TypeError: can't multiply sequence by non-int of type 'float'
#### Code Sample, a copy-pastable example if possible

```python
# Your code here
import numpy as np  
import pandas as pd
import matplotlib.pyplot as plt  
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
plt.rcParams['font.sans-serif'] = ['KaiTi']
plt.rcParams['font.serif'] = ['KaiTi']
# plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题,或者转换负号为字符串
import seaborn as sns  
sns.set_style("darkgrid",{"font.sans-serif":['KaiTi', 'Arial']})   #这是方便seaborn绘图得时候得字体设置

sns.set( palette="muted", color_codes=True)  

d = pd.read_csv('C:/Users/MaiBenBen/Desktop/nicetable/03170809.csv',sep=';', encoding='gbk')
f, ax = plt.subplots( figsize=(7,7))
plt.title('Histgram of Rock Fragmentation', fontsize=22)

print(d)
print(d.columns)
print(type(d))
y = d.loc[:,'Diameter']
#y = list(d.loc[:,'Diameter'])
print(type(y))
print(y)

#fig,axes=plt.subplots(1,2)
#sns.distplot(x,norm_hist=True,kde=False,ax=axes[0]) #左图
#sns.distplot(x,kde=False,ax=axes[1]) #右图
sns.distplot(y,kde=False,color="r")  
#sns.distplot(d.loc[:,'Diameter'],kde=False,color="r")  

print('---------------------------')
#group_labels = ['0', '10', '20','30','40','50','60', '70','80','90','100']
#ax.set_yticklabels(group_labels,#设置刻度对应的标签
#                   rotation=0, fontsize='small')#rotation选项设定x刻度标签倾斜30度。

plt.xlabel('size (cm)')
plt.ylabel('quantity of fragmentations')  #只有最后一个图有改变

fig = ax.get_figure()
fig.savefig('c-1.png', dpi=100, bbox_inches='tight')
plt.show() 
```
#### Problem description

[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
![图片](https://user-images.githubusercontent.com/29192658/57976238-d2b8ca00-7a0d-11e9-96b7-afd8e1be28a5.png)


**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!

**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.

For documentation-related issues, you can check the latest versions of the docs on `master` here:

https://pandas-docs.github.io/pandas-docs-travis/

If the issue has not been resolved there, go ahead and file it in the issue tracker.

