
" 7178400 | \n",
" 14283800 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prices = pd.read_csv('temp/p01_prices.txt', index_col=0, parse_dates=[0])\n",
"volumes = pd.read_csv('temp/p01_volumes.txt', index_col=0, parse_dates=[0])\n",
"\n",
"HTML(side_by_side2(prices, volumes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both \"prices\" and \"volumes\" datasets are 2D DataFrame objects:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(prices)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Create DataFrames from Python Dictionaries"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fakeIT = Factory.create('it_IT')\n",
"data = {'Name' : [fakeIT.name() for i in range(5)],\n",
" 'Company' : [fakeIT.company() for i in range(5)],\n",
" 'City' : [fakeIT.city() for i in range(5)]}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Name | \n",
" Company | \n",
" City | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Ruth Milani | \n",
" Basile-Gentile SPA | \n",
" Quarto Elda | \n",
"
\n",
" \n",
" 1 | \n",
" Albino Marchetti | \n",
" De luca SPA | \n",
" Donati calabro | \n",
"
\n",
" \n",
" 2 | \n",
" Vienna Cattaneo | \n",
" Ferretti, Coppola e Colombo SPA | \n",
" Gioacchino sardo | \n",
"
\n",
" \n",
" 3 | \n",
" Elga Vitale | \n",
" Bianchi, Battaglia e Fontana e figli | \n",
" San Cecco umbro | \n",
"
\n",
" \n",
" 4 | \n",
" Diana Greco | \n",
" Lombardi s.r.l. | \n",
" Borgo Sarita | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Name Company City\n",
"0 Ruth Milani Basile-Gentile SPA Quarto Elda\n",
"1 Albino Marchetti De luca SPA Donati calabro\n",
"2 Vienna Cattaneo Ferretti, Coppola e Colombo SPA Gioacchino sardo\n",
"3 Elga Vitale Bianchi, Battaglia e Fontana e figli San Cecco umbro\n",
"4 Diana Greco Lombardi s.r.l. Borgo Sarita"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data, columns = ['Name','Company','City'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Create DataFrames from Items"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Name | \n",
" Company | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Dott. Fatima Carbone | \n",
" Caputo s.r.l. | \n",
"
\n",
" \n",
" 1 | \n",
" Sig. Alighiero Sorrentino | \n",
" De rosa Group | \n",
"
\n",
" \n",
" 2 | \n",
" Sig. Pablo Monti | \n",
" Cattaneo SPA | \n",
"
\n",
" \n",
" 3 | \n",
" Demian Palmieri | \n",
" Mazza-Martinelli SPA | \n",
"
\n",
" \n",
" 4 | \n",
" Sig.ra Loretta Martino | \n",
" Conte-Sartori Group | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Name Company\n",
"0 Dott. Fatima Carbone Caputo s.r.l.\n",
"1 Sig. Alighiero Sorrentino De rosa Group\n",
"2 Sig. Pablo Monti Cattaneo SPA\n",
"3 Demian Palmieri Mazza-Martinelli SPA\n",
"4 Sig.ra Loretta Martino Conte-Sartori Group"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame.from_items([('Name', [fakeIT.name() for i in range(5)]),\n",
" ('Company', [fakeIT.company() for i in range(5)])])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 Create DataFrames fron Numpy Arrays"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" ONE | \n",
" TWO | \n",
"
\n",
" \n",
" \n",
" \n",
" a | \n",
" 2 | \n",
" 3 | \n",
"
\n",
" \n",
" b | \n",
" 5 | \n",
" 6 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" ONE TWO\n",
"a 2 3\n",
"b 5 6"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.array([[2,5],[3,6]]).T, index=list('ab'), columns=['ONE','TWO'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.5 DataFrames can be converted in Numpy Arrays"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 3],\n",
" [5, 6]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.asarray(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.6 DataFrames, Series and Panels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Pandas there are 3 main data structure types (\"data container\" objects): \n",
"\n",
"* **Series:** for one-dimensional data \n",
"* **DataFrames:** for bi-dimensional data (matrices) \n",
"* **Panels:** for 3D or nD data \n",
"\n",
"For simplicity, in this course we will describe only pandas Series and DataFrames."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 Automatic Data Alignment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see the dates has been interpreted correctly and used as row index. Notice that the rows in the two datafiles are misaligned, this is not a problem in pandas because the *Automatic Data Alignment* feature: an operation involving the two datasets will simply use `NaN` for the undefined (misaligned) values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AAPL | \n",
