

In [305]:

    
import pandas as pd
import numpy as np
%matplotlib inline

Math + `pandas` = 😍

Since y'all love math, we're going to do plenty of both today.

Flavors of statistics

One of the most basic ways to split statistics is to break it into two categories: descriptive and inferential.

Inferential statistics takes part of a population and attempts to infer something about the entire population. For example, I interview 100 likely voters about who they're going to vote for, and infer who is going to win an election.
Descriptive statistics describes only the numbers you have right in front of you. For example, I have a list of all the planes that took off from the airport yesterday, and they were on average ten minutes late.

We're going to be doing some basic descriptive statistics, because we sure aren't going to release our entire dataset to our readers. Summing it all up into a few numbers works much more nicely.

Types of data

The two major categories of data are qualitative/categorical and quantitative/numerical. I'll use both words to describe each because I'm incapcable of picking a term and stick to it.

Qualitative or categorical data is... well, categories. Things that aren't numbers. Whether you're married or single, live in Arkansas or Alabama, or have blue eyes or brown eyes.
Quantitative or numerical data is, obviously, based on numbers.

Kinds of numeric data

And, lucky us, there are kinds of numeric data!

Continuous data can be broken down into smaller and smaller numerical pieces. For example, is the temperature in your apartment 69°F, or 69.4°F, or 69.123°F?
Discrete data are still numbers, but they can only have certain values. For example, Yelp ratings are 1, 2, 3, 4 or 5 stars.

Your good friend descriptive statistics

You use descriptive statistics all the time! Averages! Maximums! Minimums! These old friends are your new friends, too.

We can break down descriptive statistics into a few major concepts, we'll talk about central tendency and variability. Let's take a look at those with a really dumb sample data set.



In [306]:

    
# Let's build a data set
df = pd.DataFrame([
 { 'name': 'Smushface', 'salary': 1200 } ,
 { 'name': 'Jen', 'salary': 25000 },
 { 'name': 'James', 'salary': 55000 },
 { 'name': 'John', 'salary': 35000 },
 { 'name': 'Josephine', 'salary': 25000 },
 { 'name': 'Jacques', 'salary': 15000 },
 { 'name': 'Bill Gates', 'salary': 100000 }    
])
df

Central Tendency

If someone hears we have this data set about salaries, they're probably going to ask, "how much do people make?" They don't want a long list of numbers, they want a single, solitary number. We can get most of the way there by describing the central tendency.

Data in the world tends to clump around certain numbers - the average height of a man, or the average score on a test. This is called the central tendency, and is usually just called the average. Luckily for us average has like two hundred different meanings: mean, median, and mode.

Double-luckily for us, pandas can compute all of those for us with appropriately-named functions.

The MEAN

The mean is what we've always thought of as the average. You add up all the data points and divide by the number of data points.



In [307]:

    
(0 + 25000 + 55000 + 35000 + 25000 + 80000 + 20000000) / 7









    Out[307]:





2888571.4285714286

But like I said, pandas can help us out here with the .mean() method.



In [308]:

    
df['salary'].mean()









    Out[308]:





36600.0

That looks ugly, let's convert it to an integer!



In [309]:

    
df['salary'].mean().astype(int)









    Out[309]:





36600

If we want to get real crazy, we can add commas to it using our old friend .format()



In [310]:

    
# ***format trick stolen from http://stackoverflow.com/a/10742904
mean_salary = df['salary'].mean().astype(int)
"{:,}".format(mean_salary)









    Out[310]:





'36,600'

But back to the mean: apparently the average of all of these salaries is over two million dollars. Does that look right to you?



In [311]:

    
df

The problem with adding everything together is Bill Gates is exerting undue influence. His salary is an outlier - a number that's either way too high or way too low and kind of screws up our data. He might actually be making that much money, sure, but by taking the mean we aren't doing a good job describing what we'd think of as the "average."

Because of how it's calculated, the mean is suseptible to outliers. Because you need to be so careful with it, the mean is definitely not my favorite way of getting the average.

The MEDIAN

The median is like a new, improved mean, in that it describes the central tendency without being suseptible to outliers. To compute the median you do two things:

Order the numbers largest to smallest
Pick the middle number



In [312]:

    
df['salary'].sort_values()









    Out[312]:





0      1200
5     15000
1     25000
4     25000
3     35000
2     55000
6    100000
Name: salary, dtype: int64

We have seven values, so it will be number four. Count up the list to discover it: 35,000 is the median. I'll prove it, too, using the power of pandas.



In [313]:

    
df['salary'].median()









    Out[313]:





25000.0

See? Told you!

