{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Exploration & Visualization\n",
"### [Data Science Africa Summer School 2016](www.datascienceafrica.org/dsa2016)\n",
"#### Notebook Created by [Ernest Mwebaze](www.air.ug/emwebaze)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"from generatedatah import *\n",
"import seaborn as sns\n",
"sns.set(color_codes=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will look at cleaning sample data from hospital records that show records of child birth that were entered onto sheets of paper and transcribed into an electronic system by interns. The data is a bit messy and hard to analyse as it is. \n",
"\n",
"Note that this is simulated data. We also tie the data to your birthday so please enter your birthday to generate your unique set."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Generate the data\n",
"random_seed = 286\n",
"Gender, Weights, Heights, Birthdays, ChildCondition = generate_birth_data(random_seed)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5148 5148 5148 5148 5148\n"
]
}
],
"source": [
"print len(Gender), len(Weights), len(Heights), len(Birthdays), len(ChildCondition)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"['byo',\n",
" 'gril',\n",
" 'byo',\n",
" 'gir',\n",
" 'gril',\n",
" 'byo',\n",
" 'girl',\n",
" 'gril',\n",
" 'Girl',\n",
" 'gir',\n",
" 'girl',\n",
" 'gril',\n",
" 'byo',\n",
" 'Girl',\n",
" 'gir',\n",
" 'byo',\n",
" 'byo',\n",
" 'gir',\n",
" 'gril',\n",
" 'girl']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gender[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clean Gender data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The attribute Gender has different variations of the words 'girl' and 'boy'. Your role is to write a function that takes in the attribute data Gender and returns cleaned words. The attribute Gender should only have words consisting of ['girl','boy']."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def convert_to_dataframe(Gender, Weights, Heights, Birthdays, ChildCondition):\n",
" # Generate a dataframe from the attributes. \n",
" # Function should return a dataframe\n",
" # HINT: Look through the documentation on how to create pandas dataframes\n",
" # TODO: WRITE YOUR CODE HERE\n",
" birthDict = {'Gender':Gender, 'Weights':Weights, 'Heights':Heights, 'BirthDays':Birthdays, 'Childcondition':ChildCondition}\n",
" birthData = pd.DataFrame(birthDict)\n",
" return birthData"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Checking out your data***\n",
"After loading a dataset it is always good to check it out using techniques from the previous lecture. Common tools are ```describe(), shape, unique()```\n",
"\n",
"For our dataset, run the function to ensure you are loading the data correctly and run the three functions above.\n",
"Questions\n",
"1. >How many records are in the dataset ? Answer = 5148\n",
"2. >What is the mean weight and mean height ? Answer = 19.13, 48.61\n",
"3. >Using unique on each column, which columns need *cleaning* ? Answer = "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" BirthDays | \n",
" Childcondition | \n",
" Gender | \n",
" Heights | \n",
" Weights | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2002-10-22 | \n",
" Healthy, Tall | \n",
" byo | \n",
" 58 | \n",
" 0.2 | \n",
"
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" \n",
" 1 | \n",
" 2003-11-23 | \n",
" Weak, Healthy | \n",
" gril | \n",
" 55 | \n",
" 3.8 | \n",
"
\n",
" \n",
" 2 | \n",
" 2002-06-21 | \n",
" Tall | \n",
" byo | \n",
" 56 | \n",
" 2.8 | \n",
"
\n",
" \n",
" 3 | \n",
" 2003-06-29 | \n",
" Tall | \n",
" gir | \n",
" 49 | \n",
" 2.9 | \n",
"
\n",
" \n",
" 4 | \n",
" 2002-10-09 | \n",
" Weak, Tall, Hairy | \n",
" gril | \n",
" 49 | \n",
" 4.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" BirthDays Childcondition Gender Heights Weights\n",
"0 2002-10-22 Healthy, Tall byo 58 0.2\n",
"1 2003-11-23 Weak, Healthy gril 55 3.8\n",
"2 2002-06-21 Tall byo 56 2.8\n",
"3 2003-06-29 Tall gir 49 2.9\n",
"4 2002-10-09 Weak, Tall, Hairy gril 49 4.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run your function to create the dataset\n",
"birthData = convert_to_dataframe(Gender, Weights, Heights, Birthdays, ChildCondition)\n",
"# birthData.head()\n",
"birthData.to_csv('birthdatacsv.csv', delimiter=',')\n",
"birthData.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5148, 5)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run shape, describe on the dataset\n",
