Python中matplotlib.pyplot.hist显示x must be 1D or 2D

求问大神：
用来做社会网络分析中的直方图的，代码如下：

 import networkx as net
import matplotlib.pyplot as plot

def sorted_map(map):
    ms=sorted(map.items(),key=lambda k:(-k[1],k[0]))
    return ms

g=net.read_pajek('russians.net')
deg=net.degree(g)

ds=sorted_map(deg)
print(ds[0:9])

h=plot.hist(deg.values(),100)
plot.loglog(h[1][1:],h[0])

结果运行之后显示的是：

 Traceback (most recent call last):
  File "degreeCentrality.py", line 14, in <module>
    h=plot.hist(deg.values())
  File "C:\Users\19665\AppData\Local\Programs\Python\Python35\lib\site-packages\matplotlib\pyplot.py", line 2965, in his
t
    stacked=stacked, data=data, **kwargs)
  File "C:\Users\19665\AppData\Local\Programs\Python\Python35\lib\site-packages\matplotlib\__init__.py", line 1819, in i
nner
    return func(ax, *args, **kwargs)
  File "C:\Users\19665\AppData\Local\Programs\Python\Python35\lib\site-packages\matplotlib\axes\_axes.py", line 5935, in
 hist
    x = _normalize_input(x, 'x')
  File "C:\Users\19665\AppData\Local\Programs\Python\Python35\lib\site-packages\matplotlib\axes\_axes.py", line 5877, in
 _normalize_input
    "{ename} must be 1D or 2D".format(ename=ename))
ValueError: x must be 1D or 2D

找了一下午了，都没找到相关的解决方法，跪求指点！

只需要将倒数第二行的h=plot.hist(deg.values(),100)改为h=plot.hist(list(deg.values()),100)就可以了，python 3.x

求解答：

#RFM分析(假设现在是2017/11/25)
from datetime import datetime 

#最近一次购买距离现在的天数
data['date']=pd.to_datetime(data['date'])
recent_buy=data[data.behavior=='购买'].groupby('user_id')['date'].apply(lambda x: datetime(2017,12,25)-x.sort_values().iloc[-1]).reset_index().rename(columns={'date':'recent'})
recent_buy.recent=recent_buy.recent.map(lambda x: x.days)

#购买频率（购买次数）
fred_buy=data[data.behavior=='购买'].groupby('user_id').date.count().reset_index().rename(columns={'date':'fred'})
#rfm

recent_fred=pd.merge(recent_buy,fred_buy,on='user_id')
#为实现自动细分，将使用R和F变量的80%分位数
quantiles=recent_fred.quantile(q=[0.8])

recent_fred['R']=np.where(recent_fred['recent']<=int(quantiles.recent.values),2,1)
recent_fred['F']=np.where(recent_fred['fred']<=int(quantiles.fred.values),1,2)
recent_fred['rfm']=recent_fred.R.map(str)+recent_fred.F.map(str)

#打标签，时间越近次数越多越重要
labels = {'12':'流失客户','22': '明星客户','11':'次要客户','21':'新客户'}
recent_fred['labels']= recent_fred['rfm'].apply(lambda x: labels[x])
rfm=pd.DataFrame(recent_fred['labels'].value_counts())

plt.figure(figsize=(10,7))
plt.pie(rfm.values,labels=rfm.index,explode=(0.05,0,0,0),colors=['red','yellowgreen','gold','orange'],autopct='%1.1f%%',textprops={'fontsize':15})
plt.legend()
plt.axis('equal')
plt.title('客户类群占比',fontsize=18)