922 lines
18 KiB
Plaintext
922 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"try:\n",
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" import seaborn\n",
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"except ImportError:\n",
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tabular data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"data/titanic.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Starting from reading this dataset, to answering questions about this data in a few lines of code:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**What is the age distribution of the passengers?**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df['Age'].hist()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**How does the survival rate of the passengers differ between sexes?**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df.groupby('Sex')[['Survived']].aggregate(lambda x: x.sum() / len(x))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Or how does it differ between the different classes?**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df.groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Are young people more likely to survive?**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df['Survived'].sum() / df['Survived'].count()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df25 = df[df['Age'] <= 25]\n",
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"df25['Survived'].sum() / len(df25['Survived'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"All the needed functionality for the above examples will be explained throughout this tutorial."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data structures\n",
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"\n",
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"Pandas provides two fundamental data objects, for 1D (``Series``) and 2D data (``DataFrame``)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Series\n",
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"\n",
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"A Series is a basic holder for **one-dimensional labeled data**. It can be created much as a NumPy array is created:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"s = pd.Series([0.1, 0.2, 0.3, 0.4])\n",
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"s"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Attributes of a Series: `index` and `values`\n",
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"\n",
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"The series has a built-in concept of an **index**, which by default is the numbers *0* through *N - 1*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"s.index"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can access the underlying numpy array representation with the `.values` attribute:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"s.values"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can access series values via the index, just like for NumPy arrays:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"s[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Unlike the NumPy array, though, this index can be something other than integers:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"s2 = pd.Series(np.arange(4), index=['a', 'b', 'c', 'd'])\n",
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"s2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"s2['c']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this way, a ``Series`` object can be thought of as similar to an ordered dictionary mapping one typed value to another typed value.\n",
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"\n",
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"In fact, it's possible to construct a series directly from a Python dictionary:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"pop_dict = {'Germany': 81.3, \n",
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" 'Belgium': 11.3, \n",
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" 'France': 64.3, \n",
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" 'United Kingdom': 64.9, \n",
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" 'Netherlands': 16.9}\n",
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"population = pd.Series(pop_dict)\n",
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"population"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can index the populations like a dict as expected:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"population['France']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"but with the power of numpy arrays:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"population * 1000"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## DataFrames: Multi-dimensional Data\n",
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"\n",
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"A DataFrame is a **tablular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n",
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"\n",
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"<img src=\"img/dataframe.png\" width=110%>"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {},
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"source": [
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"One of the most common ways of creating a dataframe is from a dictionary of arrays or lists.\n",
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"\n",
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"Note that in the IPython notebook, the dataframe will display in a rich HTML view:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
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" 'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
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" 'area': [30510, 671308, 357050, 41526, 244820],\n",
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" 'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
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"countries = pd.DataFrame(data)\n",
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"countries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Attributes of the DataFrame\n",
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"\n",
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"A DataFrame has besides a `index` attribute, also a `columns` attribute:"
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]
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},
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|
{
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|
"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"countries.index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"countries.columns"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"To check the data types of the different columns:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"countries.dtypes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"An overview of that information can be given with the `info()` method:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"countries.info()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Also a DataFrame has a `values` attribute, but attention: when you have heterogeneous data, all values will be upcasted:"
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]
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},
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|
{
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"cell_type": "code",
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|
"execution_count": null,
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|
"metadata": {
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|
"collapsed": false
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|
},
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"outputs": [],
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"source": [
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"countries.values"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"If we don't like what the index looks like, we can reset it and set one of our columns:"
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]
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},
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|
{
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|
"cell_type": "code",
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|
"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"countries = countries.set_index('country')\n",
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"countries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To access a Series representing a column in the data, use typical indexing syntax:"
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]
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},
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|
{
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|
"cell_type": "code",
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|
"execution_count": null,
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|
"metadata": {
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|
"collapsed": false
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|
},
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"outputs": [],
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"source": [
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"countries['area']"
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]
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},
|
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{
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|
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Basic operations on Series/Dataframes"
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]
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|
},
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|
{
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|
"cell_type": "markdown",
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|
"metadata": {},
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|
"source": [
|
|
"As you play around with DataFrames, you'll notice that many operations which work on NumPy arrays will also work on dataframes.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# redefining the example objects\n",
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"\n",
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"population = pd.Series({'Germany': 81.3, 'Belgium': 11.3, 'France': 64.3, \n",
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" 'United Kingdom': 64.9, 'Netherlands': 16.9})\n",
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"\n",
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"countries = pd.DataFrame({'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
|
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" 'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
|
|
" 'area': [30510, 671308, 357050, 41526, 244820],\n",
|
|
" 'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Elementwise-operations (like numpy)"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Just like with numpy arrays, many operations are element-wise:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"population / 100"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries['population'] / countries['area']"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Alignment! (unlike numpy)\n",
|
|
"\n",
|
|
"Only, pay attention to **alignment**: operations between series will align on the index: "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"s1 = population[['Belgium', 'France']]\n",
|
|
"s2 = population[['France', 'Germany']]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"s1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"s2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"s1 + s2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Reductions (like numpy)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The average population number:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"population.mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The minimum area:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries['area'].min()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For dataframes, often only the numeric columns are included in the result:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries.median()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Calculate the population numbers relative to Belgium\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Calculate the population density for each country and add this as a new column to the dataframe.\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Some other useful methods"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Sorting the rows of the DataFrame according to the values in a column:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries.sort_values('density', ascending=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"One useful method to use is the ``describe`` method, which computes summary statistics for each column:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The `plot` method can be used to quickly visualize the data in different ways:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"However, for this dataset, it does not say that much:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries['population'].plot(kind='bar')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"You can play with the `kind` keyword: 'line', 'bar', 'hist', 'density', 'area', 'pie', 'scatter', 'hexbin'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Importing and exporting data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"A wide range of input/output formats are natively supported by pandas:\n",
|
|
"\n",
|
|
"* CSV, text\n",
|
|
"* SQL database\n",
|
|
"* Excel\n",
|
|
"* HDF5\n",
|
|
"* json\n",
|
|
"* html\n",
|
|
"* pickle\n",
|
|
"* ..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pd.read"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"states.to"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Other features\n",
|
|
"\n",
|
|
"* Working with missing data (`.dropna()`, `pd.isnull()`)\n",
|
|
"* Merging and joining (`concat`, `join`)\n",
|
|
"* Grouping: `groupby` functionality\n",
|
|
"* Reshaping (`stack`, `pivot`)\n",
|
|
"* Time series manipulation (resampling, timezones, ..)\n",
|
|
"* Easy plotting"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"There are many, many more interesting operations that can be done on Series and DataFrame objects, but rather than continue using this toy data, we'll instead move to a real-world example, and illustrate some of the advanced concepts along the way.\n",
|
|
"\n",
|
|
"See the next notebooks!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"source": [
|
|
"## Acknowledgement\n",
|
|
"\n",
|
|
"> *© 2015, Stijn Van Hoey and Joris Van den Bossche (<mailto:stijnvanhoey@gmail.com>, <mailto:jorisvandenbossche@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
|
|
"\n",
|
|
"> This notebook is partly based on material of Jake Vanderplas (https://github.com/jakevdp/OsloWorkshop2014).\n",
|
|
"\n",
|
|
"---"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|