" GOOG | \n",
" JNJ | \n",
" XOM | \n",
"
\n",
" \n",
" Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015-09-21 | \n",
" 5.363521e+09 | \n",
" NaN | \n",
" 6.492465e+08 | \n",
" 7.768698e+08 | \n",
"
\n",
" \n",
" 2015-09-22 | \n",
" 5.648341e+09 | \n",
" NaN | \n",
" 9.890713e+08 | \n",
" 1.025939e+09 | \n",
"
\n",
" \n",
" 2015-09-23 | \n",
" 4.075016e+09 | \n",
" NaN | \n",
" 5.204929e+08 | \n",
" 9.961133e+08 | \n",
"
\n",
" \n",
" 2015-09-24 | \n",
" 5.728219e+09 | \n",
" NaN | \n",
" 6.638585e+08 | \n",
" 1.038861e+09 | \n",
"
\n",
" \n",
" 2015-09-25 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 2015-09-28 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" AAPL GOOG JNJ XOM\n",
"Date \n",
"2015-09-21 5.363521e+09 NaN 6.492465e+08 7.768698e+08\n",
"2015-09-22 5.648341e+09 NaN 9.890713e+08 1.025939e+09\n",
"2015-09-23 4.075016e+09 NaN 5.204929e+08 9.961133e+08\n",
"2015-09-24 5.728219e+09 NaN 6.638585e+08 1.038861e+09\n",
"2015-09-25 NaN NaN NaN NaN\n",
"2015-09-28 NaN NaN NaN NaN"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prices*volumes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which can be better formatted to a \"2 decimal places float number\" with comma as thousands separator (see Package Options):"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AAPL | \n",
" GOOG | \n",
" JNJ | \n",
" XOM | \n",
"
\n",
" \n",
" Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015-09-21 | \n",
" 5,363,520,856.4 | \n",
" - | \n",
" 649,246,461.1 | \n",
" 776,869,834.4 | \n",
"
\n",
" \n",
" 2015-09-22 | \n",
" 5,648,340,699.6 | \n",
" - | \n",
" 989,071,250.8 | \n",
" 1,025,939,479.8 | \n",
"
\n",
" \n",
" 2015-09-23 | \n",
" 4,075,016,424.0 | \n",
" - | \n",
" 520,492,915.8 | \n",
" 996,113,291.3 | \n",
"
\n",
" \n",
" 2015-09-24 | \n",
" 5,728,219,000.0 | \n",
" - | \n",
" 663,858,453.5 | \n",
" 1,038,860,816.9 | \n",
"
\n",
" \n",
" 2015-09-25 | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" 2015-09-28 | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" AAPL GOOG JNJ XOM\n",
"Date \n",
"2015-09-21 5,363,520,856.4 - 649,246,461.1 776,869,834.4\n",
"2015-09-22 5,648,340,699.6 - 989,071,250.8 1,025,939,479.8\n",
"2015-09-23 4,075,016,424.0 - 520,492,915.8 996,113,291.3\n",
"2015-09-24 5,728,219,000.0 - 663,858,453.5 1,038,860,816.9\n",
"2015-09-25 - - - -\n",
"2015-09-28 - - - -"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.float_format', lambda x: '{:,.1f}'.format(x)) # formatting\n",
"(prices*volumes).replace('nan', '-') # replacing NaN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example: calculate the volume-weighted average price"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"AAPL 114.5\n",
"JNJ 93.0\n",
"XOM 72.8\n",
"dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vwap = (prices*volumes).sum()/volumes.sum()\n",
"vwap.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 Indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Label-Based Indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`.loc` is strictly label based, will raise KeyError when the items are not found, allowed inputs are:\n",
"\n",
"* A single label\n",
"* A list or array of labels [’a’, ’b’, ’c’]\n",
"* A slice object with labels [’a’:’f’] (note that contrary to usual python slices, both the start and the stop are included!)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AAPL | \n",
" GOOG | \n",
" JNJ | \n",
" XOM | \n",
"
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" \n",
" Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015-09-21 | \n",
" 115.2 | \n",
" 635.4 | \n",
" 93.1 | \n",
" 73.4 | \n",
"
\n",
" \n",
" 2015-09-22 | \n",
" 113.4 | \n",
" 622.7 | \n",
" 93.2 | \n",
" 72.7 | \n",
"
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" \n",
" 2015-09-23 | \n",
" 114.3 | \n",
" 622.4 | \n",
" 93.0 | \n",
" 72.3 | \n",
"
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" \n",
" 2015-09-24 | \n",
" 115.0 | \n",
" 625.8 | \n",
" 92.5 | \n",
" 72.7 | \n",
"
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" \n",
" 2015-09-25 | \n",