If you happen to have an even number of data points you won't have a middle number, you'll take the mean of the middle two numbers.

My favorite description of the median comes from Statistics for the Terrified

We are all much more familiar with the mean - why? People like using the mean because it is a much easier thing to deal with than the median, mathematically, particularly in more complex situations... ... Always use the median when the distribution is skewed. You can use either the mean or the median when the population is symmetrical, because then they will give almost identical results.

Which to me reads like "if you have a computer, use the median."

The MODE

The mode is the least-used measurement of central tendency: it's the most popular value. Even though our salary dataset has a most popular value, the mode actually shouldn't be used with continuous data, you should only use it with discrete data.

Let's say our buddies are reviewing a restaurant



In [314]:

    
import pandas as pd

# Let's build a data set
reviews_df = pd.DataFrame([
 { 'restaurant': 'Burger King', 'reviewer': 'Smushface', 'yelp_stars': 2 } ,
 { 'restaurant': 'Burger King', 'reviewer': 'Jen', 'yelp_stars': 2 },
 { 'restaurant': 'Burger King', 'reviewer': 'James', 'yelp_stars': 5 },
 { 'restaurant': 'Burger King', 'reviewer': 'John', 'yelp_stars': 4 },
 { 'restaurant': 'Burger King', 'reviewer': 'Josephine', 'yelp_stars': 4 },
 { 'restaurant': 'Burger King', 'reviewer': 'Jacques', 'yelp_stars': 3 },
 { 'restaurant': 'Burger King', 'reviewer': 'Bill Gates', 'yelp_stars': 2 }    
])
reviews_df









    Out[314]:






  
    
      
      restaurant
      reviewer
      yelp_stars
    
  
  
    
      0
      Burger King
      Smushface
      2
    
    
      1
      Burger King
      Jen
      2
    
    
      2
      Burger King
      James
      5
    
    
      3
      Burger King
      John
      4
    
    
      4
      Burger King
      Josephine
      4
    
    
      5
      Burger King
      Jacques
      3
    
    
      6
      Burger King
      Bill Gates
      2



In [315]:

    
reviews_df['yelp_stars'].mode()









    Out[315]:





0    2
dtype: int64

Despite the fact that most people gave Burger King a 3 or above, the fact that the most popular score is 2 might mean something.

My favorite example of the mode being useful (and possibly only example of the mode being useful) is Amazon reviews. For example, this charger for a MacBook has some... interesting reviews.



In [316]:

    
# Import this because it won't let us display images otherwise
from IPython.display import display, HTML



In [317]:

    
display(HTML('''<img 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">'''))

Look at that adapter!

2.5 stars, not too shabby. And so cheap! The real ones are like 80 bucks, I think.

Let's take a look at the actual distribution of the scores...



In [318]:

    
display(HTML('''<img 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">'''))

Oh wait, the mode of the data is 1. The adapters are probably terrible, cancel that order.

Measures of central tendency, a review

There are three measures of central tendency.

The mean is the sum of all the numbers divided by the count of the numbers, and is pulled towards outliers.
The median is the middle number, and is not affected by outliers.
The mode is the most frequent number, is only used with nominal data, and isn't terribly useful.

The median should probably be favorite.

Slight detour: Making sense of `.describe()`

One of our old pandas friends is .describe(), which... describes some math-y stuff about a data set.



In [319]:

    
df.head()



In [320]:

    
# Make it so we only have three decimal places
pd.set_option('display.float_format', lambda x: '%.3f' % x)
df.describe()

And you see the mean displayed in all its glory, and so you scream, it doesn't include the median what garbage!!!

But it does! Really! Give it a look around and see if you can find it.

waiting!

Yes, that's right - 50% is the median. Half of the values are above, half are below. The 25% and 75% are similar meaures:

25%, a.k.a. Q1, a.k.a. the first quartile, has 25% of the values below it and 75% above it.
75%, a.k.a. Q3, a.k.a. the third quartile, has 75% of the values below it and 25% below it.
and, of course, 50% i also known as Q2.

25% can be thought of as the median of the bottom half of the data, and 75% can describe the median of the top half of the data. They give you a sense of the range of data.

Box-and-whisker plots

If we get tired of looking at lists of numbers, there's always box-and-whisker plots. They're the visual version of .describe() - they describe the minimum, Q1, median, Q3, and maximum.

Well, usually.



In [321]:

    
df.boxplot(column='salary', sym='o', return_type='axes')









    Out[321]:





<matplotlib.axes._subplots.AxesSubplot at 0x1115cc128>

The box part is 25%-75%, with the red line being the median. The whiskers are usually the maximum and minimum, but matplotlib/pandas likes to display it as "oh this is where nice values live" (a.k.a. IQR 1.5). We can make it do max and min by passing whis='range' when we make the box plot.



