如何绘制字典

Question

我在绘制以下值时遇到一些问题：

my_dict={'word1': ['31', '131', '2'], 'word2': ['42', '33', '154', '21']}

我所做的是

plt.bar(my_dict.keys(), my_dict.values(), color='g')

但我有一个错误：

TypeError:ufunc“add”不包含签名类型与dtype匹配的循环（'

那么我已经试过了

plt.plot(*zip(*sorted(my_dict.items())))
plt.show()

但我还有另外一个错误：

TypeError:不可损坏的类型：“列表”

我对频率感兴趣。

我该怎么做才能修好它？

从原始数据集（因为我在复制代码时遇到了一些错误）：

my_dict = defaultdict(list)

print({ k : v for k, v in my_dict.items() })

输出：

{'word1': ['122', '121.2', '132', '132', '144', '144.5', '144', '150', '150,5', '150,5', '150,5'], 'word2': ['230', '230', '230', '230'], 'word3': ['542', '542', '540'], 'word4': ['134', '134']}

我需要绘制每个单词中值的频率（例如，对于单词1，132和144的频率应为2，然后150.5的频率应为3，所有其他值的频率应为1）。

Answer 1

• Pandas要求列的长度相同，因此zip\u longest将用None填充空格
• 根据您希望打印数据的方式，有许多选项可以塑造数据的形状

import pandas as pd
from itertools import zip_longest
import matplotlib.pyplot as plt

# data
d = {'word1': ['122', '121.2', '132', '132', '144', '144.5', '144', '150', '150.5', '150.5', '150.5'], 'word2': ['230', '230', '230', '230'], 'word3': ['542', '542', '540'], 'word4': ['134', '134']}

# since the values lists are uneven
cols = d.keys()
val = list(zip_longest(*d.values()))

# dataframe
df = pd.DataFrame(val, columns=cols, dtype=float)

    word1  word2  word3  word4
0   122.0  230.0  542.0  134.0
1   121.2  230.0  542.0  134.0
2   132.0  230.0  540.0    NaN
3   132.0  230.0    NaN    NaN
4   144.0    NaN    NaN    NaN
5   144.5    NaN    NaN    NaN
6   144.0    NaN    NaN    NaN
7   150.0    NaN    NaN    NaN
8   150.5    NaN    NaN    NaN
9   150.5    NaN    NaN    NaN
10  150.5    NaN    NaN    NaN

ax = df.plot.bar()

f = [df[c].value_counts().to_dict() for c in df.columns]  # list of list of value counts
f = dict(kv for d in f for kv in d.items())  # this will break if the values for each word aren't unique

for p in ax.patches:

    if p.get_height() > 0:

        # add value at top of bar
        ax.annotate(format(p.get_height(), '.1f'),
                    (p.get_x() + p.get_width() / 2., p.get_height() + 10),
                    ha = 'center', va = 'center', fontsize=9, rotation=90,
                    xytext = (0, 10), textcoords = 'offset points')

        # add frequency of value at center of bar
        ax.annotate(format(f[p.get_height()], '.0f'),
            (p.get_x() + p.get_width() / 2., p.get_height() / 2),
            ha = 'center', va = 'center', fontsize=9, rotation=0,
            xytext = (0, 10), textcoords = 'offset points')

tdf = df.T  # transpose dataframe df

ax = tdf.plot.bar()

f = [df[c].value_counts().to_dict() for c in df.columns]  # list of list of value counts
f = dict(kv for d in f for kv in d.items())  # this will break if the values for each word aren't unique

for p in ax.patches:

    if p.get_height() > 0:

        # add value at top of bar
        ax.annotate(format(p.get_height(), '.1f'),
                    (p.get_x() + p.get_width() / 2., p.get_height() + 10),
                    ha = 'center', va = 'center', fontsize=9, rotation=90,
                    xytext = (0, 10), textcoords = 'offset points')

        # add frequency of value at center of bar
        ax.annotate(format(f[p.get_height()], '.0f'),
            (p.get_x() + p.get_width() / 2., p.get_height() / 2),
            ha = 'center', va = 'center', fontsize=9, rotation=0,
            xytext = (0, 10), textcoords = 'offset points')

• 由hue着色会根据列中由hue，word使用的唯一值的数量将条形图偏离中心。
  • 在下面的示例中，所有四个单词都包含值150.5，因此您可以在图中看到它们分组。
  • 只需增加figsize高度即可

  import seaborn as sns
  
  d = {'word1': ['122', '121.2', '132', '132', '144', '144.5', '144', '150', '150.5', '150.5', '150.5'], 'word2': ['230', '230', '230', '230', '150.5'], 'word3': ['542', '542', '540', '150.5'], 'word4': ['134', '134', '150.5']}
  
  cols = d.keys()
  val = list(zip_longest(*d.values()))
  
  # dataframe
  df = pd.DataFrame(val, columns=cols, dtype=float)
  
  # convert from wide to long
  df['id'] = df.index
  dfl = pd.wide_to_long(df, stubnames='word', j='x', i='id').reset_index().rename(columns={'word': 'v', 'x': 'word'}).dropna()
  
  # groupby for frequency counts
  dflg = dfl.groupby('word').agg({'v': 'value_counts'}).rename(columns={'v': 'freq_count'}).reset_index().sort_values('v')
  
  # plot
  plt.figure(figsize=(6, 10))
  p = sns.barplot(y='v', x='freq_count', data=dflg, hue='word', orient='h')
  

  https://imgs.xnip.cn/cj/n/86/d25b3012-c5cd-4005-a6b8-7ace79cbea9b.png" width="100%" height="100%" />

Answer 2

import matplotlib.pyplot as plt
from numpy import random

mydict={'word1': ['122', '121.2', '132', '132', '144', '144.5', '144', '150', '150,5', '150,5', '150,5'], 'word2': ['230', '230', '230', '230'], 'word3': ['542', '542', '540'], 'word4': ['134', '134']}


for k,l in mydict.items():
    labeled = False
    c=random.rand(3,)
    for v in l:
        if labeled:
            plt.bar(v,len([d for d in l if d==v]),color=c)
        else:
            plt.bar(v,len([d for d in l if d==v]),label=k,color=c)
            labeled = True

plt.legend()
plt.show()