" 114.7 | \n",
" 612.0 | \n",
" 91.0 | \n",
" 73.2 | \n",
"
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" \n",
" 2015-09-28 | \n",
" 112.4 | \n",
" 594.9 | \n",
" 91.4 | \n",
" 72.6 | \n",
"
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"
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" | \n",
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"
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" \n",
" \n",
" \n",
"
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"
"
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(side_by_side2(prices, prices.loc['2012-11-21':'2012-11-27',['AAPL', 'GOOG']]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Columns can be selected without specifying the index:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(side_by_side2(prices, prices[['AAPL', 'GOOG']]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Position-Based Indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`.iloc` is strictly position based, will raise KeyError when the items are out of bounds:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" 113.4 | \n",
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" 2015-09-23 | \n",
" 114.3 | \n",
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" 115.0 | \n",
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],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(side_by_side2(prices, prices.iloc[1:5,[0, 1]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.3 Mixed Indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3 Advanced Indexing - .ix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`.ix` will always **try first** to resolve **labeled** index (like `.loc`), **then** it will fall back on **potitional** indexing (like `.loc`)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rows can be indexed using the ix method. Try by yourself: \n",
"\n",
" prices.ix[1:4,0:2] # Position-based Indexing \n",
" prices.ix[:'2012-11-23'] # Label-based Indexing on index (rows)\n",
" prices.ix[:,[2,2,1]] # Duplicated values on columns \n",
" prices.ix[::2] # One value every two rows\n",
" prices.ix[::-1] # Reverse rows\n",
" prices.ix[prices['AAPL'] > 380] # Boolean indexing on index\n",
" prices.ix[:,[len(c)<4 for c in prices.columns]] # Boolean indexing on columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" 2015-09-22 | \n",
" 93.2 | \n",
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"text/plain": [
" JNJ XOM\n",
"Date \n",
"2015-09-21 93.1 73.4\n",
"2015-09-22 93.2 72.7\n",
"2015-09-23 93.0 72.3\n",
"2015-09-24 92.5 72.7\n",
"2015-09-25 91.0 73.2\n",
"2015-09-28 91.4 72.6"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prices.ix[:,[len(c)<4 for c in prices.columns]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4 DataFrame Basic Operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 Reindex/Reorder rows and columns"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" | \n",
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" W | \n",
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" T | \n",
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" b | \n",
" 6.0 | \n",
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" nan | \n",
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" a | \n",
" 3.0 | \n",
" 2.0 | \n",
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" 12.0 | \n",
" 11.0 | \n",
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"
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" z | \n",
" nan | \n",
" nan | \n",
" nan | \n",
"
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" \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = np.array([[2,5,8,11],[3,6,9,12]])\n",
"d1 = pd.DataFrame(data.T, index=list('abce'), columns=['K','W'])\n",
"HTML(side_by_side2(d1, pd.DataFrame(d1, index=list('baez'), columns=['W','K','T'])) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 Calculate new columns"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"
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],
"text/plain": [
" K W Z B\n",
"a 2 3 1 False\n",
"b 5 6 1 True\n",
"c 8 9 1 True\n",
"e 11 12 1 True"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d1['Z'] = d1['W']-d1['K']\n",
"d1['B'] = d1['W']>4\n",
"d1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 Deleting rows and columns"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" \n",
