问题描述
我有一个来自 Pandas 的 DataFrame:
I have a DataFrame from Pandas:
import pandas as pd
inp = [{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}]
df = pd.DataFrame(inp)
print df
输出:
c1 c2
0 10 100
1 11 110
2 12 120
现在我想遍历这个框架的行.对于每一行，我希望能够通过列名访问其元素(单元格中的值).例如:
Now I want to iterate over the rows of this frame. For every row I want to be able to access its elements (values in cells) by the name of the columns. For example:
for row in df.rows:
print row['c1'], row['c2']
在 Pandas 中可以做到这一点吗?
Is it possible to do that in Pandas?
我发现了这个 类似问题.但它并没有给我我需要的答案.例如，这里建议使用:
I found this similar question. But it does not give me the answer I need. For example, it is suggested there to use:
for date, row in df.T.iteritems():
或
for row in df.iterrows():
但我不明白 row 对象是什么以及如何使用它.
But I do not understand what the row object is and how I can work with it.
推荐答案
DataFrame.iterrows 是生成索引和行(作为系列)的生成器:
DataFrame.iterrows is a generator which yields both the index and row (as a Series):
import pandas as pd
df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]})
df = df.reset_index() # make sure indexes pair with number of rows
for index, row in df.iterrows():
print(row['c1'], row['c2'])
10 100
11 110
12 120
这篇关于如何在 Pandas 中遍历 DataFrame 中的行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！