如何在Pandas中的DataFrame中的行上进行迭代 [英] How to iterate over rows in a DataFrame in Pandas
问题描述
我从熊猫那里有一个 DataFrame
:
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
现在我要遍历该框架的行。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']
在熊猫市有可能做到这一点吗?
Is it possible to do that in Pandas?
我发现了这个类似的问题。但这并不能给我我所需的答案。例如,建议在那里使用:
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():
但是我不明白<$ c $是什么c> row 对象是以及如何使用它。
But I do not understand what the row
object is and how I can work with it.
推荐答案
DataFrame.iterrows
是一个生成器,它同时产生索引和行(作为系列):
DataFrame.iterrows
is a generator which yields both the index and row (as a Series):
import pandas as pd
import numpy as np
df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]})
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中的DataFrame中的行上进行迭代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IT屋!