In [1]:

    

import os, sys; sys.path.insert(0, '/home/trax/trax/website');
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "trax.settings"); import django; django.setup()
from tracks.models import Laptime

import matplotlib.pyplot as plt; import numpy as np; import sklearn; import pandas; import pylab
from django_pandas.io import read_frame


    

In [2]:

    

ls = Laptime.objects.all().order_by('player__defaultspeedmultiplier').select_related("player", "track", "vehicle")
l = ls[4]
l


    

    
Out[2]:





<Laptime: law-of11k-hours Jester (Racecar)>

In [4]:

    

len(ls)


    

    
Out[4]:





4778

In [5]:

    

df = read_frame(ls, fieldnames='track player__username vehicle__name millis_per_km vehicle__name vehicle__cc_millis_per_km'.split())
df.columns = ["track", "user", "vehicle", "millis_per_km", "cc_millis_per_km"]
# add comparison factor for user's millis to broughys millis
df['factor'] = pandas.Series(df.millis_per_km / df.cc_millis_per_km)


    

In [6]:

    

# get rid of outliers
q = df["factor"].quantile(0.9)
d2 = df[df["factor"] < q]


    

In [7]:

    

# all factors without outliers
# p = df["factor"]
# p.plot()
p1 = d2["factor"]
p1.plot()
pylab.show()


    

In [53]:

    

MÜLL
df["usergroup"] = df["user"].apply(lambda x: x)
df.groupby("usergroup")


    

    
Out[53]:





<pandas.core.groupby.DataFrameGroupBy object at 0x7f8eeb105860>

In [10]:

    

plt.boxplot_frame_groupby(df)
pylab.show()


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-10-527d6ecf61b8> in <module>()
----> 1 plt.boxplot_frame_groupby(df)
      2 pylab.show()

AttributeError: module 'matplotlib.pyplot' has no attribute 'boxplot_frame_groupby'

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [52]:

    

pandas.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])


    

    
Out[52]:






  

    

      

      
A
      
B
      
C
      
D
      
E
    
  
  

    

      
0
      
0.100080
      
0.854954
      
0.264603
      
0.691615
      
0.716892
    
    

      
1
      
0.192392
      
0.993862
      
0.487273
      
0.128545
      
0.541286
    
    

      
2
      
0.947816
      
0.923699
      
0.834418
      
0.426552
      
0.560064
    
    

      
3
      
0.891635
      
0.598437
      
0.474494
      
0.650938
      
0.971475
    
    

      
4
      
0.180480
      
0.679458
      
0.579854
      
0.917437
      
0.365245
    
    

      
5
      
0.785697
      
0.540084
      
0.150067
      
0.579005
      
0.951232
    
    

      
6
      
0.571493
      
0.110758
      
0.569167
      
0.071267
      
0.073609
    
    

      
7
      
0.727015
      
0.556342
      
0.256808
      
0.677769
      
0.699883
    
    

      
8
      
0.589915
      
0.868187
      
0.915272
      
0.817897
      
0.331520
    
    

      
9
      
0.382842
      
0.006132
      
0.315041
      
0.609432
      
0.202716
    
  

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [38]:

    

import itertools
from pandas.tools.plotting import boxplot_frame_groupby
tuples = [t for t in itertools.product(range(1000), range(4))]
index = pandas.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
data = np.random.randn(len(index),4)
df = pandas.DataFrame(data, columns=list('ABCD'), index=index)

grouped = df.groupby(level='lvl1')
boxplot_frame_groupby(grouped)

grouped = df.unstack(level='lvl1').groupby(level=0, axis=1)
# boxplot_frame_groupby(grouped, subplots=False)
# pylab.show()
df


    

    
Out[38]:






  

    

      

      

      
A
      
B
      
C
      
D
    
    

      
lvl0
      
lvl1
      

      

      

      

    
  
  

    

      
0
      
0
      
1.037234
      
-0.131072
      
-0.630253
      
0.207825
    
    

      
1
      
-0.790533
      
-0.166950
      
-0.509833
      
1.021970
    
    

      
2
      
1.121644
      
-0.037015
      
0.030709
      
-1.391198
    
    

      
3
      
1.794266
      
-0.584101
      
-0.702191
      
-1.797148
    
    

      
1
      
0
      
-0.367077
      
0.598757
      
1.161980
      
-1.095347
    
    

      
1
      
1.650469
      
-0.121776
      
-1.130366
      
0.090755
    
    

      
2
      
-0.606585
      
-1.372448
      
-0.233941
      
1.040336
    
    

      
3
      
-1.741796
      
0.497184
      
-0.069335
      
-0.475822
    
    

      
2
      
0
      
-0.732036
      
0.532453
      
-1.126113
      
-0.148860
    
    

      
1
      
0.242468
      
1.573469
      
0.392710
      
-0.368261
    
    

      
2
      
-0.765153
      
-0.382160
      
0.011847
      
0.112543
    
    

      
3
      
0.114847
      
1.567667
      
-0.230247
      
0.693603
    
    

      
3
      
0
      
2.252577
      
-0.809137
      
0.349943
      
0.495256
    
    

      
1
      
-0.101655
      
-1.121743
      
-0.558346
      
-1.344632
    
    

      
2
      
1.197552
      
-1.012816
      
-0.603643
      
-0.340096
    
    

      
3
      
-1.222104
      
-0.825593
      
-0.013635
      
1.322623
    
    

      
4
      
0
      
-0.668481
      
0.234923
      
0.911751
      
-1.098247
    
    

      
1
      
0.611709
      
-0.367818
      
1.581235
      
-1.319264
    
    

