diff --git a/pandas/pandas.ipynb b/pandas/pandas.ipynb
index 6e4873948b0010464231e4c7d9f9cc93ff054bd1..31cfbf3bdd2334d8da92e5383b75f8b1b068d816 100644
--- a/pandas/pandas.ipynb
+++ b/pandas/pandas.ipynb
@@ -1,23 +1,1919 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data Manging with pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Introduction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## powerful Python data analysis toolkit"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "pandas is a Python package aiming to provide\n",
+ "- fast\n",
+ "- flexible\n",
+ "- expressive\n",
+ "\n",
+ "data structures designed to make working with\n",
+ "\n",
+ "- relational\n",
+ "- labeled\n",
+ "\n",
+ "data both easy and intuitive. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Suitable for data of all sorts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Tabular data with columns of different data types (as in an SQL table or Excel spreadsheet)\n",
+ "- Ordered and unordered time series data (not necessarily fixed-frequency)\n",
+ "- Arbitrary matrix data with row and column labels (homogeneously typed or heterogeneous)\n",
+ "- Any other form of observational / statistical data sets (the data need not be labeled to be placed into a pandas data structure)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Primary data structures"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Series (1-dimensional)\n",
+ "- DataFrame (2-dimensional)\n",
+ "\n",
+ "For R users, DataFrame provides everything that R’s data.frame provides and much more.\n",
+ "\n",
+ "pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting started"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "u'0.20.1'"
+ ]
+ },
+ "execution_count": 97,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.__version__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Object creation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Series"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1.0\n",
+ "1 3.0\n",
+ "2 5.0\n",
+ "3 NaN\n",
+ "4 6.0\n",
+ "5 8.0\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create Series with missing data\n",
+ "s = pd.Series([1,3,5,np.nan,6,8])\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DatetimeIndex(['2017-09-01', '2017-09-02', '2017-09-03', '2017-09-04',\n",
+ " '2017-09-05', '2017-09-06'],\n",
+ " dtype='datetime64[ns]', freq='D')"
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create DatetimeIndex for 6 days\n",
+ "dates = pd.date_range('20170901', periods=6)\n",
+ "dates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.23051414, -0.28580632, 0.35457973, 1.63813223],\n",
+ " [ 1.46610552, 0.36143299, -0.69967186, 0.5909181 ],\n",
+ " [ 0.85840437, -0.20824091, -0.32334378, -0.26983698],\n",
+ " [ 0.26830511, -0.78065576, -0.22962856, 0.77808124],\n",
+ " [ 1.83936044, 1.14887577, 1.2779539 , 0.70010126],\n",
+ " [-1.19054289, 0.91047078, -0.0720562 , -0.41625165]])"
+ ]
+ },
+ "execution_count": 100,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create 6x4 NP array with random values\n",
+ "ran_values = np.random.randn(6,4)\n",
+ "ran_values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(ran_values, columns=list('ABDC'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>-0.285806</td>\n",
+ " <td>0.354580</td>\n",
+ " <td>1.638132</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>0.700101</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>0.910471</td>\n",
+ " <td>-0.072056</td>\n",
+ " <td>-0.416252</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "0 0.230514 -0.285806 0.354580 1.638132\n",
+ "1 1.466106 0.361433 -0.699672 0.590918\n",
+ "2 0.858404 -0.208241 -0.323344 -0.269837\n",
+ "3 0.268305 -0.780656 -0.229629 0.778081\n",
+ "4 1.839360 1.148876 1.277954 0.700101\n",
+ "5 -1.190543 0.910471 -0.072056 -0.416252"
+ ]
+ },
+ "execution_count": 102,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.set_index(dates, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-01</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>-0.285806</td>\n",
+ " <td>0.354580</td>\n",
+ " <td>1.638132</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>0.700101</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-06</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>0.910471</td>\n",
+ " <td>-0.072056</td>\n",
+ " <td>-0.416252</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-01 0.230514 -0.285806 0.354580 1.638132\n",
+ "2017-09-02 1.466106 0.361433 -0.699672 0.590918\n",
+ "2017-09-03 0.858404 -0.208241 -0.323344 -0.269837\n",
+ "2017-09-04 0.268305 -0.780656 -0.229629 0.778081\n",
+ "2017-09-05 1.839360 1.148876 1.277954 0.700101\n",
+ "2017-09-06 -1.190543 0.910471 -0.072056 -0.416252"
+ ]
+ },
+ "execution_count": 104,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>C</th>\n",
+ " <th>D</th>\n",
+ " <th>E</th>\n",
+ " <th>F</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.0</td>\n",
+ " <td>2017-09-20</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3</td>\n",
+ " <td>LUMC</td>\n",