#### Expected Output
     Unnamed: 0   ID  Center-X  Center-Y   Area  Perimeter  Min-Y  Min-X  Max-Y  Max-X  (pix)Diameter  ConvexArea  Holes  FilledArea  Solidity  Major  Minor  Ori       Diameter
0             0    0       6.8      17.0    359       95.5      2      2     40     17           21.4         417      0         359      0.86   41.7   12.7  1.4  1.681.916.933
1             1    1     106.2      73.5  25099      924.9      2      2    165    270          178.8       30361      0       25099      0.83  242.4  150.6  0.3  1.405.265.176
2             2    2     233.1       5.8    232       75.4      2    217     12    251           17.2         255      0         232      0.91   33.8    9.0 -0.1  1.351.821.086
3             3    3     269.6       2.6      5        3.0      2    269      5    272            2.5           6      0           5      0.83    4.0    2.1  0.8    196.485.623
4             4    4     296.0       2.0      3        1.0      2    295      3    298            2.0           3      0           3      1.00    3.3    0.0  0.0  1.571.884.984
5             5    5     341.2      52.1  10897      617.2      2    226     97    455          117.8       13789      0       10897      0.79  203.8   87.4 -0.1  9.258.402.556
6             6    6     595.9      31.1  11264     1027.4      2    421    100    725          119.8       19139      0       11264      0.59  268.8   90.7  0.0  9.415.591.054
7             7    7     743.6      26.6   1199      156.6      2    725     54    762           39.1        1384      0        1199      0.87   57.1   29.2 -1.3  3.073.035.144
8             8    8     843.6      42.0   7722      612.8      2    765     90    940           99.2       10442      0        7722      0.74  175.6   65.5 -0.3  7.796.549.521
9             9    9     892.5       3.0      6        6.0      2    892      5    894            2.8           6      0           6      1.00    3.3    2.0 -1.6  2.200.638.978
10           10   10     907.5       2.0      2        0.0      2    907      3    909            1.6           2      0           2      1.00    2.0    0.0  0.0  1.257.507.987
11           11   11     927.1      18.4    147       64.0      2    923     32    933           13.7         182      0         147      0.81   28.5    7.6 -1.3  1.076.741.214
12           12   12    1179.1      12.2    544      138.3      2   1162     25   1207           26.3         759      0         544      0.72   42.4   22.8  0.1  2.067.028.754
13           13   13    1273.9      38.7   9789      475.7      2   1193     88   1343          111.6       10616      0        9789      0.92  151.7   85.6 -0.1  8.771.118.211
14           14   14    1384.0       3.0      3        1.0      2   1384      5   1385            2.0           3      0           3      1.00    3.3    0.0 -1.6  1.571.884.984
15           15   15     257.0       7.0      1        0.0      7    257      8    258            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
16           16   16     171.0      13.0      3        1.0     13    170     14    173            2.0           3      0           3      1.00    3.3    0.0  0.0  1.571.884.984
17           17   17     281.0      13.5     18       18.0     13    277     15    286            4.8          18      0          18      1.00   10.3    2.0  0.0  3.772.523.962
18           18   18    1115.7      25.5    480       95.9     14   1101     39   1137           24.7         537      0         480      0.89   34.8   18.6  0.4  1.941.277.955
19           19   19     286.0      15.0      1        0.0     15    286     16    287            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
20           20   20    1010.9      38.3    910      179.5     15    980     64   1044           34.0        1092      0         910      0.83   77.0   16.9 -0.7  2.672.204.473
21           21   21     913.6      20.0     20       14.6     17    912     23    916            5.0          21      0          20      0.95    6.2    4.2  1.3    392.971.246
22           22   22     876.9      19.1      8        7.4     18    876     21    879            3.2           8      0           8      1.00    3.5    2.7 -0.8  2.515.015.974
23           23   23    1349.0      19.0      1        0.0     19   1349     20   1350            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
24           24   24     725.0      20.0      1        0.0     20    725     21    726            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
25           25   25    1360.3      29.5    295       89.5     20   1350     41   1382           19.4         387      0         295      0.76   28.3   16.8  0.3  1.524.728.435
26           26   26     879.0      21.0      1        0.0     21    879     22    880            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
27           27   27     219.2      25.1     31       24.1     23    214     30    224            6.3          44      0          31      0.70   10.2    5.7 -0.2     49.514.377
28           28   28     891.8      23.4      5        5.2     23    891     25    894            2.5           5      0           5      1.00    3.1    1.8  0.3    196.485.623
29           29   29     905.7      25.8     53       26.4     23    902     31    910            8.2          56      0          53      0.95    9.2    7.6 -0.3  6.444.728.435