" | \n",
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" SUM | \n",
"
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" \n",
" \n",
" \n",
" a | \n",
" 2 | \n",
" 3 | \n",
" 6.0 | \n",
"
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" \n",
" b | \n",
" 5 | \n",
" 6 | \n",
" 13.0 | \n",
"
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" c | \n",
" 8 | \n",
" 9 | \n",
" 19.0 | \n",
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" 12 | \n",
" 25.0 | \n",
"
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"
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],
"text/plain": [
""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d1['SUM'] = d1.sum(axis=1)\n",
"HTML(side_by_side2(d1, d1.drop(['b', 'c'], axis=0), d1.drop(['Z', 'B'], axis=1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.4 Inserting colums in a specific position"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" | \n",
" K | \n",
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" W | \n",
" Z | \n",
" B | \n",
" SUM | \n",
"
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" \n",
" \n",
" \n",
" a | \n",
" 2 | \n",
" 20.1 | \n",
" 3 | \n",
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" 6.0 | \n",
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" \n",
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" 5 | \n",
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" 1 | \n",
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" 13.0 | \n",
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" 8 | \n",
" 8,103.1 | \n",
" 9 | \n",
" 1 | \n",
" True | \n",
" 19.0 | \n",
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" 11 | \n",
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" 12 | \n",
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"
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" \n",
"
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"
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],
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d2 = d1.copy() # .copy() method is needed to create a new object.\n",
"d2.insert(1, 'Exp(W)', np.exp(d1['W']))\n",
"HTML(side_by_side2(d1, d2, space=10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example: Indexing rows to create a new column with empty values, then use the Forward Fill Padding to fill the gaps"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"
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],
"text/plain": [
" K W Z B SUM part\n",
"a 2 3 1 False 6.0 2.0\n",
"b 5 6 1 True 13.0 5.0\n",
"c 8 9 1 True 19.0 5.0\n",
"e 11 12 1 True 25.0 5.0"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d1['part'] = d1['K'].ix[:2]\n",
"d1['part'] = d1['part'].fillna(method='ffill')\n",
"d1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.5 Check if a value or a list of given values are contained in a specific column"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# TODO: Upgrade side_by_side2 to include series\n",
"HTML(side_by_side2(d1, d1['K'].isin([3, 8])))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.6 Rename columns"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(side_by_side2(d1, d1.rename(columns={'K':'ONE','W':'TWO','Z':'THREE'})))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.7 Iterate efficiently through rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`iterrows` returns an iterator yielding each index value along with a Series containing the data in each row:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a ** 2 - 3 - 1 - False - 6.0 - 2.0\n",
"b ** 5 - 6 - 1 - True - 13.0 - 5.0\n",
"c ** 8 - 9 - 1 - True - 19.0 - 5.0\n",
"e ** 11 - 12 - 1 - True - 25.0 - 5.0\n"
]
}
],
"source": [
"for row_index, row in d1.iterrows():\n",
" print(row_index, '**', ' - '.join([str(item) for item in row]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`itertuples` returns an iterator yielding a tuple for each row in the DataFrame. The first element of the tuple is the row’s corresponding index value, while the remaining elements are the row values:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('a', 2, 3, 1, False, 6.0, 2.0)\n",
"('b', 5, 6, 1, True, 13.0, 5.0)\n",
"('c', 8, 9, 1, True, 19.0, 5.0)\n",
"('e', 11, 12, 1, True, 25.0, 5.0)\n"