+ " <td>researcher</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1.0</td>\n",
+ " <td>2017-09-20</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3</td>\n",
+ " <td>EMC</td>\n",
+ " <td>researcher</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1.0</td>\n",
+ " <td>2017-09-20</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3</td>\n",
+ " <td>LUMC</td>\n",
+ " <td>researcher</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.0</td>\n",
+ " <td>2017-09-20</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3</td>\n",
+ " <td>EMC</td>\n",
+ " <td>researcher</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B C D E F\n",
+ "0 1.0 2017-09-20 1.0 3 LUMC researcher\n",
+ "1 1.0 2017-09-20 1.0 3 EMC researcher\n",
+ "2 1.0 2017-09-20 1.0 3 LUMC researcher\n",
+ "3 1.0 2017-09-20 1.0 3 EMC researcher"
+ ]
+ },
+ "execution_count": 105,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create DataFrame by using a dict of series-like objects.\n",
+ "df2 = pd.DataFrame({ 'A' : 1.,\n",
+ " 'B' : pd.Timestamp('20170920'),\n",
+ " 'C' : pd.Series(1, index=list(range(4)), dtype='float32'),\n",
+ " 'D' : np.array([3] * 4, dtype='int32'),\n",
+ " 'E' : pd.Categorical([\"LUMC\",\"EMC\",\"LUMC\",\"EMC\"]),\n",
+ " 'F' : 'researcher'\n",
+ " })\n",
+ "df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A float64\n",
+ "B datetime64[ns]\n",
+ "C float32\n",
+ "D int32\n",
+ "E category\n",
+ "F object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 106,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.dtypes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tab completion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# df.<TAB> shows attributes and column names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exploring data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Top (head) and bottom (tail) of data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-01</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>-0.285806</td>\n",
+ " <td>0.354580</td>\n",
+ " <td>1.638132</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>0.700101</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-01 0.230514 -0.285806 0.354580 1.638132\n",
+ "2017-09-02 1.466106 0.361433 -0.699672 0.590918\n",
+ "2017-09-03 0.858404 -0.208241 -0.323344 -0.269837\n",
+ "2017-09-04 0.268305 -0.780656 -0.229629 0.778081\n",
+ "2017-09-05 1.839360 1.148876 1.277954 0.700101"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>0.700101</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-06</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>0.910471</td>\n",
+ " <td>-0.072056</td>\n",
+ " <td>-0.416252</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-05 1.839360 1.148876 1.277954 0.700101\n",
+ "2017-09-06 -1.190543 0.910471 -0.072056 -0.416252"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.tail(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Meta data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DatetimeIndex(['2017-09-01', '2017-09-02', '2017-09-03', '2017-09-04',\n",
+ " '2017-09-05', '2017-09-06'],\n",
+ " dtype='datetime64[ns]', freq='D')"
+ ]
+ },
+ "execution_count": 110,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index([u'A', u'B', u'D', u'C'], dtype='object')"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.23051414, -0.28580632, 0.35457973, 1.63813223],\n",
+ " [ 1.46610552, 0.36143299, -0.69967186, 0.5909181 ],\n",
+ " [ 0.85840437, -0.20824091, -0.32334378, -0.26983698],\n",
+ " [ 0.26830511, -0.78065576, -0.22962856, 0.77808124],\n",
+ " [ 1.83936044, 1.14887577, 1.2779539 , 0.70010126],\n",
+ " [-1.19054289, 0.91047078, -0.0720562 , -0.41625165]])"
+ ]
+ },
+ "execution_count": 112,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic statistics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>6.000000</td>\n",
+ " <td>6.000000</td>\n",
+ " <td>6.000000</td>\n",
+ " <td>6.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.578691</td>\n",
+ " <td>0.191013</td>\n",
+ " <td>0.051306</td>\n",
+ " <td>0.503524</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.077292</td>\n",
+ " <td>0.747672</td>\n",
+ " <td>0.692115</td>\n",
+ " <td>0.755390</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>-0.416252</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.239962</td>\n",
+ " <td>-0.266415</td>\n",
+ " <td>-0.299915</td>\n",
+ " <td>-0.054648</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.563355</td>\n",
+ " <td>0.076596</td>\n",
+ " <td>-0.150842</td>\n",
+ " <td>0.645510</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>1.314180</td>\n",
+ " <td>0.773211</td>\n",
+ " <td>0.247921</td>\n",
+ " <td>0.758586</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>1.638132</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "count 6.000000 6.000000 6.000000 6.000000\n",
+ "mean 0.578691 0.191013 0.051306 0.503524\n",
+ "std 1.077292 0.747672 0.692115 0.755390\n",
+ "min -1.190543 -0.780656 -0.699672 -0.416252\n",
+ "25% 0.239962 -0.266415 -0.299915 -0.054648\n",