In [322]:

    
df.boxplot(column='salary', sym='o', whis='range', return_type='axes')









    Out[322]:





<matplotlib.axes._subplots.AxesSubplot at 0x1118356a0>

It might look a little bit better if we pull in an actual data set. Let's use those billionaires we worked on before.



In [323]:

    
rich_df = pd.read_excel("rich_people.xlsx")
rich_df = rich_df[rich_df['year'] == 2014]
rich_df.head(2)









    Out[323]:






  
    
      
      year
      name
      rank
      citizenship
      countrycode
      networthusbillion
      selfmade
      typeofwealth
      gender
      age
      ...
      relationshiptocompany
      foundingdate
      gdpcurrentus
      sourceofwealth
      notes
      notes2
      source
      source_2
      source_3
      source_4
    
  
  
    
      1
      2014
      A. Jerrold Perenchio
      663
      United States
      USA
      2.600
      self-made
      executive
      male
      83.000
      ...
      former chairman and CEO
      1955.000
      nan
      television, Univision
      represented Marlon Brando and Elizabeth Taylor
      NaN
      http://en.wikipedia.org/wiki/Jerry_Perenchio
      http://www.forbes.com/profile/a-jerrold-perenc...
      COLUMN ONE; A Hollywood Player Who Owns the Ga...
      NaN
    
    
      5
      2014
      Abdulla Al Futtaim
      687
      United Arab Emirates
      ARE
      2.500
      inherited
      inherited
      male
      nan
      ...
      relation
      1930.000
      nan
      auto dealers, investments
      company split between him and cousin in 2000
      NaN
      http://en.wikipedia.org/wiki/Al-Futtaim_Group
      http://www.al-futtaim.ae/content/groupProfile.asp
      NaN
      NaN
    
  

2 rows × 30 columns



In [324]:

    
rich_df = rich_df.dropna(subset=['age', 'networthusbillion', 'foundingdate'])
rich_df.describe()









    



/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/numpy/lib/function_base.py:3823: RuntimeWarning: Invalid value encountered in percentile
  RuntimeWarning)






    Out[324]:






  
    
      
      year
      rank
      networthusbillion
      age
      north
      politicalconnection
      founder
      foundingdate
      gdpcurrentus
    
  
  
    
      count
      1578.000
      1578.000
      1578.000
      1578.000
      1578.000
      54.000
      1578.000
      1578.000
      0.000
    
    
      mean
      2014.000
      805.160
      3.958
      63.393
      0.577
      1.000
      0.519
      1963.465
      nan
    
    
      std
      0.000
      463.300
      5.857
      13.154
      0.494
      0.000
      0.500
      38.329
      nan
    
    
      min
      2014.000
      1.000
      1.000
      24.000
      0.000
      1.000
      0.000
      1615.000
      nan
    
    
      25%
      2014.000
      408.000
      1.400
      53.000
      0.000
      nan
      0.000
      1948.000
      nan
    
    
      50%
      2014.000
      796.000
      2.200
      63.000
      1.000
      nan
      1.000
      1973.000
      nan
    
    
      75%
      2014.000
      1210.000
      3.700
      73.000
      1.000
      nan
      1.000
      1991.000
      nan
    
    
      max
      2014.000
      1565.000
      76.000
      98.000
      1.000
      1.000
      1.000
      2012.000
      nan



In [325]:

    
rich_df.boxplot(column='networthusbillion', whis='range', return_type='axes')









    Out[325]:





<matplotlib.axes._subplots.AxesSubplot at 0x111c0c940>



In [326]:

    
rich_df.boxplot(column='age', whis='range', return_type='axes')









    Out[326]:





<matplotlib.axes._subplots.AxesSubplot at 0x111c2e080>

Officializing the spread

I'm going to steal a set of numbers from Khan Academy. Let's say we have two very boring sets of numbers.



In [327]:

    
list_one = pd.Series([-10, 0, 10, 20, 30])
list_two = pd.Series([8, 9, 10, 11, 12])
print("list one is")
print(list_one)
print("list two is")
print(list_two)









    



list one is
0   -10
1     0
2    10
3    20
4    30
dtype: int64
list two is
0     8
1     9
2    10
3    11
4    12
dtype: int64

Let's use their central tendencies to describe them.