"birthData.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Heights | \n",
" Weights | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 5148.000000 | \n",
" 5148.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 48.613636 | \n",
" 19.128613 | \n",
"
\n",
" \n",
" std | \n",
" 6.364251 | \n",
" 32.747978 | \n",
"
\n",
" \n",
" min | \n",
" 38.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 43.000000 | \n",
" 1.750000 | \n",
"
\n",
" \n",
" 50% | \n",
" 49.000000 | \n",
" 3.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 54.000000 | \n",
" 8.325000 | \n",
"
\n",
" \n",
" max | \n",
" 59.000000 | \n",
" 121.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Heights Weights\n",
"count 5148.000000 5148.000000\n",
"mean 48.613636 19.128613\n",
"std 6.364251 32.747978\n",
"min 38.000000 0.000000\n",
"25% 43.000000 1.750000\n",
"50% 49.000000 3.000000\n",
"75% 54.000000 8.325000\n",
"max 59.000000 121.000000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"birthData.describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Healthy, Tall', 'Weak, Healthy', 'Tall', 'Weak, Tall, Hairy'], dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run unique on each column and see which columns need fixing\n",
"birthData['Childcondition'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" BirthDays | \n",
" Childcondition | \n",
" Gender | \n",
" Heights | \n",
" Weights | \n",
"
\n",
" \n",
" \n",
" \n",
" 3 | \n",
" 2003-06-29 | \n",
" Tall | \n",
" gir | \n",
" 49 | \n",
" 2.9 | \n",
"
\n",
" \n",
" 4 | \n",
" 2002-10-09 | \n",
" Weak, Tall, Hairy | \n",
" gril | \n",
" 49 | \n",
" 4.0 | \n",
"
\n",
" \n",
" 7 | \n",
" 2002-08-18 | \n",
" Healthy, Tall | \n",
" gril | \n",
" 41 | \n",
" 3.6 | \n",
"
\n",
" \n",
" 8 | \n",
" 2003-08-03 | \n",
" Weak, Healthy | \n",
" Girl | \n",
" 45 | \n",
" 2.4 | \n",
"
\n",
" \n",
" 15 | \n",
" 2003-06-13 | \n",
" Tall | \n",
" byo | \n",
" 48 | \n",
" 0.9 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" BirthDays Childcondition Gender Heights Weights\n",
"3 2003-06-29 Tall gir 49 2.9\n",
"4 2002-10-09 Weak, Tall, Hairy gril 49 4.0\n",
"7 2002-08-18 Healthy, Tall gril 41 3.6\n",
"8 2003-08-03 Weak, Healthy Girl 45 2.4\n",
"15 2003-06-13 Tall byo 48 0.9"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"birthData[:5]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# OK lets clean out the Gender column\n",
"def remove_invalid_strings(birthData):\n",
" # Remove all rows with empty Gender inputs i.e those with \"\"\n",
" # Alternatively select all rows of the dataset where the Gender inputs are not \"\"\n",
" q = birthData['Gender'] != \"\"\n",
"# q2 = birthData['Heights'] < 50\n",
" birthData = birthData[q]\n",
" return birthData"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Questions\n",
"1. >From analysing the new dataset using shape or some other command, how many bad rows were removed ? Answer = "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4576, 5)\n",
"['byo' 'gril' 'gir' 'girl' 'Girl' 'Gal' 'Boyz' 'boy']\n"
]
}
],
"source": [
"# Test your cleaned out Gender column\n",
"birthData = remove_invalid_strings(birthData)\n",
"print birthData.shape\n",
"print birthData['Gender'].unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The Weights of the kids look weird ! A Kid of 0.5 Kgs ?, Kid of 110 Kgs ? Perhaps we need to clean out this data\n",
"\n",
"# What are the min and max weights in the data ?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Complete these functions to clean the data\n",
"def remove_invalid_lowweights(birthData):\n",
" # Remove rows where weights are below 1.5\n",
" # TODO: WRITE YOUR CODE HERE\n",
" q = birthData['Weights'] >= 1.5\n",
" outbirthData = birthData[q]\n",
" return outbirthData\n",
"\n",
"def remove_invalid_highweights(birthData):\n",
" # Remove rows where weights are above 4.5\n",
" # TODO: WRITE YOUR CODE HERE\n",
" q = birthData['Weights'] <= 4.5\n",
" outbirthdata = birthData[q]\n",
" return outbirthdata"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Heights | \n",
" Weights | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 2427.000000 | \n",
" 2427.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 48.553770 | \n",
" 2.891965 | \n",
"
\n",
" \n",
" std | \n",
" 6.358931 | \n",
" 0.746659 | \n",
"
\n",
" \n",
" min | \n",
" 38.000000 | \n",
" 1.500000 | \n",
"
\n",
" \n",
" 25% | \n",
" 43.000000 | \n",