      
2
      
-2.016797
      
0.413958
      
0.852004
      
1.175787
    
    

      
3
      
-0.279463
      
0.192337
      
1.764994
      
0.162347
    
    

      
5
      
0
      
0.402730
      
0.996482
      
-0.395128
      
-0.013840
    
    

      
1
      
0.621986
      
-0.583754
      
0.142636
      
-0.874932
    
    

      
2
      
1.298750
      
-1.714110
      
0.282662
      
0.698525
    
    

      
3
      
-0.268268
      
-1.737733
      
-0.968238
      
-0.315662
    
    

      
6
      
0
      
-0.672817
      
0.551330
      
0.051940
      
1.024918
    
    

      
1
      
1.116194
      
-0.958255
      
-0.323179
      
-0.356206
    
    

      
2
      
-0.745336
      
-0.238441
      
1.650456
      
0.376522
    
    

      
3
      
1.012349
      
-0.958845
      
0.525263
      
0.710771
    
    

      
7
      
0
      
0.770052
      
-0.627664
      
1.546957
      
0.434225
    
    

      
1
      
-1.093304
      
-1.753206
      
-0.429491
      
-0.704373
    
    

      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
992
      
2
      
0.735160
      
1.135372
      
-0.158876
      
-0.253111
    
    

      
3
      
-0.905235
      
1.733026
      
0.151235
      
-0.710076
    
    

      
993
      
0
      
-0.395200
      
-0.585855
      
0.306544
      
-0.743348
    
    

      
1
      
-0.849522
      
0.367174
      
-1.868907
      
-0.589423
    
    

      
2
      
-0.657082
      
0.958811
      
2.563504
      
-0.760427
    
    

      
3
      
-1.012479
      
1.039202
      
0.406111
      
-0.714200
    
    

      
994
      
0
      
-0.759811
      
-1.299131
      
0.956670
      
-2.196856
    
    

      
1
      
1.350195
      
0.814285
      
-0.531620
      
-0.388837
    
    

      
2
      
-0.526952
      
1.559027
      
-1.626518
      
-1.002361
    
    

      
3
      
0.960374
      
0.170227
      
-0.620980
      
-0.034101
    
    

      
995
      
0
      
2.195378
      
1.094035
      
-0.379494
      
1.888917
    
    

      
1
      
-0.426803
      
-0.587423
      
-2.083089
      
-0.429638
    
    

      
2
      
0.153566
      
-0.098318
      
-2.090262
      
-1.038682
    
    

      
3
      
-0.088220
      
-1.561281
      
-1.739887
      
-1.091735
    
    

      
996
      
0
      
-0.352882
      
0.606018
      
-0.621756
      
1.704960
    
    

      
1
      
-0.404691
      
1.252860
      
-0.254731
      
1.611457
    
    

      
2
      
-1.834375
      
-0.389918
      
-0.434337
      
-0.004427
    
    

      
3
      
0.801324
      
-0.725113
      
1.616316
      
-0.124851
    
    

      
997
      
0
      
0.408135
      
0.699071
      
-1.374958
      
2.616838
    
    

      
1
      
0.527522
      
-1.313852
      
0.106142
      
-0.735446
    
    

      
2
      
-0.347176
      
-1.672608
      
-0.855823
      
0.101256
    
    

      
3
      
-0.547187
      
-1.165161
      
0.757301
      
-0.471222
    
    

      
998
      
0
      
0.777989
      
-0.866290
      
0.078288
      
-0.810593
    
    

      
1
      
0.664134
      
-1.662360
      
-0.854448
      
-1.886010
    
    

      
2
      
0.932448
      
-1.026190
      
1.913659
      
-0.627087
    
    

      
3
      
-0.473102
      
1.572455
      
0.448506
      
-0.315747
    
    

      
999
      
0
      
-1.000346
      
-0.753827
      
1.042781
      
-0.215304
    
    

      
1
      
-0.816334
      
0.013106
      
-0.693447
      
0.163932
    
    

      
2
      
-0.629821
      
-0.143060
      
0.451467
      
0.108889
    
    

      
3
      
-0.113360
      
0.342278
      
-0.394479
      
-0.570539
    
  

4000 rows × 4 columns

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [30]:

    

from sklearn.datasets import load_boston


    

In [31]:

    

boston = load_boston()
print(boston.data.shape)


    

    




(506, 13)

In [32]:

    

print(boston.DESCR)


    

    




Boston House Prices dataset
===========================

Notes
------
Data Set Characteristics:  

    :Number of Instances: 506 

    :Number of Attributes: 13 numeric/categorical predictive
    
    :Median Value (attribute 14) is usually the target

    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - AGE      proportion of owner-occupied units built prior to 1940
        - DIS      weighted distances to five Boston employment centres
        - RAD      index of accessibility to radial highways
        - TAX      full-value property-tax rate per $10,000
        - PTRATIO  pupil-teacher ratio by town
        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
        - LSTAT    % lower status of the population
        - MEDV     Median value of owner-occupied homes in $1000's

    :Missing Attribute Values: None

    :Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset.
http://archive.ics.uci.edu/ml/datasets/Housing


This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
prices and the demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics
...', Wiley, 1980.   N.B. Various transformations are used in the table on
pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression
problems.   
     
**References**

   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)

In [36]:

    

from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt

lr = linear_model.LinearRegression()
boston = load_boston()
y = boston.target

# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validation:
predicted = cross_val_predict(lr, boston.data, y, cv=10)

fig, ax = plt.subplots()
ax.scatter(y, predicted)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()


    

In [ ]: