In [1]:
# Numeric Packages
from __future__ import division
import numpy as np
import pandas as pd
import scipy.stats as sps
# Plotting packages
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('whitegrid')
# Other
from datetime import datetime, timedelta
import statsmodels.api as sm
In [2]:
# Import turnstile data and convert datetime column to datetime python objects
df = pd.read_csv('turnstile_weather_v2.csv')
df['datetime'] = pd.to_datetime(df['datetime'])
In [3]:
# Because the hour '0' is actually the entries from 20:00 to 24:00, it makes more sense to label it 24 when plotting data
df.datetime -= timedelta(seconds=1)
df['day']= df.datetime.apply(lambda x: x.day)
df['hour'] = df.datetime.apply(lambda x: x.hour+1)
df['weekday'] = df.datetime.apply(lambda x: not bool(x.weekday()//5))
df['day_week'] = df.datetime.apply(lambda x: x.weekday())
# df.loc[df['hour']==24, 'day'] -=1
In [4]:
# The dataset includes the Memorial Day Public Holiday, which should be classified as a weekend.
df.loc[df['day']==30,'weekday'] = False
In [5]:
# Filter out results from 30th April... they're just going to make things messy in the plots
plot_df = df[df.datetime.apply(lambda x: x.month==5)]
In [6]:
timelabels = ['Midnight - 4am','4am - 8am','8am - 12pm','12pm - 4pm','4pm - 8pm','8pm - Midnight']
weekdays = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
In [7]:
plt.figure(figsize=[20,6])
plt.subplot(211)
data=df.pivot_table(values='ENTRIESn_hourly',index='hour',columns='day', aggfunc=np.sum)
sns.heatmap(data, cmap='YlGnBu', yticklabels=timelabels)
plt.ylabel('')
plt.xlabel('')
plt.xlim(0,32)
plt.title('Daily NYC Subway Ridership (top) and Precipitation (bottom) for May 2011', fontsize=16)
# plt.show()
plt.subplot(212)
data=df.pivot_table(values='precipi',index='hour',columns='day', aggfunc=np.mean)
# plt.figure(figsize=[20,3])
sns.heatmap(data, cmap='OrRd', yticklabels=timelabels)
plt.ylabel('')
plt.xlim(0,32)
plt.xticks([])
# plt.title('Daily NYC Subway Ridership for May 2011', fontsize=16)
plt.show()
In [8]:
mydf = df[df.day!=30].pivot_table(values='ENTRIESn_hourly', index=['day','day_week','hour'], aggfunc=np.sum).reset_index()
mydf = mydf.pivot_table(values='ENTRIESn_hourly', index='hour', columns='day_week', aggfunc=np.mean)
sns.heatmap(mydf, yticklabels=timelabels, xticklabels=weekdays)
plt.xlabel('')
plt.ylabel('')
plt.title('Daily NYC Subway Ridership for May 2011', fontsize=14)
plt.show()
In [10]:
df.columns
Out[10]:
Index([u'UNIT', u'DATEn', u'TIMEn', u'ENTRIESn', u'EXITSn', u'ENTRIESn_hourly',
u'EXITSn_hourly', u'datetime', u'hour', u'day_week', u'weekday',
u'station', u'latitude', u'longitude', u'conds', u'fog', u'precipi',
u'pressurei', u'rain', u'tempi', u'wspdi', u'meanprecipi',
u'meanpressurei', u'meantempi', u'meanwspdi', u'weather_lat',
u'weather_lon', u'day'],
dtype='object')
In [11]:
df['hour_str'] = [str(i) for i in df['hour']]
df['day_str'] = df.datetime.apply(lambda x: x.strftime('%A'))
In [60]:
mod = sm.OLS.from_formula('ENTRIESn_hourly ~ precipi + C(weekday) + UNIT + C(hour) -1', data=df)
res = mod.fit()
print res.summary()
OLS Regression Results
==============================================================================
Dep. Variable: ENTRIESn_hourly R-squared: 0.552
Model: OLS Adj. R-squared: 0.549
Method: Least Squares F-statistic: 212.0
Date: Sat, 01 Aug 2015 Prob (F-statistic): 0.00
Time: 00:06:18 Log-Likelihood: -3.8420e+05
No. Observations: 42649 AIC: 7.689e+05
Df Residuals: 42402 BIC: 7.710e+05
Df Model: 246
Covariance Type: nonrobust
=====================================================================================
coef std err t P>|t| [95.0% Conf. Int.]
