如何绘制字典

Question

我在绘制以下值时遇到了一些问题：

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')

但我得到了这个错误：

TypeError: ufunc 'add' 不包含带有签名匹配的循环 类型 dtype('

然后我尝试了

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

但我得到了另一个错误：

TypeError: unhashable type: 'list'

我会对频率感兴趣。

我应该怎么做才能解决它？

来自原始数据集（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 word1，132 和 144 的频率应为 2，150.5 的频率应为 3，所有其他值的频率应为 1）。

Answer 1

您可以使用matplotlib 做到这一点：

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()

Answer 2

使用pandas 和zip_longest

• Pandas 要求列的长度相同，因此zip_longest 将用None 填充空白。
• 有许多选项可以根据您想要的绘图方式来塑造数据。

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

带有注释的绘图

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')

没有注释

• hue 着色根据hue 使用的列中唯一值的数量，在本例中为word，使条形偏离中心。
  • 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被分组。
• 条形是水平的以容纳大量值。
  • 只需增加figsize 的高度。

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')