" 2.300000 | \n",
"
\n",
" \n",
" 50% | \n",
" 48.000000 | \n",
" 2.900000 | \n",
"
\n",
" \n",
" 75% | \n",
" 54.000000 | \n",
" 3.600000 | \n",
"
\n",
" \n",
" max | \n",
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" Heights Weights\n",
"count 2427.000000 2427.000000\n",
"mean 48.553770 2.891965\n",
"std 6.358931 0.746659\n",
"min 38.000000 1.500000\n",
"25% 43.000000 2.300000\n",
"50% 48.000000 2.900000\n",
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"max 59.000000 4.100000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test that the functions work\n",
"birthData = remove_invalid_lowweights(birthData)\n",
"birthData = remove_invalid_highweights(birthData)\n",
"\n",
"# Get max and min weights\n",
"birthData.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Questions\n",
"1. >What are the new min and max weights ? Answer = 1.5, 4.1"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['gril', 'byo', 'gir', 'Girl', 'girl', 'Gal', 'Boyz', 'boy'], dtype=object)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"birthData.Gender.unique()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = [2,3,4,5]\n",
"df = pd.Series(a)\n",
"\n",
"def mul_a(x):\n",
" output = x * 2\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 4\n",
"1 6\n",
"2 8\n",
"3 10\n",
"dtype: int64\n"
]
}
],
"source": [
"b = df.apply(mul_a)\n",
"print b"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def convert_lowercase(x):\n",
" result = x.lower()\n",
" return result\n",
"\n",
"def rename_gender(x):\n",
" result = x\n",
" if x in ['gril','gir','gal']:\n",
" result = 'girl'\n",
" if x in ['byo','boyz']:\n",
" result = 'boy'\n",
" return result\n",
"\n",
"def clean_gender_attribute(birthData):\n",
" # Clean up the gender attribute to remain with gender in ['girl','boy']\n",
" # Plan for execution\n",
" # 1. Convert all the genders to lowercase\n",
" # 2. Look up the 'apply' function for Pandas and see how to use it to replace the dodgy data to correct data\n",
" # TODO: WRITE YOUR CODE HERE\n",
" birthData.Gender = birthData.Gender.apply(convert_lowercase)\n",
" birthData.Gender = birthData.Gender.apply(rename_gender) \n",
" \n",
" return birthData\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Check that your code works and there are only two genders\n",
"birthData = clean_gender_attribute(birthData)\n",
"assert len(birthData.Gender.unique()) == 2\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Do basic analysis \n",
"Now we do some basic analysis with our data. So perhaps the hospital wants to find out how many boys were born in a particular month, or year. What the average weights for girls were vs those for boys"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Just to check - What is the size of the dataset you are using now ?\n",
"birthData.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Calculate basic statistics for the hospital\n",
"# HINT: It may help to generate different datasets for the different genders\n",
"def num_of_girls_boys(birthData):\n",
" # Return number of boys and girls\n",
" # TODO: WRITE YOUR CODE HERE\n",
" return (num_boys, num_girls)\n",
"\n",
"def average_gender_weight(birthData):\n",
" # Return the average weight of the different genders\n",
" # TODO: WRITE YOUR CODE HERE\n",
" return (avg_weight_boys, avg_weight_girls)\n",
"\n",
"def average_gender_height(birthData):\n",
" # Return the average weight of the different genders\n",
" # TODO: WRITE YOUR CODE HERE\n",
" return (avg_height_boys, avg_height_girls)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Dealing with text in data\n",
"Some times you have to deal with text in the dataset. For example we have the condition of the children in this dataset which is textual information.\n",
"\n",
"Pandas provides for doing this kind of analysis with the ```str.contains()``` function that can be applied to a series of a column of a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 False\n",
"1 True\n",
"2 False\n",
"dtype: bool\n"
]
}
],
"source": [
"# Example\n",
"b = ['Juma', 'Jane', 'Tobias']\n",
"dexample = pd.Series(b)\n",
"print dexample.str.contains('Jane')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" BirthDays | \n",
" Childcondition | \n",
" Gender | \n",
" Heights | \n",
" Weights | \n",
"
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" 1 | \n",
" 2003-11-23 | \n",
" Weak, Healthy | \n",
" girl | \n",
" 55 | \n",
" 3.8 | \n",
"