-------------------------------------------------------------------------------------
C(weekday)[False] -2184.8212 155.032 -14.093 0.000 -2488.686 -1880.956
C(weekday)[True] -1006.7314 154.529 -6.515 0.000 -1309.611 -703.852
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UNIT[T.R007] 175.5685 215.718 0.814 0.416 -247.244 598.381
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UNIT[T.R335] 278.4820 217.671 1.279 0.201 -148.158 705.122
UNIT[T.R336] -86.6639 217.671 -0.398 0.691 -513.304 339.976
UNIT[T.R337] -33.7918 216.356 -0.156 0.876 -457.855 390.271
UNIT[T.R338] -149.2196 214.484 -0.696 0.487 -569.613 271.174
UNIT[T.R341] 434.9128 211.884 2.053 0.040 19.615 850.210
UNIT[T.R344] 384.5815 217.678 1.767 0.077 -42.073 811.236
UNIT[T.R345] 384.2600 214.189 1.794 0.073 -35.556 804.076
UNIT[T.R346] 1175.7727 214.493 5.482 0.000 755.362 1596.183
UNIT[T.R348] 37.2557 214.483 0.174 0.862 -383.134 457.646
UNIT[T.R354] 84.5718 216.680 0.390 0.696 -340.125 509.268
UNIT[T.R356] 1014.2371 213.889 4.742 0.000 595.010 1433.464
UNIT[T.R358] 109.6844 216.679 0.506 0.613 -315.012 534.381
UNIT[T.R370] 389.4751 213.893 1.821 0.069 -29.759 808.709
UNIT[T.R371] 605.3028 215.099 2.814 0.005 183.704 1026.901
UNIT[T.R372] 588.4894 216.044 2.724 0.006 165.040 1011.939
UNIT[T.R373] 536.0482 216.364 2.478 0.013 111.969 960.127
UNIT[T.R382] 814.6732 213.590 3.814 0.000 396.033 1233.314
UNIT[T.R424] 245.8998 217.341 1.131 0.258 -180.093 671.892
UNIT[T.R429] 929.0759 211.883 4.385 0.000 513.782 1344.370
UNIT[T.R453] 1678.7294 218.360 7.688 0.000 1250.740 2106.719
UNIT[T.R454] -3.4266 216.688 -0.016 0.987 -428.139 421.285
UNIT[T.R455] -52.5719 216.365 -0.243 0.808 -476.652 371.509
UNIT[T.R456] 135.9838 213.599 0.637 0.524 -282.674 554.642
UNIT[T.R459] -46.5100 260.940 -0.178 0.859 -557.958 464.938
UNIT[T.R464] -219.9875 216.036 -1.018 0.309 -643.422 203.447
C(hour)[T.8] 282.6097 34.719 8.140 0.000 214.560 350.659
C(hour)[T.12] 2687.8018 32.751 82.067 0.000 2623.609 2751.995
C(hour)[T.16] 1985.5882 32.738 60.651 0.000 1921.421 2049.755
C(hour)[T.20] 2905.1814 32.771 88.650 0.000 2840.949 2969.414
C(hour)[T.24] 1129.3470 32.652 34.588 0.000 1065.349 1193.345
precipi -1200.9817 377.184 -3.184 0.001 -1940.270 -461.693
==============================================================================
Omnibus: 30389.520 Durbin-Watson: 1.550
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1133111.735
Skew: 2.993 Prob(JB): 0.00
Kurtosis: 27.532 Cond. No. 220.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
In [62]:
plt.plot([0,20000],[0,20000])
plt.scatter(df.ENTRIESn_hourly,res.predict())
Out[62]:
<matplotlib.collections.PathCollection at 0x10e773890>
In [14]:
mydf2 = df.pivot_table(values='ENTRIESn_hourly', index=['day','weekday','hour_str','precipi'], aggfunc=np.sum).reset_index()
mod = sm.OLS.from_formula('ENTRIESn_hourly ~ precipi + weekday + hour_str', data=mydf2)
res = mod.fit()
print res.summary()
OLS Regression Results
==============================================================================
Dep. Variable: ENTRIESn_hourly R-squared: 0.753
Model: OLS Adj. R-squared: 0.745
Method: Least Squares F-statistic: 87.61
Date: Fri, 31 Jul 2015 Prob (F-statistic): 1.37e-57
Time: 14:56:33 Log-Likelihood: -2796.4
No. Observations: 209 AIC: 5609.