+ "50% 0.563355 0.076596 -0.150842 0.645510\n",
+ "75% 1.314180 0.773211 0.247921 0.758586\n",
+ "max 1.839360 1.148876 1.277954 1.638132"
+ ]
+ },
+ "execution_count": 113,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Transposing data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>2017-09-01 00:00:00</th>\n",
+ " <th>2017-09-02 00:00:00</th>\n",
+ " <th>2017-09-03 00:00:00</th>\n",
+ " <th>2017-09-04 00:00:00</th>\n",
+ " <th>2017-09-05 00:00:00</th>\n",
+ " <th>2017-09-06 00:00:00</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>A</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.858404</td>\n",
+ " <td>0.268305</td>\n",
+ " <td>1.839360</td>\n",
+ " <td>-1.190543</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>B</th>\n",
+ " <td>-0.285806</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>0.910471</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D</th>\n",
+ " <td>0.354580</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>-0.072056</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C</th>\n",
+ " <td>1.638132</td>\n",
+ " <td>0.590918</td>\n",
+ " <td>-0.269837</td>\n",
+ " <td>0.778081</td>\n",
+ " <td>0.700101</td>\n",
+ " <td>-0.416252</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 2017-09-01 2017-09-02 2017-09-03 2017-09-04 2017-09-05 2017-09-06\n",
+ "A 0.230514 1.466106 0.858404 0.268305 1.839360 -1.190543\n",
+ "B -0.285806 0.361433 -0.208241 -0.780656 1.148876 0.910471\n",
+ "D 0.354580 -0.699672 -0.323344 -0.229629 1.277954 -0.072056\n",
+ "C 1.638132 0.590918 -0.269837 0.778081 0.700101 -0.416252"
+ ]
+ },
+ "execution_count": 114,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sorting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>C</th>\n",
+ " <th>D</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-01</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>-0.285806</td>\n",
+ " <td>1.638132</td>\n",
+ " <td>0.354580</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>0.590918</td>\n",
+ " <td>-0.699672</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.269837</td>\n",
+ " <td>-0.323344</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>0.778081</td>\n",
+ " <td>-0.229629</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>0.700101</td>\n",
+ " <td>1.277954</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-06</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>0.910471</td>\n",
+ " <td>-0.416252</td>\n",
+ " <td>-0.072056</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B C D\n",
+ "2017-09-01 0.230514 -0.285806 1.638132 0.354580\n",
+ "2017-09-02 1.466106 0.361433 0.590918 -0.699672\n",
+ "2017-09-03 0.858404 -0.208241 -0.269837 -0.323344\n",
+ "2017-09-04 0.268305 -0.780656 0.778081 -0.229629\n",
+ "2017-09-05 1.839360 1.148876 0.700101 1.277954\n",
+ "2017-09-06 -1.190543 0.910471 -0.416252 -0.072056"
+ ]
+ },
+ "execution_count": 121,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Sort on axis\n",
+ "df.sort_index(axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-01</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>-0.285806</td>\n",
+ " <td>0.354580</td>\n",
+ " <td>1.638132</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-06</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>0.910471</td>\n",
+ " <td>-0.072056</td>\n",
+ " <td>-0.416252</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " <td>0.700101</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-04 0.268305 -0.780656 -0.229629 0.778081\n",
+ "2017-09-01 0.230514 -0.285806 0.354580 1.638132\n",
+ "2017-09-03 0.858404 -0.208241 -0.323344 -0.269837\n",
+ "2017-09-02 1.466106 0.361433 -0.699672 0.590918\n",
+ "2017-09-06 -1.190543 0.910471 -0.072056 -0.416252\n",
+ "2017-09-05 1.839360 1.148876 1.277954 0.700101"
+ ]
+ },
+ "execution_count": 116,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Sort by value\n",
+ "df.sort_values(by='B')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data Selection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Label based"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2017-09-01 0.230514\n",
+ "2017-09-02 1.466106\n",
+ "2017-09-03 0.858404\n",
+ "2017-09-04 0.268305\n",
+ "2017-09-05 1.839360\n",
+ "2017-09-06 -1.190543\n",
+ "Freq: D, Name: A, dtype: float64"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select column, which returns a series\n",
+ "df['A']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-02 1.466106 0.361433 -0.699672 0.590918\n",
+ "2017-09-03 0.858404 -0.208241 -0.323344 -0.269837\n",
+ "2017-09-04 0.268305 -0.780656 -0.229629 0.778081"
+ ]
+ },
+ "execution_count": 130,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Row based\n",
+ "df[1:4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-02 1.466106 0.361433 -0.699672 0.590918\n",
+ "2017-09-03 0.858404 -0.208241 -0.323344 -0.269837\n",
+ "2017-09-04 0.268305 -0.780656 -0.229629 0.778081"
+ ]
+ },
+ "execution_count": 135,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Or index based\n",
+ "df['20170902':'20170904']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 1.466106\n",
+ "B 0.361433\n",
+ "D -0.699672\n",
+ "C 0.590918\n",
+ "Name: 2017-09-02 00:00:00, dtype: float64"
+ ]
+ },
+ "execution_count": 138,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Cross section using a label\n",
+ "df.loc['2017-09-02']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-01</th>\n",
+ " <td>0.230514</td>\n",
+ " <td>-0.285806</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>0.268305</td>\n",
+ " <td>-0.780656</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.839360</td>\n",
+ " <td>1.148876</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-06</th>\n",
+ " <td>-1.190543</td>\n",
+ " <td>0.910471</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B\n",
+ "2017-09-01 0.230514 -0.285806\n",
+ "2017-09-02 1.466106 0.361433\n",
+ "2017-09-03 0.858404 -0.208241\n",
+ "2017-09-04 0.268305 -0.780656\n",
+ "2017-09-05 1.839360 1.148876\n",
+ "2017-09-06 -1.190543 0.910471"
+ ]
+ },
+ "execution_count": 145,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Multi access selection based on label\n",
+ "df.loc[:,['A','B']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>B</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>0.361433</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>-0.780656</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " B C\n",
+ "2017-09-02 0.361433 0.590918\n",
+ "2017-09-03 -0.208241 -0.269837\n",
+ "2017-09-04 -0.780656 0.778081"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Multi dimension label slicing\n",
+ "df.loc['20170902':'20170904',['B','C']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 1.466106\n",
+ "B 0.361433\n",
+ "Name: 2017-09-02 00:00:00, dtype: float64"
+ ]
+ },
+ "execution_count": 151,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Reduced dimension of return object for single rows\n",
+ "df.loc['20170902',['A','B']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.4661055188464265"
+ ]
+ },
+ "execution_count": 153,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Scalar values\n",
+ "df.loc['20170902','A']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Position based\n",
+ "The semantics follow closely python and numpy slicing. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 0.268305\n",
+ "B -0.780656\n",
+ "D -0.229629\n",
+ "C 0.778081\n",
+ "Name: 2017-09-04 00:00:00, dtype: float64"
+ ]
+ },
+ "execution_count": 157,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Row\n",
+ "df.iloc[3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>-0.229629</td>\n",
+ " <td>0.778081</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.277954</td>\n",
+ " <td>0.700101</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " D C\n",
+ "2017-09-04 -0.229629 0.778081\n",
+ "2017-09-05 1.277954 0.700101"
+ ]
+ },
+ "execution_count": 163,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Multi dimension\n",
+ "df.iloc[3:5,2:4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>1.466106</td>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " <td>0.590918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>0.858404</td>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " <td>-0.269837</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B D C\n",
+ "2017-09-02 1.466106 0.361433 -0.699672 0.590918\n",
+ "2017-09-03 0.858404 -0.208241 -0.323344 -0.269837"
+ ]
+ },
+ "execution_count": 164,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.iloc[1:3,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 165,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style>\n",
+ " .dataframe thead tr:only-child th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>B</th>\n",
+ " <th>D</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-09-01</th>\n",
+ " <td>-0.285806</td>\n",
+ " <td>0.354580</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-02</th>\n",
+ " <td>0.361433</td>\n",
+ " <td>-0.699672</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-03</th>\n",
+ " <td>-0.208241</td>\n",
+ " <td>-0.323344</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-04</th>\n",
+ " <td>-0.780656</td>\n",
+ " <td>-0.229629</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-05</th>\n",
+ " <td>1.148876</td>\n",
+ " <td>1.277954</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-09-06</th>\n",
+ " <td>0.910471</td>\n",
+ " <td>-0.072056</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " B D\n",
+ "2017-09-01 -0.285806 0.354580\n",
+ "2017-09-02 0.361433 -0.699672\n",
+ "2017-09-03 -0.208241 -0.323344\n",
+ "2017-09-04 -0.780656 -0.229629\n",
+ "2017-09-05 1.148876 1.277954\n",
+ "2017-09-06 0.910471 -0.072056"
+ ]
+ },
+ "execution_count": 165,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.iloc[:,1:3]"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 166,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "u'/Users/mark'"
+ "0.36143299164038073"
]
},
- "execution_count": 1,
+ "execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "%pwd"
+ "df.iloc[1,1]"
]
},
{