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" 2 | \n",
" 2002-06-21 | \n",
" Tall | \n",
" boy | \n",
" 56 | \n",
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"
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" 3 | \n",
" 2003-06-29 | \n",
" Tall | \n",
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" 49 | \n",
" 2.9 | \n",
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" 4 | \n",
" 2002-10-09 | \n",
" Weak, Tall, Hairy | \n",
" girl | \n",
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" 4.0 | \n",
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" 5 | \n",
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" 59 | \n",
" 4.0 | \n",
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" \n",
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"
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"text/plain": [
" BirthDays Childcondition Gender Heights Weights\n",
"1 2003-11-23 Weak, Healthy girl 55 3.8\n",
"2 2002-06-21 Tall boy 56 2.8\n",
"3 2003-06-29 Tall girl 49 2.9\n",
"4 2002-10-09 Weak, Tall, Hairy girl 49 4.0\n",
"5 2003-07-03 Weak, Healthy boy 59 4.0"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"birthData.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Do some string manipulations on the data\n",
"def num_healthy_kids(birthData):\n",
" # Return the number of healthy kids born \n",
" # Do this by filtering on the childs condition at birth\n",
" # TODO: WRITE YOUR CODE HERE\n",
" return num_kids\n",
"\n",
"def num_tall_kids(birthData):\n",
" # Return the number of tall kids born \n",
" # Do this by filtering on the childs condition at birth\n",
" # TODO: WRITE YOUR CODE HERE\n",
" return num_tall_kids\n",
"\n",
"def num_tall_height_kids(birthData):\n",
" # Return number of tall kids who also have a height of over 40 cm\n",
" # TODO: WRITE YOUR CODE HERE\n",
" return num_tall_kids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Questions\n",
"1. >How healthy kids are there at birth ? Answer = \n",
"2. >Whats the number of tall kids at birth ? Answer = \n",
"3. >Whats the number of tall kids with height > 40 cm ? Answer = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualization\n",
"Visualization of data can be for various purposes. Commonly you want to use visualization for exploration of the data and presentation of the data. Here we will look at exploration. \n",
"\n",
"A key library to use is seaborn, but there are various others. Pandas offers quick ways of visualizing data in dataframes and Series. Underneath it uses matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Lets start by using matplotlib\n",
"def plot_scatter(birthData):\n",
" # Plot a scatter plot of Height vs Weight\n",
" X = birthData['Heights']\n",
" Y = birthData['Weights']\n",
" plt.figure()\n",
" plt.scatter(X,Y)\n",
" plt.title('BirthRate: Height vs Weight')\n",
" plt.xlabel('Heights')\n",
" plt.ylabel('Weights')\n",
" return plt\n",
"\n",
"def plot_bar(birthData):\n",
" # Plot bar charts for the gender\n",
" X = birthData['Heights']\n",
" Y = birthData['Weights']\n",
" plt.figure()\n",
" plt.bar(X,Y)\n",
" plt.title('Weight distribution relative to Height')\n",
" plt.xlabel('Height')\n",
" plt.ylabel('Weight')\n",
" return plt\n",
"\n",
"def plot_hist(birthData):\n",
" # plot histogram\n",
" X = birthData['Heights']\n",
" plt.figure()\n",
" plt.hist(X)\n",
" plt.title('Height Distribution')\n",
" plt.xlabel('Height')\n",
" plt.ylabel('Frequency')\n",
" return plt"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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0WHJvJfdHMmfJ/Vn23nKs2rTXqB09IBEPjhlr6Zwl1yqZL3mHzel0OnVPwozTp4t1T0HJ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\nTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_scatter(birthData)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+fYCkKm+XAQDwsA4dAr2WWYYH/ncvvEtISFBeXp6mTJmi/Px8JSYmutpnzZql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\nYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_hist(birthData)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PPpHP51NxcbEKCgo0aNAgff3rX9eUKVOiPGX0VFRUqLm5WU8++aQ2b94sh8Oh\nefPmhdaqpKREixcvlmVZ8vl8F3zcZyDoaZ3Ypz53yy23qKSkRPn5+Wpvb9fKlSv12muvsU99QU/r\nxD7V1YX+f87+dGF90T7e6x4AAIP1m7vuAQDApSP0AAAYjNADAGAwQg8AgMEIPQAABiP0AAAYjNAD\nAGCw/w/ncLC3FHiVCAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"birthData.Weights.plot(kind='hist')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Using seaborn transforms the plots to much better looking plots :-)**\n",