Df Residuals: 201 BIC: 5635.
Df Model: 7
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [95.0% Conf. Int.]
-----------------------------------------------------------------------------------
Intercept 5.247e+05 3.24e+04 16.177 0.000 4.61e+05 5.89e+05
weekday[T.True] 2.098e+05 2.41e+04 8.716 0.000 1.62e+05 2.57e+05
hour_str[T.16] -1.754e+05 3.9e+04 -4.499 0.000 -2.52e+05 -9.85e+04
hour_str[T.20] 7.171e+04 3.96e+04 1.811 0.072 -6364.734 1.5e+05
hour_str[T.24] -3.471e+05 3.94e+04 -8.817 0.000 -4.25e+05 -2.69e+05
hour_str[T.4] -5.92e+05 3.88e+04 -15.253 0.000 -6.69e+05 -5.15e+05
hour_str[T.8] -5.259e+05 3.75e+04 -14.017 0.000 -6e+05 -4.52e+05
precipi -1.118e+06 3.14e+05 -3.560 0.000 -1.74e+06 -4.99e+05
==============================================================================
Omnibus: 88.410 Durbin-Watson: 2.074
Prob(Omnibus): 0.000 Jarque-Bera (JB): 327.603
Skew: -1.718 Prob(JB): 7.27e-72
Kurtosis: 8.080 Cond. No. 37.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
In [15]:
len(df)
Out[15]:
42649
In [16]:
sm.qqplot((mydf2.ENTRIESn_hourly-res.predict()))
plt.show()
In [17]:
plt.plot([0,1000000],[0,1000000])
plt.scatter(mydf2.ENTRIESn_hourly,res.predict())
Out[17]:
<matplotlib.collections.PathCollection at 0x10ad31290>
In [18]:
def rsquared(x, y):
""" Return R^2 where x and y are array-like.
http://stackoverflow.com/questions/893657/how-do-i-calculate-r-squared-using-python-and-numpy
"""
slope, intercept, r_value, p_value, std_err = sps.linregress(x, y)
return r_value**2
In [19]:
rsquared(df.ENTRIESn_hourly,res.predict())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-ed3aec44d8e3> in <module>()
----> 1 rsquared(df.ENTRIESn_hourly,res.predict())
<ipython-input-18-9612bef57a51> in rsquared(x, y)
5 """
6
----> 7 slope, intercept, r_value, p_value, std_err = sps.linregress(x, y)
8 return r_value**2
/anaconda/lib/python2.7/site-packages/scipy/stats/stats.pyc in linregress(x, y)
3049
3050 # average sum of squares:
-> 3051 ssxm, ssxym, ssyxm, ssym = np.cov(x, y, bias=1).flat
3052 r_num = ssxym
3053 r_den = np.sqrt(ssxm*ssym)
/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in cov(m, y, rowvar, bias, ddof)
1893 if y is not None:
1894 y = array(y, copy=False, ndmin=2, dtype=dtype)
-> 1895 X = concatenate((X, y), axis)
1896
1897 X -= X.mean(axis=1-axis, keepdims=True)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
In [20]:
plt.hist(df.ENTRIESn_hourly-res.predict(), bins=100)
plt.show()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-cc443b2eb156> in <module>()
----> 1 plt.hist(df.ENTRIESn_hourly-res.predict(), bins=100)
2 plt.show()
/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in wrapper(left, right, name)
530 if hasattr(lvalues, 'values'):
531 lvalues = lvalues.values
--> 532 return left._constructor(wrap_results(na_op(lvalues, rvalues)),
533 index=left.index, name=left.name,
534 dtype=dtype)
/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in na_op(x, y)
467 try:
468 result = expressions.evaluate(op, str_rep, x, y,
--> 469 raise_on_error=True, **eval_kwargs)
470 except TypeError:
471 if isinstance(y, (np.ndarray, pd.Series, pd.Index)):
/anaconda/lib/python2.7/site-packages/pandas/computation/expressions.pyc in evaluate(op, op_str, a, b, raise_on_error, use_numexpr, **eval_kwargs)
216 if use_numexpr:
217 return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error,
--> 218 **eval_kwargs)
219 return _evaluate_standard(op, op_str, a, b, raise_on_error=raise_on_error)
220
/anaconda/lib/python2.7/site-packages/pandas/computation/expressions.pyc in _evaluate_numexpr(op, op_str, a, b, raise_on_error, truediv, reversed, **eval_kwargs)
127
128 if result is None:
--> 129 result = _evaluate_standard(op, op_str, a, b, raise_on_error)
130
131 return result
/anaconda/lib/python2.7/site-packages/pandas/computation/expressions.pyc in _evaluate_standard(op, op_str, a, b, raise_on_error, **eval_kwargs)
69 if _TEST_MODE:
70 _store_test_result(False)
---> 71 return op(a, b)
72
73
ValueError: operands could not be broadcast together with shapes (42649,) (209,)
In [21]:
sns.set_style('white')