..          ...  ...       ...       ...    ...        ...    ...    ...    ...    ...            ...         ...    ...         ...       ...    ...    ...  ...            ...
449         449  449    1295.5     876.0      2        0.0    876   1295    877   1297            1.6           2      0           2      1.00    2.0    0.0  0.0  1.257.507.987
450         450  450     685.4     905.9   1543      192.8    877    651    924    714           44.3        1845      0        1543      0.84   57.4   40.7  0.7    348.172.524
451         451  451     378.7     880.4      7        7.2    880    377    882    381            3.0           7      0           7      1.00    4.2    1.9 -0.1  2.357.827.476
452         452  452     312.5     883.0      2        0.0    883    312    884    314            1.6           2      0           2      1.00    2.0    0.0  0.0  1.257.507.987
453         453  453    1341.1     902.6    249       82.9    885   1334    919   1349           17.8         332      0         249      0.75   33.1   11.6 -1.4  1.398.977.636
454         454  454     666.0     888.0      1        0.0    888    666    889    667            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
455         455  455     750.0     888.5      2        0.0    888    750    890    751            1.6           2      0           2      1.00    2.0    0.0 -1.6  1.257.507.987
456         456  456     729.5     891.0      2        0.0    891    729    892    731            1.6           2      0           2      1.00    2.0    0.0  0.0  1.257.507.987
457         457  457     997.2     895.6      8        7.4    894    996    898   1000            3.2          10      0           8      0.80    5.5    2.1 -0.6  2.515.015.974
458         458  458    1300.5     895.5     11        9.2    894   1299    900   1303            3.7          15      0          11      0.73    6.8    3.4 -1.4    290.798.722
459         459  459     586.8     900.4     38       24.7    897    584    906    592            7.0          47      0          38      0.81    8.5    6.8 -1.5  5.501.597.444
460         460  460     406.3     904.6     24       17.1    901    402    909    412            5.5          40      0          24      0.60    9.8    4.8 -0.7  4.322.683.706
461         461  461     449.3     908.0     73       36.9    901    444    914    455            9.6          92      0          73      0.79   12.3    9.2 -1.1  7.545.047.923
462         462  462    1204.0     902.0      1        0.0    902   1204    903   1205            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
463         463  463    1206.5     903.0      2        0.0    903   1206    904   1208            1.6           2      0           2      1.00    2.0    0.0  0.0  1.257.507.987
464         464  464      10.3     915.4    307       75.8    904      2    924     26           19.8         333      0         307      0.92   27.0   16.4 -0.7  1.556.166.134
465         465  465    1383.0     904.0      1        0.0    904   1383    905   1384            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
466         466  466    1212.0     905.5      4        4.4    905   1211    907   1214            2.3           4      0           4      1.00    3.2    1.2 -0.6  1.807.667.732
467         467  467    1384.0     905.0      1        0.0    905   1384    906   1385            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
468         468  468     580.1     917.9    580      145.6    907    551    924    614           27.2         755      0         580      0.77   61.9   15.1 -0.1  2.137.763.578
469         469  469    1381.0     916.6     63       42.0    909   1375    924   1385            9.0         107      0          63      0.59   18.0    8.4 -1.1  7.073.482.428
470         470  470     403.0     913.0      5        3.0    913    401    914    406            2.5           5      0           5      1.00    5.7    0.0  0.0    196.485.623
471         471  471     421.6     919.1     71       50.3    913    412    924    430            9.5         138      0          71      0.51   23.1    8.7 -0.3  7.466.453.674
472         472  472     714.0     920.1     35       23.9    915    710    924    718            6.7          37      0          35      0.95   11.1    4.7  0.9  5.265.814.696
473         473  473    1373.0     917.0      1        0.0    917   1373    918   1374            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
474         474  474     439.2     919.8     26       17.4    918    437    923    443            5.8          27      0          26      0.96    6.8    5.0  0.5  4.558.466.454
475         475  475     483.0     918.0      1        0.0    918    483    919    484            1.1           1      0           1      1.00    0.0    0.0  0.8    0.864536741
476         476  476     481.5     919.0      2        0.0    919    481    920    483            1.6           2      0           2      1.00    2.0    0.0  0.0  1.257.507.987
477         477  477    1298.5     923.0     16       14.0    923   1291    924   1307            4.5          16      0          16      1.00   18.4    0.0  0.0  3.536.741.214
478         478  478    1321.0     923.0      3        1.0    923   1320    924   1323            2.0           3      0           3      1.00    3.3    0.0  0.0  1.571.884.984