]
}
],
"source": [
"for t in d1.itertuples():\n",
" print(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5 Duplicated Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.1 Find duplicated data in columns"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" a b c a dup a+b dup a+b dup - take last\n",
"0 one x -0.3 False False False\n",
"1 one y -0.1 True False False\n",
"2 two y 1.2 False False True\n",
"3 three x -0.5 False False False\n",
"4 two y 1.3 True True False"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d3 = pd.read_csv('temp/p01_d2.csv', index_col=0)\n",
"d3['a dup'] = d3.duplicated(['a'])\n",
"d3['a+b dup'] = d3.duplicated(['a', 'b'])\n",
"d3['a+b dup - take last'] = d3.duplicated(['a', 'b'], keep='last')\n",
"d3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Remove Duplicates"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
" a b c a dup a+b dup a+b dup - take last\n",
"0 one x -0.3 False False False\n",
"1 one y -0.1 True False False\n",
"3 three x -0.5 False False False\n",
"4 two y 1.3 True True False"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d3.drop_duplicates(['a', 'b'],keep='last')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6 Working with Large Arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6.1 Control the DataFrame memory occupation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by generating a DataFrame from a Numpy Array. We'll see than there is no memory overhead on DataFrame Values:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows x Cols x 8: 80000\n",
"np Array Memory Occupation: 80000\n",
"Dataframe Values Memory Occupation: 80000\n",
"Dataframe Index Memory Occupation: 800\n",
"Dataframe Columns Memory Occupation: 800\n"
]
}
],
"source": [
"rows, cols = 100, 100\n",
"np_array = np.array(np.random.randn(rows, cols), dtype=np.float64)\n",
"d4 = pd.DataFrame(np_array)\n",
"print ('Rows x Cols x 8: ', rows*cols*8)\n",
"print ('np Array Memory Occupation: ', np_array.nbytes)\n",
"print ('Dataframe Values Memory Occupation: ', d4.values.nbytes)\n",
"print ('Dataframe Index Memory Occupation: ', d4.index.nbytes)\n",
"print ('Dataframe Columns Memory Occupation: ', d4.columns.nbytes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To reduce the memory occupation it's possible to change the value's dtype:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataframe Values Memory Occupation: 20000\n"
]
}
],
"source": [
"d4 = d4.astype(dtype=np.float16)\n",
"print ('Dataframe Values Memory Occupation: ', d4.values.nbytes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the data is sparse the Dataframe can be sparsified as well to save further resources with the `to_sparse()` method:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataframe Values Memory Occupation: 20000\n",
"Dataframe Values Memory Occupation: 80000\n"
]
}
],
"source": [
"d4.ix[2:,4:] = np.nan\n",
"print ('Dataframe Values Memory Occupation: ', d4.values.nbytes)\n",
"d4 = d4.to_sparse()\n",
"print ('Dataframe Values Memory Occupation: ', d4.values.nbytes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case rows and colums are np.int64 arrays:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99],\n",
" dtype='int64')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d4.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6.2 Explore large arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Working with large arrays: in Excel is difficult to explore arrays with thousands of lines and columns. Explore the pandas capabilities with the following code. The first line visualize the firts two lines, while the second actually load the whole file. Try to do the same in Excel for comparison."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d3 = pd.read_csv('example_data/p01_d3.csv.gz', compression='gzip')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Istat - Comune - Provincia - Regione - Prefisso - CAP - CodFisco - Abitanti - Link - "
]
}
],
"source": [
"for col in d3.columns: \n",
" print (col, end=' - ')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try by yourself: \n",
"\n",
" d3.head()\n",
" d3[d3.columns[:3]].head()\n",
" d3[d3.columns[-4:-1]].tail()\n",
" d3.ix[1000:1010, :7]\n",
" d3.ix[:, 'Abitanti'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" CodFisco | \n",