In [328]:

    
print("The mean of list_one is", list_one.mean())
print("The mean of list_two is", list_one.mean())
print("The median of list_one is", list_one.median())
print("The median of list_two is", list_one.median())









    



The mean of list_one is 10.0
The mean of list_two is 10.0
The median of list_one is 10.0
The median of list_two is 10.0

Huh! But I mean, let's be honest: THESE LISTS OF NUMBERS ARE VERY DIFFERENT. If their central tendencies are the same, the way to describe them, then, is to talk about the spread, or how the actual numbers themselves are distributed.

So we learned about the range before, it's the difference between the smallest and largest number.

For [-10, 0, 10, 20, 30], the range is 40. It's much more dispersed.
For [8, 9, 10, 11, 12], the range is 4. It's much tighter.

That's helpful! But there are more ways to measure the spread than just range.

Measures of spread

Along with range, there are two other things we need to learn about how these numbers are distributed: variance and standard deviation.

Range is the difference between the largest and smallest number
Variance is difference between each data point and the mean, squared. And then you take the mean of that. What Yeah, I know, we'll break it down in a second.
Standard deviation is the square root of the variance.

Variance

Each data point, subtracted from the mean, squared, and then you add all that together. It looks like this:



In [329]:

    
# Data points [-10, 0, 10, 20, 30]
# Mean: 10
((-10 - 10)**2 + (0 - 10)**2 + (10 - 10)**2 + (20 - 10)**2 + (30 - 10)**2) / 5









    Out[329]:





200.0



In [330]:

    
# Data points [8, 9, 10, 11, 12]
# Mean: 10
((8 - 10)**2 + (9 - 10)**2 + (10 - 10)**2 + (11 - 10)**2 + (12 - 10)**2) / 5









    Out[330]:





2.0



In [331]:

    
# And pandas agrees
# Please don't ask why ddof=0 it has to do with sample variance
print(list_one.var(ddof=0))
print(list_two.var(ddof=0))

So first, the first data set has a much higher variance than the first variance.

Standard deviation



In [332]:

    
import math



In [333]:

    
# Data points [-10, 0, 10, 20, 30]
# Variance: 200
math.sqrt(200)









    Out[333]:





14.142135623730951



In [334]:

    
# Data points [8, 9, 10, 11, 12]
# Variance: 2.0
math.sqrt(2)









    Out[334]:





1.4142135623730951

The guy on Khan Academy is like "Yeah! The first data set has ten times the standard deviation than the second data set!" which he is really excited about. Since the standard deviation is 10x larger, think about it as "generally, a data point in the first data set is 10x further from the mean than in the second data set."

Measures of spread recap

Range is easy. Variance and standard deviation are a little tougher - think of them as measurements of how far away from the mean your data generally is. High variance/standard deviation = numbers are generally spread out. Small variance/standard deviation = numbers are generally closer to the mean.

Finding outliers

Standard deviation is helpful because it describes how far away from the mean your data generally is. We can use this to find data points that are usually far from the mean. These are outliers!



In [335]:

    
rich_df.sort_values(by='age', ascending=False).head(3)









    Out[335]:






  
    
      
      year
      name
      rank
      citizenship
      countrycode
      networthusbillion
      selfmade
      typeofwealth
      gender
      age
      ...
      relationshiptocompany
      foundingdate
      gdpcurrentus
      sourceofwealth
      notes
      notes2
      source
      source_2
      source_3
      source_4
    
  
  
    
      516
      2014
      David Rockefeller, Sr.
      580
      United States
      USA
      2.900
      inherited
      inherited
      male
      98.000
      ...
      relation
      1870.000
      nan
      oil, banking
      family made most of fortune in the late 19th a...
      NaN
      http://en.wikipedia.org/wiki/David_Rockefeller
      http://en.wikipedia.org/wiki/Standard_Oil
      http://en.wikipedia.org/wiki/Rockefeller_family
      NaN
    
    
      1277
      2014
      Karl Wlaschek
      305
      Austria
      AUT
      4.800
      self-made
      founder non-finance
      male
      96.000
      ...
      founder
      1953.000
      nan
      retail
      NaN
      NaN
      http://en.wikipedia.org/wiki/BILLA
      http://en.wikipedia.org/wiki/Karl_Wlaschek
      https://www.billa.at/Footer_Nav_Seiten/Geschic...
      NaN
    
    
      1328
      2014
      Kirk Kerkorian
      328
      United States
      USA
      4.500
      self-made
      self-made finance
      male
      96.000
      ...
      investor
      1924.000
      nan
      casinos, investments
      purchased in 1969
      NaN
      http://en.wikipedia.org/wiki/Kirk_Kerkorian
      http://www.forbes.com/profile/kirk-kerkorian/
      PROFILE: Las Vegas billionaire amassed his wea...
      NaN
    
  

3 rows × 30 columns

How strange is it that those old rich people are so old? We can see how many standard deviations they are away from the mean.