df.hist(column='ENTRIESn_hourly', by='rain', bins=np.arange(0,15000,1000), sharey=True, figsize=[14,8])
sns.despine(left=True)
In [23]:
df.columns
Out[23]:
Index([u'UNIT', u'DATEn', u'TIMEn', u'ENTRIESn', u'EXITSn', u'ENTRIESn_hourly',
u'EXITSn_hourly', u'datetime', u'hour', u'day_week', u'weekday',
u'station', u'latitude', u'longitude', u'conds', u'fog', u'precipi',
u'pressurei', u'rain', u'tempi', u'wspdi', u'meanprecipi',
u'meanpressurei', u'meantempi', u'meanwspdi', u'weather_lat',
u'weather_lon', u'day', u'hour_str', u'day_str'],
dtype='object')
In [36]:
plt.figure(figsize=[8,6])
sns.heatmap(
df[['ENTRIESn_hourly','EXITSn_hourly','day_week','weekday','day','hour','fog','precipi','rain','tempi','wspdi']].corr(),
)
Out[36]:
<matplotlib.axes._subplots.AxesSubplot at 0x1105c0d90>
In [50]:
import matplotlib as mpl
In [51]:
plt.scatter(mpl.dates.date2num(df.datetime), df.tempi)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-51-1c2d10411298> in <module>()
----> 1 plt.scatter(mpl.dates.date2num(df.datetime), df.tempi)
/anaconda/lib/python2.7/site-packages/matplotlib/dates.pyc in date2num(d)
308 if not d.size:
309 return d
--> 310 return _to_ordinalf_np_vectorized(d)
311
312
/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in __call__(self, *args, **kwargs)
1698 vargs.extend([kwargs[_n] for _n in names])
1699
-> 1700 return self._vectorize_call(func=func, args=vargs)
1701
1702 def _get_ufunc_and_otypes(self, func, args):
/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in _vectorize_call(self, func, args)
1761 _res = func()
1762 else:
-> 1763 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
1764
1765 # Convert args to object arrays first
/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in _get_ufunc_and_otypes(self, func, args)
1723 # arrays (the input values are not checked to ensure this)
1724 inputs = [asarray(_a).flat[0] for _a in args]
-> 1725 outputs = func(*inputs)
1726
1727 # Performance note: profiling indicates that -- for simple
/anaconda/lib/python2.7/site-packages/matplotlib/dates.pyc in _to_ordinalf(dt)
202 dt -= delta
203
--> 204 base = float(dt.toordinal())
205 if hasattr(dt, 'hour'):
206 base += (dt.hour / HOURS_PER_DAY + dt.minute / MINUTES_PER_DAY +
AttributeError: 'numpy.datetime64' object has no attribute 'toordinal'
In [22]:
sns.distplot(df.ENTRIESn_hourly, kde=False, fit=sps.lognorm)
sns.despine(left=True)
plt.xlim(xmin=0)
Out[22]:
(0, 35000.0)
In [ ]:
df.columns
In [ ]:
sns.pairplot(df[[')
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-272-033902782ad7> in <module>()
----> 1 sns.pairplot(df)
/anaconda/lib/python2.7/site-packages/seaborn/linearmodels.pyc in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)
1581 hue_order=hue_order, palette=palette,
1582 diag_sharey=diag_sharey,
-> 1583 size=size, aspect=aspect, dropna=dropna, **grid_kws)
1584
1585 # Add the markers here as PairGrid has figured out how many levels of the
/anaconda/lib/python2.7/site-packages/seaborn/axisgrid.pyc in __init__(self, data, hue, hue_order, palette, hue_kws, vars, x_vars, y_vars, diag_sharey, size, aspect, despine, dropna)
1200 figsize=figsize,
1201 sharex="col", sharey="row",
-> 1202 squeeze=False)
1203
1204 self.fig = fig
/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc in subplots(nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw, **fig_kw)
1115 else:
1116 subplot_kw['sharey'] = axarr[sys[i]]
-> 1117 axarr[i] = fig.add_subplot(gs[i // ncols, i % ncols], **subplot_kw)
1118
1119 # returned axis array will be always 2-d, even if nrows=ncols=1
/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc in add_subplot(self, *args, **kwargs)
962 self._axstack.remove(ax)
963
--> 964 a = subplot_class_factory(projection_class)(self, *args, **kwargs)
965
966 self._axstack.add(key, a)
/anaconda/lib/python2.7/site-packages/matplotlib/axes/_subplots.pyc in __init__(self, fig, *args, **kwargs)
76
77 # _axes_class is set in the subplot_class_factory
---> 78 self._axes_class.__init__(self, fig, self.figbox, **kwargs)
79
80 def __reduce__(self):
/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in __init__(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs)
