In [1]:
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
from ggplot import *
import pygal
import numpy as np
from sklearn import datasets

In [14]:
iris = sns.load_dataset("iris")
titanic = sns.load_dataset("titanic")

In [15]:
titanic.head()


Out[15]:
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
0 0 3 male 22 1 0 7.2500 S Third man True NaN Southampton no False
1 1 1 female 38 1 0 71.2833 C First woman False C Cherbourg yes False
2 1 3 female 26 0 0 7.9250 S Third woman False NaN Southampton yes True
3 1 1 female 35 1 0 53.1000 S First woman False C Southampton yes False
4 0 3 male 35 0 0 8.0500 S Third man True NaN Southampton no True

In [27]:
sns.pairplot(titanic[["alive", "pclass", "fare", "age"]], hue="alive")


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-27-85f20cf687d5> in <module>()
----> 1 sns.pairplot(titanic[["alive", "pclass", "fare", "age"]], hue="alive")

D:\Anaconda3\lib\site-packages\seaborn\linearmodels.py 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)
   1602     if grid.square_grid:
   1603         if diag_kind == "hist":
-> 1604             grid.map_diag(plt.hist, **diag_kws)
   1605         elif diag_kind == "kde":
   1606             diag_kws["legend"] = False

D:\Anaconda3\lib\site-packages\seaborn\axisgrid.py in map_diag(self, func, **kwargs)
   1325                         vals.append(np.array([]))
   1326                 func(vals, color=self.palette, histtype="barstacked",
-> 1327                      **kwargs)
   1328             else:
   1329                 for k, label_k in enumerate(self.hue_names):

D:\Anaconda3\lib\site-packages\matplotlib\pyplot.py in hist(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)
   2956                       histtype=histtype, align=align, orientation=orientation,
   2957                       rwidth=rwidth, log=log, color=color, label=label,
-> 2958                       stacked=stacked, data=data, **kwargs)
   2959     finally:
   2960         ax.hold(washold)

D:\Anaconda3\lib\site-packages\matplotlib\__init__.py in inner(ax, *args, **kwargs)
   1809                     warnings.warn(msg % (label_namer, func.__name__),
   1810                                   RuntimeWarning, stacklevel=2)
-> 1811             return func(ax, *args, **kwargs)
   1812         pre_doc = inner.__doc__
   1813         if pre_doc is None:

D:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
   6008             # this will automatically overwrite bins,
   6009             # so that each histogram uses the same bins
-> 6010             m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
   6011             m = m.astype(float)  # causes problems later if it's an int
   6012             if mlast is None:

D:\Anaconda3\lib\site-packages\numpy\lib\function_base.py in histogram(a, bins, range, normed, weights, density)
    174         if (mn > mx):
    175             raise AttributeError(
--> 176                 'max must be larger than min in range parameter.')
    177 
    178     # Histogram is an integer or a float array depending on the weights.

AttributeError: max must be larger than min in range parameter.

In [20]:
titanic[["survived", "pclass", "age", "fare"]]


Out[20]:
survived pclass age fare
0 0 3 22 7.2500
1 1 1 38 71.2833
2 1 3 26 7.9250
3 1 1 35 53.1000
4 0 3 35 8.0500
5 0 3 NaN 8.4583
6 0 1 54 51.8625
7 0 3 2 21.0750
8 1 3 27 11.1333
9 1 2 14 30.0708
10 1 3 4 16.7000
11 1 1 58 26.5500
12 0 3 20 8.0500
13 0 3 39 31.2750
14 0 3 14 7.8542
15 1 2 55 16.0000
16 0 3 2 29.1250
17 1 2 NaN 13.0000
18 0 3 31 18.0000
19 1 3 NaN 7.2250
20 0 2 35 26.0000
21 1 2 34 13.0000
22 1 3 15 8.0292
23 1 1 28 35.5000
24 0 3 8 21.0750
25 1 3 38 31.3875
26 0 3 NaN 7.2250
27 0 1 19 263.0000
28 1 3 NaN 7.8792
29 0 3 NaN 7.8958
... ... ... ... ...
861 0 2 21 11.5000
862 1 1 48 25.9292
863 0 3 NaN 69.5500
864 0 2 24 13.0000
865 1 2 42 13.0000
866 1 2 27 13.8583
867 0 1 31 50.4958
868 0 3 NaN 9.5000
869 1 3 4 11.1333
870 0 3 26 7.8958
871 1 1 47 52.5542
872 0 1 33 5.0000
873 0 3 47 9.0000
874 1 2 28 24.0000
875 1 3 15 7.2250
876 0 3 20 9.8458
877 0 3 19 7.8958
878 0 3 NaN 7.8958
879 1 1 56 83.1583
880 1 2 25 26.0000
881 0 3 33 7.8958
882 0 3 22 10.5167
883 0 2 28 10.5000
884 0 3 25 7.0500
885 0 3 39 29.1250
886 0 2 27 13.0000
887 1 1 19 30.0000
888 0 3 NaN 23.4500
889 1 1 26 30.0000
890 0 3 32 7.7500

891 rows × 4 columns