In [336]:

    
rich_df['age_std'] = ((rich_df['age'] - rich_df['age'].mean()).apply(abs) / rich_df['age'].std())



In [337]:

    
rich_df.sort_values(by='age', ascending=False).head(3)









    Out[337]:






  
    
      
      year
      name
      rank
      citizenship
      countrycode
      networthusbillion
      selfmade
      typeofwealth
      gender
      age
      ...
      foundingdate
      gdpcurrentus
      sourceofwealth
      notes
      notes2
      source
      source_2
      source_3
      source_4
      age_std
    
  
  
    
      516
      2014
      David Rockefeller, Sr.
      580
      United States
      USA
      2.900
      inherited
      inherited
      male
      98.000
      ...
      1870.000
      nan
      oil, banking
      family made most of fortune in the late 19th a...
      NaN
      http://en.wikipedia.org/wiki/David_Rockefeller
      http://en.wikipedia.org/wiki/Standard_Oil
      http://en.wikipedia.org/wiki/Rockefeller_family
      NaN
      2.631
    
    
      1277
      2014
      Karl Wlaschek
      305
      Austria
      AUT
      4.800
      self-made
      founder non-finance
      male
      96.000
      ...
      1953.000
      nan
      retail
      NaN
      NaN
      http://en.wikipedia.org/wiki/BILLA
      http://en.wikipedia.org/wiki/Karl_Wlaschek
      https://www.billa.at/Footer_Nav_Seiten/Geschic...
      NaN
      2.479
    
    
      1328
      2014
      Kirk Kerkorian
      328
      United States
      USA
      4.500
      self-made
      self-made finance
      male
      96.000
      ...
      1924.000
      nan
      casinos, investments
      purchased in 1969
      NaN
      http://en.wikipedia.org/wiki/Kirk_Kerkorian
      http://www.forbes.com/profile/kirk-kerkorian/
      PROFILE: Las Vegas billionaire amassed his wea...
      NaN
      2.479
    
  

3 rows × 31 columns

They are thirteen standard deviations away from the mean. Generally, 3.0 is considered a crazy outlier. 1.5 is considered maybe an outlier, but probably not really. So no one's looking crazy old here.

What about in terms of wealth?



In [338]:

    
rich_df['age'].plot(kind='box')









    Out[338]:





<matplotlib.axes._subplots.AxesSubplot at 0x111c20748>



In [339]:

    
rich_df['wealth_std'] = ((rich_df['networthusbillion'] - rich_df['networthusbillion'].mean()).apply(abs) / rich_df['networthusbillion'].std())



In [340]:

    
rich_df.sort_values(by='wealth_std', ascending=False).head(3)









    Out[340]:






  
    
      
      year
      name
      rank
      citizenship
      countrycode
      networthusbillion
      selfmade
      typeofwealth
      gender
      age
      ...
      gdpcurrentus
      sourceofwealth
      notes
      notes2
      source
      source_2
      source_3
      source_4
      age_std
      wealth_std
    
  
  
    
      284
      2014
      Bill Gates
      1
      United States
      USA
      76.000
      self-made
      founder non-finance
      male
      58.000
      ...
      nan
      Microsoft
      NaN
      NaN
      http://www.forbes.com/profile/bill-gates/
      NaN
      NaN
      NaN
      0.410
      12.301
    
    
      348
      2014
      Carlos Slim Helu
      2
      Mexico
      MEX
      72.000
      self-made
      privatized and resources
      male
      74.000
      ...
      nan
      telecom
      NaN
      NaN
      http://www.ozy.com/provocateurs/carlos-slims-w...
      NaN
      NaN
      NaN
      0.806
      11.618
    
    
      124
      2014
      Amancio Ortega
      3
      Spain
      ESP
      64.000
      self-made
      founder non-finance
      male
      77.000
      ...
      nan
      retail
      NaN
      NaN
      http://www.forbes.com/profile/amancio-ortega/
      NaN
      NaN
      NaN
      1.034
      10.252
    
  

3 rows × 32 columns

Hey, look at that! They're crazy wealthy! It's OUT OF CONTROL! THEY ARE SO WEALTHY. UNBELIEVABLE!!!!

We could also know that by looking at a simple histogram, of course.



