Python 按索引遍历数据帧
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Iterate through a dataframe by index
提问by Stacey
I have a dataframe called staticData which looks like this:
我有一个名为 staticData 的数据框，它看起来像这样：
narrow_sector broad_sector country exchange \
unique_id
BBG.MTAA.STM.S Semiconductors Technology CH MTAA
BBG.MTAA.CNHI.S Machinery-Diversified Industrial GB MTAA
BBG.MTAA.FCA.S Auto Manufacturers Consumer Cyclical GB MTAA
BBG.MTAA.A2A.S Electric Utilities IT MTAA
BBG.MTAA.ACE.S Electric Utilities IT MTAA
I am trting to iterate through the dataframe row by row picking out two bits of information the index (unique_id) and the exchange. I am having a problem iterating on the index. Please see my code:
我正在逐行遍历数据帧，从中挑选出两位信息，即索引 (unique_id) 和交换。我在迭代索引时遇到问题。请看我的代码：
for i, row in staticData.iterrows():
unique_id = staticData.ix[i]
exchange = row['exchange']
I have tried unique_id = row['unique_id'], but can't get it to work...
我试过 unique_id = row['unique_id']，但无法让它工作......
I am trying to return say for row1
我正在尝试为 row1 返回 say
unique_id = BBG.MTAA.STM.S
exchange = MTAA
采纳答案by EdChum
You want the following:
您需要以下内容：
for i, row in staticData.iterrows():
unique_id = i
exchange = row['exchange']
i will be the index label value
我将是索引标签值
Example:
例子：
In [57]:
df = pd.DataFrame(np.random.randn(5,3), index=list('abcde'), columns=list('fgh'))
df
Out[57]:
f g h
a -0.900835 -0.913989 -0.624536
b -0.854091 0.286364 -0.869539
c 1.090133 -0.771667 1.258372
d -0.721753 -0.329211 0.479295
e 0.520786 0.273722 0.824172
In [62]:
for i, row in df.iterrows():
print('index: ', i, 'col g:', row['g'])
index: a col g: -0.913988608754
index: b col g: 0.286363847188
index: c col g: -0.771666520074
index: d col g: -0.329211394286
index: e col g: 0.273721527592
回答by knagaev
May be more pandasian way?
可能是更熊猫的方式？
staticData.apply((lambda x: (x.name, x['exchange'])), axis=1)