"\n",
">Import seaborn and re-run the plots"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XYk3hthYJ5pSaOX36NK688koAZ99LuvHGG1FeXq5vdkRERP/H76aU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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.jointplot(x='Heights', y='Weights', data=birthData)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting time-series data\n",
"The index in a Pandas dataframe is a very useful thing to know about. You can convert the indices of a data frame using the index functions."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Childcondition | \n",
" Gender | \n",
" Heights | \n",
" Weights | \n",
"
\n",
" \n",
" BirthDays | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2003-11-23 | \n",
" Weak, Healthy | \n",
" girl | \n",
" 55 | \n",
" 3.8 | \n",
"
\n",
" \n",
" 2002-06-21 | \n",
" Tall | \n",
" boy | \n",
" 56 | \n",
" 2.8 | \n",
"
\n",
" \n",
" 2003-06-29 | \n",
" Tall | \n",
" girl | \n",
" 49 | \n",
" 2.9 | \n",
"
\n",
" \n",
" 2002-10-09 | \n",
" Weak, Tall, Hairy | \n",
" girl | \n",
" 49 | \n",
" 4.0 | \n",
"
\n",
" \n",
" 2003-07-03 | \n",
" Weak, Healthy | \n",
" boy | \n",
" 59 | \n",
" 4.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Childcondition Gender Heights Weights\n",
"BirthDays \n",
"2003-11-23 Weak, Healthy girl 55 3.8\n",
"2002-06-21 Tall boy 56 2.8\n",
"2003-06-29 Tall girl 49 2.9\n",
"2002-10-09 Weak, Tall, Hairy girl 49 4.0\n",
"2003-07-03 Weak, Healthy boy 59 4.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reset the index of the data\n",
"birthData = birthData.set_index('BirthDays')\n",
"birthData.head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Wue/atQsbNmzAX//1X+P888/HZz/72dSS+9hUnF5n0zwwuYBtu6cyT5q8Q10G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\nC8aOHYu2t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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot time series\n",
"#Use groupby to get a count of births per month\n",
"\n",
"d = birthData.reset_index()\n",
"d.BirthDays.value_counts().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions\n",
"1. >Plot a timeseries of the number of boys born in the two year period\n",
"2. >Plot the timeseries of number of girls born in 2003 \n",
"3. >Plot a bar plot of the births per month"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We can now generate data from various time intervals or periods \n",
"# For example quantifying the births by years\n",
"# Type birthData.index? to see all the possible options\n",
"\n",
"yd = birthData[birthData.index.year == 2002]\n",
"yd.Weight.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Statistical Rigor, Machine Learning \n",
"Sometimes during analysis we want to determine how significant our inference are in the data. For this we tend to use statistical tests. \n",
"\n",
"Some questions one may try to answer are for example Is there a difference in the heights of boys and girls born throughout the period ?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.stats\n",
"# Assume normality\n",
"def calculate_statistics(birthData):\n",
" # Assuming normality of the data, use a suitable test to calculate the test statistic using the \n",
" # two-sample t-test (Welch test)\n",
" # and the p-value associated with the statistic. \n",
" # TODO: WRITE YOUR CODE HERE\n",
" test_statistic, p_value = scipy.stats.ttest_ind(birthData.Height[birthData.Gender=='girl'], birthData.Marks[birthData.Gender=='boy'], equal_var=False)\n",
" \n",
" return test_statistic, p_value\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_statistic, pvalue = calculate_statistics(ageData)\n",
"print test_statistic, pvalue"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Not making the normality assumption for the birth data\n",
"def calculate_statistics_nonnormal(ageData):\n",
" # Without assuming normality of the data, use a suitable test to calculate the test statistic\n",
" # and the p-value associated with the statistic. \n",
" # TODO: WRITE YOUR CODE HERE\n",
" test_statistic, p_value = scipy.stats.mannwhitneyu(birthData.Height[birthData.Gender=='girl'], birthData.Marks[birthData.Gender=='boy'])\n",
" \n",
" return test_statistic, p_value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_statistic, pvalue = calculate_statistics_nonnormal(ageData)\n",
"print test_statistic, pvalue"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}