423
424 # this call may differ for non-sep axes, e.g., polar
--> 425 self._init_axis()
426
427 if axisbg is None:
/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in _init_axis(self)
482 self.xaxis = maxis.XAxis(self)
483 self.spines['bottom'].register_axis(self.xaxis)
--> 484 self.spines['top'].register_axis(self.xaxis)
485 self.yaxis = maxis.YAxis(self)
486 self.spines['left'].register_axis(self.yaxis)
/anaconda/lib/python2.7/site-packages/matplotlib/spines.pyc in register_axis(self, axis)
155 self.axis = axis
156 if self.axis is not None:
--> 157 self.axis.cla()
158
159 def cla(self):
/anaconda/lib/python2.7/site-packages/matplotlib/axis.pyc in cla(self)
752 self._set_artist_props(self.label)
753
--> 754 self.reset_ticks()
755
756 self.converter = None
/anaconda/lib/python2.7/site-packages/matplotlib/axis.pyc in reset_ticks(self)
766
767 self.majorTicks.extend([self._get_tick(major=True)])
--> 768 self.minorTicks.extend([self._get_tick(major=False)])
769 self._lastNumMajorTicks = 1
770 self._lastNumMinorTicks = 1
/anaconda/lib/python2.7/site-packages/matplotlib/axis.pyc in _get_tick(self, major)
1660 else:
1661 tick_kw = self._minor_tick_kw
-> 1662 return XTick(self.axes, 0, '', major=major, **tick_kw)
1663
1664 def _get_label(self):
/anaconda/lib/python2.7/site-packages/matplotlib/axis.pyc in __init__(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major)
147
148 self.tick1line = self._get_tick1line()
--> 149 self.tick2line = self._get_tick2line()
150 self.gridline = self._get_gridline()
151
/anaconda/lib/python2.7/site-packages/matplotlib/axis.pyc in _get_tick2line(self)
411 markersize=self._size,
412 markeredgewidth=self._width,
--> 413 zorder=self._zorder,
414 )
415
/anaconda/lib/python2.7/site-packages/matplotlib/lines.pyc in __init__(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)
335 self.set_markeredgecolor(markeredgecolor)
336 self.set_markeredgewidth(markeredgewidth)
--> 337 self.set_fillstyle(fillstyle)
338
339 self.verticalOffset = None
/anaconda/lib/python2.7/site-packages/matplotlib/lines.pyc in set_fillstyle(self, fs)
457 ACCEPTS: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
458 """
--> 459 self._marker.set_fillstyle(fs)
460
461 def set_markevery(self, every):
/anaconda/lib/python2.7/site-packages/matplotlib/markers.pyc in set_fillstyle(self, fillstyle)
216 % ' '.join(self.fillstyles))
217 self._fillstyle = fillstyle
--> 218 self._recache()
219
220 def get_joinstyle(self):
/anaconda/lib/python2.7/site-packages/matplotlib/markers.pyc in _recache(self)
189 self._capstyle = 'butt'
190 self._filled = True
--> 191 self._marker_function()
192
193 if six.PY3:
/anaconda/lib/python2.7/site-packages/matplotlib/markers.pyc in _set_tickup(self)
692
693 def _set_tickup(self):
--> 694 self._transform = Affine2D().scale(1.0, 1.0)
695 self._snap_threshold = 1.0
696 self._filled = False
/anaconda/lib/python2.7/site-packages/matplotlib/transforms.pyc in scale(self, sx, sy)
1915 np.float_)
1916 self._mtx = np.dot(scale_mtx, self._mtx)
-> 1917 self.invalidate()
1918 return self
1919
KeyboardInterrupt:
Traceback (most recent call last):
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/ipkernel.py", line 181, in do_execute
shell.run_cell(code, store_history=store_history, silent=silent)
File "/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2874, in run_cell
self.events.trigger('post_execute')
File "/anaconda/lib/python2.7/site-packages/IPython/core/events.py", line 74, in trigger
func(*args, **kwargs)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/pylab/backend_inline.py", line 109, in flush_figures
return show(True)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/pylab/backend_inline.py", line 32, in show
display(figure_manager.canvas.figure)
File "/anaconda/lib/python2.7/site-packages/IPython/core/display.py", line 159, in display
format_dict, md_dict = format(obj, include=include, exclude=exclude)
File "/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py", line 179, in format
data = formatter(obj)
File "<string>", line 2, in __call__