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"text/plain": [
" CAP CodFisco Abitanti\n",
"8087 33020 M200 607\n",
"8088 13848 M201 1152\n",
"8089 87040 M202 2413\n",
"8090 83030 M203 1232\n",
"8091 89867 M204 2055"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d3[d3.columns[-4:-1]].tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7 Column pct_change and shift"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is possibile to add multiple new columns to a `DataFrame`."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" one two three four five\n",
"0 3.0 8.0 3.0 1.0 nan\n",
"1 5.0 16.0 6.0 2.0 nan\n",
"2 7.0 28.0 nan 4.0 nan\n",
"3 10.0 37.0 nan 7.0 nan\n",
"4 13.0 45.0 15.0 11.0 16.0\n",
"5 16.0 57.0 18.0 16.0 19.0\n",
"6 56.0 69.0 nan 65.0 82.0\n",
"7 72.0 90.0 nan 88.0 91.0"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = np.array([[3, 5, 7, 10, 13, 16, 56, 72],\n",
" [8, 16, 28, 37, 45, 57, 69, 90],\n",
" [3, 6, NaN, NaN, 15, 18, NaN, NaN],\n",
" [1, 2, 4, 7, 11, 16, 65, 88],\n",
" [NaN, NaN, NaN, NaN, 16, 19, 82, 91]])\n",
"d4 = pd.DataFrame(data.T, columns=['one', 'two', 'three', 'four', 'five'])\n",
"d4"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" 7 | \n",
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" 91.0 | \n",
" 1.3 | \n",
" 1.3 | \n",
"
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" \n",
"
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"
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],
"text/plain": [
" one two three four five one ret two ret\n",
"0 3.0 8.0 3.0 1.0 nan nan nan\n",
"1 5.0 16.0 6.0 2.0 nan 1.7 2.0\n",
"2 7.0 28.0 nan 4.0 nan 1.4 1.8\n",
"3 10.0 37.0 nan 7.0 nan 1.4 1.3\n",
"4 13.0 45.0 15.0 11.0 16.0 1.3 1.2\n",
"5 16.0 57.0 18.0 16.0 19.0 1.2 1.3\n",
"6 56.0 69.0 nan 65.0 82.0 3.5 1.2\n",
"7 72.0 90.0 nan 88.0 91.0 1.3 1.3"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d4[['one ret','two ret']] = d4[['one','two']].pct_change()+1\n",
"d4"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"
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],
"text/plain": [
" one two three four five one ret two ret four var\n",
"0 3.0 8.0 3.0 1.0 nan nan nan nan\n",
"1 5.0 16.0 6.0 2.0 nan 1.7 2.0 0.0\n",
"2 7.0 28.0 nan 4.0 nan 1.4 1.8 0.7\n",
"3 10.0 37.0 nan 7.0 nan 1.4 1.3 1.1\n",
"4 13.0 45.0 15.0 11.0 16.0 1.3 1.2 1.4\n",
"5 16.0 57.0 18.0 16.0 19.0 1.2 1.3 1.6\n",
"6 56.0 69.0 nan 65.0 82.0 3.5 1.2 3.9\n",
"7 72.0 90.0 nan 88.0 91.0 1.3 1.3 3.1"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d4['four var'] = np.log(d4['four'] - d4['four'].shift())\n",
"d4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8 Reindex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`DataFrame.reindex` method conforms a `DataFrame` to a new index, filling cells with no values. It is possible to use this method to rearrange columns."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
" one one ret two two ret four four var\n",
"0 3.0 nan 8.0 nan 1.0 nan\n",
"1 5.0 1.7 16.0 2.0 2.0 0.0\n",
"2 7.0 1.4 28.0 1.8 4.0 0.7\n",
"3 10.0 1.4 37.0 1.3 7.0 1.1\n",
"4 13.0 1.3 45.0 1.2 11.0 1.4\n",
"5 16.0 1.2 57.0 1.3 16.0 1.6\n",
"6 56.0 3.5 69.0 1.2 65.0 3.9\n",
"7 72.0 1.3 90.0 1.3 88.0 3.1"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d5 = d4.reindex(columns=['one','one ret','two','two ret','four','four var'])\n",
"d5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that `DataFrame.reindex` gives a new view, hence `d4` isn't changed."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"
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" 3.1 | \n",
"
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" \n",
"
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"
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],
"text/plain": [
" one two three four five one ret two ret four var\n",
"0 3.0 8.0 3.0 1.0 nan nan nan nan\n",
"1 5.0 16.0 6.0 2.0 nan 1.7 2.0 0.0\n",
"2 7.0 28.0 nan 4.0 nan 1.4 1.8 0.7\n",
"3 10.0 37.0 nan 7.0 nan 1.4 1.3 1.1\n",
"4 13.0 45.0 15.0 11.0 16.0 1.3 1.2 1.4\n",
"5 16.0 57.0 18.0 16.0 19.0 1.2 1.3 1.6\n",
"6 56.0 69.0 nan 65.0 82.0 3.5 1.2 3.9\n",
"7 72.0 90.0 nan 88.0 91.0 1.3 1.3 3.1"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9 More on Indexing: Multi Index"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"text/plain": [