In [341]:

    
rich_df['networthusbillion'].hist()









    Out[341]:





<matplotlib.axes._subplots.AxesSubplot at 0x11203ab00>



In [342]:

    
rich_df['networthusbillion'].hist(bins=25)









    Out[342]:





<matplotlib.axes._subplots.AxesSubplot at 0x111e6fb70>



In [343]:

    
rich_df['networthusbillion'].hist(bins=50)









    Out[343]:





<matplotlib.axes._subplots.AxesSubplot at 0x1123cf7b8>



In [344]:

    
rich_df['networthusbillion'].describe()









    Out[344]:





count   1578.000
mean       3.958
std        5.857
min        1.000
25%        1.400
50%        2.200
75%        3.700
max       76.000
Name: networthusbillion, dtype: float64

Even though we have a ton of billionaires, basically everyone is a baby billionaire with barely billions of dollars. This is skewed data. Compare it with a histograph of age.



In [345]:

    
rich_df['networthusbillion'].hist(bins=25)









    Out[345]:





<matplotlib.axes._subplots.AxesSubplot at 0x1127177f0>



In [346]:

    
rich_df['age'].hist(bins=25)









    Out[346]:





<matplotlib.axes._subplots.AxesSubplot at 0x1127ade10>

Mostly billionaires are around 60, but sometimes they're younger or older. This is something you can use simple summary statistics on. The net worth data is skewed, and attempting to use your normal boring statistics on it is a terrible mistake.

The age data is called a normal distribution. It's nice, it's pleasant, it's normal. You can do normal things with it, like look for outliers. Let's read in some NBA data and look for outliers.



In [347]:

    
nba_df = pd.read_csv("nba.csv")



In [348]:

    
nba_df['WT'].describe()









    Out[348]:





count   528.000
mean    221.206
std      27.943
min      20.000
25%     200.000
50%     220.000
75%     240.000
max     290.000
Name: WT, dtype: float64



In [349]:

    
nba_df['WT'].hist()









    Out[349]:





<matplotlib.axes._subplots.AxesSubplot at 0x112c2d710>

That mostly looks clustered around one value. But what's that weird one? Let's grab a standard deviation for every point.



In [350]:

    
nba_df['wt_std'] = ((nba_df['WT'] - nba_df['WT'].mean()).apply(abs) / nba_df['WT'].std())
nba_df.sort_values(by='wt_std', ascending=False).head(5)









    Out[350]:






  
    
      
      Name
      Age
      Team
      POS
      #
      2013 $
      Ht (In.)
      WT
      EXP
      1st Year
      DOB
      School
      City
      State (Province, Territory, Etc..)
      Country
      Race
      HS Only
      wt_std
    
  
  
    
      40
      Taylor, Jermaine
      26
      Cavaliers
      G
      8
      $780,871
      77
      20
      4
      2009
      12/8/1986
      Central Florida
      Tavares, FL
      Florida
      US
      Black
      No
      7.201
    
    
      136
      Brown, Kwame
      31
      76ers
      C
      54
      $2,945,901
      83
      290
      12
      2001
      3/10/1982
      Glynn Adademy (GA)
      Charleston, SC
      South Carolina
      US
      Black
      Yes
      2.462
    
    
      315
      Pekovi?, Nikola
      27
      Timberwolves
      C
      14
      $12,000,000
      83
      290
      3
      2010
      1/3/1986
      n/a
      Bijelo Polje
      n/a
      Yugoslavia
      White
      No
      2.462
    
    
      374
      Jefferson, Al
      28
      Bobcats
      F/C
      25
      $13,500,000
      82
      289
      9
      2004
      1/4/1985
      Prentiss HS (MS)
      Monticello, MS
      Mississippi
      US
      Black
      Yes
      2.426
    
    
      207
      Davis, Glen
      27
      Magic
      F/C
      11
      $6,400,000
      81
      289
      6
      2007
      1/1/1986
      LSU
      Baton Rouge, LA
      Louisiana
      US
      Black
      No
      2.426

Jermaine Taylor has a weight that's 7 standard deviations away, which means we should probably look at it.

Oh look, he weighs 20 pounds. Does he actually weigh 20 pounds? We could do some research, but I'm thinking he doesn't.

How about we get rid of everyone that's a bad outlier? We only have one guy so far, but we might as well.



In [351]:

    
# Only keep people with a standard deviation of less than three
cleaned_nba_df = nba_df[nba_df['wt_std'] < 3]



In [352]:

    
cleaned_nba_df.sort_values(by='wt_std', ascending=False).head(3)









    Out[352]:






  
    
      
      Name
      Age
      Team
      POS
      #
      2013 $
      Ht (In.)
      WT
      EXP
      1st Year
      DOB
      School
      City
      State (Province, Territory, Etc..)
      Country
      Race
      HS Only
      wt_std
    
  
  
    
      315
      Pekovi?, Nikola
      27
      Timberwolves
      C
      14
      $12,000,000
      83
      290
      3
      2010
      1/3/1986
      n/a
      Bijelo Polje
      n/a
      Yugoslavia
      White
      No
      2.462
    
    
      136
      Brown, Kwame
      31
      76ers
      C
      54
      $2,945,901
      83
      290
      12
      2001
      3/10/1982
      Glynn Adademy (GA)
      Charleston, SC
      South Carolina
      US
      Black
      Yes
      2.462
    
    
      374
      Jefferson, Al
      28
      Bobcats
      F/C
      25
      $13,500,000
      82
      289
      9
      2004
      1/4/1985
      Prentiss HS (MS)
      Monticello, MS
      Mississippi
      US
      Black
      Yes
      2.426

Now that we got rid of some people, we can also recalculate the standard deviation! Remember, standard deviation is a relationship to the mean, and outliers move the mean.



In [353]:

    
cleaned_nba_df['new_wt_std'] = ((cleaned_nba_df['WT'] - cleaned_nba_df['WT'].mean()).apply(abs) / cleaned_nba_df['WT'].std())
cleaned_nba_df.sort_values(by='new_wt_std', ascending=False).head(5)









    



/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':






    Out[353]:






  
    
      
      Name
      Age
      Team
      POS
      #
      2013 $
      Ht (In.)
      WT
      EXP
      1st Year
      DOB
      School
      City
      State (Province, Territory, Etc..)
      Country
      Race
      HS Only
      wt_std
      new_wt_std
    
  
  
    
      315
      Pekovi?, Nikola
      27
      Timberwolves
      C
      14
      $12,000,000
      83
      290
      3
      2010
      1/3/1986
      n/a
      Bijelo Polje
      n/a
      Yugoslavia
      White
      No
      2.462
      2.576
    
    
      136
      Brown, Kwame
      31
      76ers
      C
      54
      $2,945,901
      83
      290
      12
      2001
      3/10/1982
      Glynn Adademy (GA)
      Charleston, SC
      South Carolina
      US
      Black
      Yes
      2.462
      2.576
    
    
      207
      Davis, Glen
      27
      Magic
      F/C
      11
      $6,400,000
      81
      289
      6
      2007
      1/1/1986
      LSU
      Baton Rouge, LA
      Louisiana
      US
      Black
      No
      2.426
      2.539
    
    
      374
      Jefferson, Al
      28
      Bobcats
      F/C
      25
      $13,500,000
      82
      289
      9
      2004
      1/4/1985
      Prentiss HS (MS)
      Monticello, MS
      Mississippi
      US
      Black
      Yes
      2.426
      2.539
    
    
      205
      Hamilton, Justin
      23
      Heat
      C
      7
      $490,180
      84
      155
      0
      2013
      4/1/1990
      LSU
      Newport Beach, CA
      California
      US
      Black
      No
      2.369
      2.508

So now it's a little bit crazier to be so far from the mean, but generally things are all legitimate and pleasant.



In [354]:

    
# Histogram of weights with uncleaned data
nba_df['WT'].hist()









    Out[354]:





<matplotlib.axes._subplots.AxesSubplot at 0x1129e1278>



In [355]:

    
# Histogram of weights with cleaned data
cleaned_nba_df['WT'].hist()









    Out[355]:





<matplotlib.axes._subplots.AxesSubplot at 0x11292b470>



In [356]:

    
# Box-and-whisker plot of weights with uncleaned data
nba_df['WT'].plot(kind='box', whis='range')









    Out[356]:





<matplotlib.axes._subplots.AxesSubplot at 0x112c58d68>



In [357]:

    
# Box-and-whisker plot of weights with cleaned data
cleaned_nba_df['WT'].plot(kind='box', whis='range')









    Out[357]:





<matplotlib.axes._subplots.AxesSubplot at 0x112faf080>

See how pleasant that is? So pleasant.



In [ ]:

	name	salary
0	Smushface	1200
1	Jen	25000
2	James	55000
3	John	35000
4	Josephine	25000
5	Jacques	15000
6	Bill Gates	100000

	restaurant	reviewer	yelp_stars
0	Burger King	Smushface	2
1	Burger King	Jen	2
2	Burger King	James	5
3	Burger King	John	4
4	Burger King	Josephine	4
5	Burger King	Jacques	3
6	Burger King	Bill Gates	2

	salary
count	7.000
mean	36600.000
std	32530.806
min	1200.000
25%	20000.000
50%	25000.000
75%	45000.000
max	100000.000

	year	name	rank	citizenship	countrycode	networthusbillion	selfmade	typeofwealth	gender	age	...	relationshiptocompany	foundingdate	gdpcurrentus	sourceofwealth	notes	notes2	source	source_2	source_3	source_4
1	2014	A. Jerrold Perenchio	663	United States	USA	2.600	self-made	executive	male	83.000	...	former chairman and CEO	1955.000	nan	television, Univision	represented Marlon Brando and Elizabeth Taylor	NaN	http://en.wikipedia.org/wiki/Jerry_Perenchio	http://www.forbes.com/profile/a-jerrold-perenc...	COLUMN ONE; A Hollywood Player Who Owns the Ga...	NaN
5	2014	Abdulla Al Futtaim	687	United Arab Emirates	ARE	2.500	inherited	inherited	male	nan	...	relation	1930.000	nan	auto dealers, investments	company split between him and cousin in 2000	NaN	http://en.wikipedia.org/wiki/Al-Futtaim_Group	http://www.al-futtaim.ae/content/groupProfile.asp	NaN	NaN

	year	rank	networthusbillion	age	north	politicalconnection	founder	foundingdate	gdpcurrentus
count	1578.000	1578.000	1578.000	1578.000	1578.000	54.000	1578.000	1578.000	0.000
mean	2014.000	805.160	3.958	63.393	0.577	1.000	0.519	1963.465	nan
std	0.000	463.300	5.857	13.154	0.494	0.000	0.500	38.329	nan
min	2014.000	1.000	1.000	24.000	0.000	1.000	0.000	1615.000	nan
25%	2014.000	408.000	1.400	53.000	0.000	nan	0.000	1948.000	nan
50%	2014.000	796.000	2.200	63.000	1.000	nan	1.000	1973.000	nan
75%	2014.000	1210.000	3.700	73.000	1.000	nan	1.000	1991.000	nan
max	2014.000	1565.000	76.000	98.000	1.000	1.000	1.000	2012.000	nan

	year	name	rank	citizenship	countrycode	networthusbillion	selfmade	typeofwealth	gender	age	...	relationshiptocompany	foundingdate	gdpcurrentus	sourceofwealth	notes	notes2	source	source_2	source_3	source_4
516	2014	David Rockefeller, Sr.	580	United States	USA	2.900	inherited	inherited	male	98.000	...	relation	1870.000	nan	oil, banking	family made most of fortune in the late 19th a...	NaN	http://en.wikipedia.org/wiki/David_Rockefeller	http://en.wikipedia.org/wiki/Standard_Oil	http://en.wikipedia.org/wiki/Rockefeller_family	NaN
1277	2014	Karl Wlaschek	305	Austria	AUT	4.800	self-made	founder non-finance	male	96.000	...	founder	1953.000	nan	retail	NaN	NaN	http://en.wikipedia.org/wiki/BILLA	http://en.wikipedia.org/wiki/Karl_Wlaschek	https://www.billa.at/Footer_Nav_Seiten/Geschic...	NaN
1328	2014	Kirk Kerkorian	328	United States	USA	4.500	self-made	self-made finance	male	96.000	...	investor	1924.000	nan	casinos, investments	purchased in 1969	NaN	http://en.wikipedia.org/wiki/Kirk_Kerkorian	http://www.forbes.com/profile/kirk-kerkorian/	PROFILE: Las Vegas billionaire amassed his wea...	NaN

	Name	Age	Team	POS	#	2013 $	Ht (In.)	WT	EXP	1st Year	DOB	School	City	State (Province, Territory, Etc..)	Country	Race	HS Only	wt_std
40	Taylor, Jermaine	26	Cavaliers	G	8	$780,871	77	20	4	2009	12/8/1986	Central Florida	Tavares, FL	Florida	US	Black	No	7.201
136	Brown, Kwame	31	76ers	C	54	$2,945,901	83	290	12	2001	3/10/1982	Glynn Adademy (GA)	Charleston, SC	South Carolina	US	Black	Yes	2.462
315	Pekovi?, Nikola	27	Timberwolves	C	14	$12,000,000	83	290	3	2010	1/3/1986	n/a	Bijelo Polje	n/a	Yugoslavia	White	No	2.462
374	Jefferson, Al	28	Bobcats	F/C	25	$13,500,000	82	289	9	2004	1/4/1985	Prentiss HS (MS)	Monticello, MS	Mississippi	US	Black	Yes	2.426
207	Davis, Glen	27	Magic	F/C	11	$6,400,000	81	289	6	2007	1/1/1986	LSU	Baton Rouge, LA	Louisiana	US	Black	No	2.426

Math + pandas = 😍