File "/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py", line 224, in catch_format_error
r = method(self, *args, **kwargs)
File "/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py", line 335, in __call__
return printer(obj)
File "/anaconda/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 207, in <lambda>
png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
File "/anaconda/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 117, in print_figure
fig.canvas.print_figure(bytes_io, **kw)
File "/anaconda/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2214, in print_figure
restore_bbox()
File "/anaconda/lib/python2.7/site-packages/matplotlib/tight_bbox.py", line 50, in restore_bbox
fig.transFigure.invalidate()
File "/anaconda/lib/python2.7/site-packages/matplotlib/transforms.py", line 135, in invalidate
return self._invalidate_internal(value, invalidating_node=self)
File "/anaconda/lib/python2.7/site-packages/matplotlib/transforms.py", line 159, in _invalidate_internal
invalidating_node=self)
File "/anaconda/lib/python2.7/site-packages/matplotlib/transforms.py", line 159, in _invalidate_internal
invalidating_node=self)
File "/anaconda/lib/python2.7/site-packages/matplotlib/transforms.py", line 157, in _invalidate_internal
for parent in list(six.itervalues(self._parents)):
KeyboardInterrupt
ERROR:tornado.general:Uncaught exception, closing connection.
Traceback (most recent call last):
File "/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 407, in _run_callback
callback(*args, **kwargs)
File "/anaconda/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 252, in dispatcher
return self.dispatch_shell(stream, msg)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 213, in dispatch_shell
handler(stream, idents, msg)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 388, in execute_request
self._abort_queues()
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 588, in _abort_queues
self._abort_queue(stream)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 611, in _abort_queue
poller.poll(50)
File "/anaconda/lib/python2.7/site-packages/zmq/sugar/poll.py", line 101, in poll
return zmq_poll(self.sockets, timeout=timeout)
File "zmq/backend/cython/_poll.pyx", line 115, in zmq.backend.cython._poll.zmq_poll (zmq/backend/cython/_poll.c:1625)
File "zmq/backend/cython/checkrc.pxd", line 12, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/_poll.c:1958)
PyErr_CheckSignals()
KeyboardInterrupt
ERROR:tornado.general:Uncaught exception, closing connection.
Traceback (most recent call last):
File "/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 433, in _handle_events
self._handle_recv()
File "/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 465, in _handle_recv
self._run_callback(callback, msg)
File "/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 407, in _run_callback
callback(*args, **kwargs)
File "/anaconda/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 252, in dispatcher
return self.dispatch_shell(stream, msg)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 213, in dispatch_shell
handler(stream, idents, msg)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 388, in execute_request
self._abort_queues()
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 588, in _abort_queues
self._abort_queue(stream)
File "/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py", line 611, in _abort_queue
poller.poll(50)
File "/anaconda/lib/python2.7/site-packages/zmq/sugar/poll.py", line 101, in poll
return zmq_poll(self.sockets, timeout=timeout)
File "zmq/backend/cython/_poll.pyx", line 115, in zmq.backend.cython._poll.zmq_poll (zmq/backend/cython/_poll.c:1625)
File "zmq/backend/cython/checkrc.pxd", line 12, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/_poll.c:1958)
PyErr_CheckSignals()
KeyboardInterrupt
In [28]:
plt.figure(figsize=[13,33])
sns.stripplot(y='UNIT', x='date_time', data=df, size=6)
plt.xlim(df.date_time.min(), datetime(2011,5,15,0,0,0))
Out[28]:
(734258.0, 734272.0)
In [ ]:
Content source: chris-jd/udacity
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