" 0 1 2\n",
"Country Number Dir \n",
"Fra one x -0.1 0.3 -0.2\n",
" two y 0.3 -0.9 0.3\n",
" z 1.8 1.0 -1.4\n",
"Ger one x -0.7 -0.5 0.4\n",
"Jap one x 1.2 1.2 -0.2\n",
" two x -0.4 0.5 -0.6\n",
"USA one y -1.9 -0.9 0.5\n",
" z -0.1 -1.0 0.7"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d6 = pd.read_csv('temp/p01_d4.csv', index_col=['Country',\n",
" 'Number',\n",
" 'Dir'])\n",
"d6 = d6.sortlevel()\n",
"d6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try by yourself:\n",
"\n",
" d6.ix['Fra']\n",
" d6.ix['Fra', 'two']\n",
" d6.ix['Fra':'Ger']\n",
" d6.reorder_levels([2,1,0], axis=0).sortlevel(0)\n",
" d6.reset_index(level=1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" 0 1 2\n",
"Number Dir \n",
"one x -0.1 0.3 -0.2\n",
"two y 0.3 -0.9 0.3\n",
" z 1.8 1.0 -1.4"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d6.ix['Fra'] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10 Package Options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The way the DataFrames are displayed can be customized ijn many ways: ([See documentation](http://pandas.pydata.org/pandas-docs/stable/basics.html#working-with-package-options)):"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Display Max Rows: \t 60\n",
"display.max_rows : int\n",
" If max_rows is exceeded, switch to truncate view. Depending on\n",
" `large_repr`, objects are either centrally truncated or printed as\n",
" a summary view. 'None' value means unlimited.\n",
"\n",
" In case python/IPython is running in a terminal and `large_repr`\n",
" equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
" the height of the terminal and print a truncated object which fits\n",
" the screen height. The IPython notebook, IPython qtconsole, or\n",
" IDLE do not run in a terminal and hence it is not possible to do\n",
" correct auto-detection.\n",
" [default: 60] [currently: 60]\n",
"\n",
"\n"
]
}
],
"source": [
"print ('Display Max Rows: \\t', pd.get_option('display.max_rows'))\n",
"pd.describe_option('display.max_rows')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.max_rows', 10)\n",
"pd.get_option('display.max_rows')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"60"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.reset_option('display.max_rows')\n",
"pd.get_option('display.max_rows')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Try by yourself**:\n",
"\n",
" pd.describe_option('display.chop_threshold')\n",
" pd.describe_option('display.colheader_justify')\n",
" pd.describe_option('display.column_space')\n",
" pd.describe_option('display.date_dayfirst')\n",
" pd.describe_option('display.date_yearfirst')\n",
" pd.describe_option('display.encoding')\n",
" pd.describe_option('display.expand_frame_repr')\n",
" pd.describe_option('display.float_format')\n",
" pd.describe_option('display.max_columns')\n",
" pd.describe_option('display.max_colwidth')\n",
" pd.describe_option('display.max_rows')\n",
" pd.describe_option('display.notebook_repr_html')\n",
" pd.describe_option('display.precision')\n",
" # for more options see documentation..."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" AAPL GOOG JNJ XOM\n",
"Date \n",
"2015-09-21 115.2 635.4 93.1 73.4\n",
"2015-09-22 113.4 622.7 93.2 72.7\n",
"2015-09-23 114.3 622.4 93.0 72.3\n",
"2015-09-24 115.0 625.8 92.5 72.7\n",
"2015-09-25 114.7 612.0 91.0 73.2\n",
"2015-09-28 112.4 594.9 91.4 72.6"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.precision', 2)\n",
"pd.set_option('display.notebook_repr_html', False)\n",
"prices"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
" AAPL GOOG JNJ XOM\n",
"Date \n",
"2015-09-21 115.2 635.4 93.1 73.4\n",
"2015-09-22 113.4 622.7 93.2 72.7\n",
"2015-09-23 114.3 622.4 93.0 72.3\n",
"2015-09-24 115.0 625.8 92.5 72.7\n",
"2015-09-25 114.7 612.0 91.0 73.2\n",
"2015-09-28 112.4 594.9 91.4 72.6"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.set_option('display.notebook_repr_html', True)\n",
"prices"
]
},
{
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"metadata": {},
"source": [
"---\n",
"\n",
"Visit [www.add-for.com]() for more tutorials and updates.\n",
"\n",
"This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License."
]
}
],
"metadata": {
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"name": "python3"
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