[479 rows x 19 columns]
Index(['Unnamed: 0', 'ID', 'Center-X', 'Center-Y', 'Area', 'Perimeter',
       'Min-Y', 'Min-X', 'Max-Y', 'Max-X', '(pix)Diameter', 'ConvexArea',
       'Holes', 'FilledArea', 'Solidity', 'Major', 'Minor', 'Ori', 'Diameter'],
      dtype='object')
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.series.Series'>
0      1.681.916.933
1      1.405.265.176
2      1.351.821.086
3        196.485.623
4      1.571.884.984
5      9.258.402.556
6      9.415.591.054
7      3.073.035.144
8      7.796.549.521
9      2.200.638.978
10     1.257.507.987
11     1.076.741.214
12     2.067.028.754
13     8.771.118.211
14     1.571.884.984
15       0.864536741
16     1.571.884.984
17     3.772.523.962
18     1.941.277.955
19       0.864536741
20     2.672.204.473
21       392.971.246
22     2.515.015.974
23       0.864536741
24       0.864536741
25     1.524.728.435
26       0.864536741
27        49.514.377
28       196.485.623
29     6.444.728.435
           ...
449    1.257.507.987
450      348.172.524
451    2.357.827.476
452    1.257.507.987
453    1.398.977.636
454      0.864536741
455    1.257.507.987
456    1.257.507.987
457    2.515.015.974
458      290.798.722
459    5.501.597.444
460    4.322.683.706
461    7.545.047.923
462      0.864536741
463    1.257.507.987
464    1.556.166.134
465      0.864536741
466    1.807.667.732
467      0.864536741
468    2.137.763.578
469    7.073.482.428
470      196.485.623
471    7.466.453.674
472    5.265.814.696
473      0.864536741
474    4.558.466.454
475      0.864536741
476    1.257.507.987
477    3.536.741.214
478    1.571.884.984
Name: Diameter, Length: 479, dtype: object
C:\Users\MaiBenBen\Python36\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Traceback (most recent call last):
  File "c:\Users\MaiBenBen\.vscode\extensions\ms-python.python-2019.4.12954\pythonFiles\ptvsd_launcher.py", line 43, in <module>
    main(ptvsdArgs)
  File "c:\Users\MaiBenBen\.vscode\extensions\ms-python.python-2019.4.12954\pythonFiles\lib\python\ptvsd\__main__.py", line 410, in main
    run()
  File "c:\Users\MaiBenBen\.vscode\extensions\ms-python.python-2019.4.12954\pythonFiles\lib\python\ptvsd\__main__.py", line 291, in run_file
    runpy.run_path(target, run_name='__main__')
  File "C:\Users\MaiBenBen\Python36\lib\runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "C:\Users\MaiBenBen\Python36\lib\runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "C:\Users\MaiBenBen\Python36\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "c:\Users\MaiBenBen\Desktop\nicetable\HIST.py", line 28, in <module>
    **sns.distplot(y,kde=False,color="r")**
  File "C:\Users\MaiBenBen\Python36\lib\site-packages\seaborn\distributions.py", line 215, in distplot
    bins = min(_freedman_diaconis_bins(a), 50)
  File "C:\Users\MaiBenBen\Python36\lib\site-packages\seaborn\distributions.py", line 34, in _freedman_diaconis_bins
    h = 2 * iqr(a) / (len(a) ** (1 / 3))
  File "C:\Users\MaiBenBen\Python36\lib\site-packages\seaborn\utils.py", line 366, in iqr
    q1 = stats.scoreatpercentile(a, 25)
  File "C:\Users\MaiBenBen\Python36\lib\site-packages\scipy\stats\stats.py", line 1670, in scoreatpercentile
    return _compute_qth_percentile(sorted, per, interpolation_method, axis)
  File "C:\Users\MaiBenBen\Python36\lib\site-packages\scipy\stats\stats.py", line 1713, in _compute_qth_percentile
    return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
TypeError: can't multiply sequence by non-int of type 'float'
#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 28.8.0
Cython: None
numpy: 1.16.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.2
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.4
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.1
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.8
pymysql: 0.9.0
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None

            pd.version
            '0.24.2'


</details>
