我有两个数据帧，一个是 topic_ ，它是目标数据帧，而 tw 是源数据帧。 topic_ 是一个主题词矩阵，其中每个单元格存储一个词出现在特定主题中的概率。我已经使用 numpy.zeros 将 topic_ 数据帧初始化为零。 tw 数据框的示例-
print(tw)
topic_id word_prob_pair
0 0 [(customer, 0.061703717964), (team, 0.01724444...
1 1 [(team, 0.0260560163563), (customer, 0.0247838...
2 2 [(customer, 0.0171786268847), (footfall, 0.012...
3 3 [(team, 0.0290787264225), (product, 0.01570401...
4 4 [(team, 0.0197917953222), (data, 0.01343226630...
5 5 [(customer, 0.0263740639141), (team, 0.0251677...
6 6 [(customer, 0.0289764173735), (team, 0.0249938...
7 7 [(client, 0.0265082412402), (want, 0.016477447...
8 8 [(customer, 0.0524006965405), (team, 0.0322975...
9 9 [(generic, 0.0373422774996), (product, 0.01834...
10 10 [(customer, 0.0305256248248), (team, 0.0241559...
11 11 [(customer, 0.0198707090364), (ad, 0.018516805...
12 12 [(team, 0.0159852971954), (customer, 0.0124540...
13 13 [(team, 0.033444510469), (store, 0.01961003290...
14 14 [(team, 0.0344793243818), (customer, 0.0210975...
15 15 [(team, 0.026416114692), (customer, 0.02041691...
16 16 [(campaign, 0.0486186973667), (team, 0.0236024...
17 17 [(customer, 0.0208270072145), (branch, 0.01757...
18 18 [(team, 0.0280889397541), (customer, 0.0127932...
19 19 [(team, 0.0297011415217), (customer, 0.0216007...
我的 topic_ dataframe 的大小为 num_topics(即 20)乘以 number_of_unique_words(在 tw dataframe 中)
以下是我用来替换 topic_ 数据帧中每个值的代码
for each_topic in range(num_topics):
a = tw['word_prob_pair'].iloc[each_topic]
for word, prob in a:
topic_.set_value(each_topic, word, prob)
有没有更好的方法来完成这项任务？
最佳答案
您可以将list comprehension 与DataFrame 构造函数一起使用，最后将NaN 替换为0 by fillna :
df = pd.DataFrame({'word_prob_pair':[
[('customer', 0.061703717964), ('team', 0.01724444)],
[('team', 0.0260560163563), ('customer', 0.0247838)],
[('customer', 0.0171786268847), ('footfall', 0.012)],
[('team', 0.0290787264225), ('product', 0.01570401)],
[('team', 0.0197917953222), ('data', 0.01343226630)],
[('customer', 0.0263740639141), ('team', 0.0251677)],
[('customer', 0.0289764173735), ('team', 0.0249938)],
[('client', 0.0265082412402), ('want', 0.016477447)]
] })
print (df)
word_prob_pair
0 [(customer, 0.061703717964), (team, 0.01724444)]
1 [(team, 0.0260560163563), (customer, 0.0247838)]
2 [(customer, 0.0171786268847), (footfall, 0.012)]
3 [(team, 0.0290787264225), (product, 0.01570401)]
4 [(team, 0.0197917953222), (data, 0.0134322663)]
5 [(customer, 0.0263740639141), (team, 0.0251677)]
6 [(customer, 0.0289764173735), (team, 0.0249938)]
7 [(client, 0.0265082412402), (want, 0.016477447)]
df1 = pd.DataFrame([dict(x) for x in df.word_prob_pair])
df1 = df1.fillna(0)
print (df1)
client customer data footfall product team want
0 0.000000 0.061704 0.000000 0.000 0.000000 0.017244 0.000000
1 0.000000 0.024784 0.000000 0.000 0.000000 0.026056 0.000000
2 0.000000 0.017179 0.000000 0.012 0.000000 0.000000 0.000000
3 0.000000 0.000000 0.000000 0.000 0.015704 0.029079 0.000000
4 0.000000 0.000000 0.013432 0.000 0.000000 0.019792 0.000000
5 0.000000 0.026374 0.000000 0.000 0.000000 0.025168 0.000000
6 0.000000 0.028976 0.000000 0.000 0.000000 0.024994 0.000000
7 0.026508 0.000000 0.000000 0.000 0.000000 0.000000 0.016477
关于python - 替换 Pandas 数据框中每个单元格值的有效方法，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42339649/