问题描述
假设我想用循环中的值创建并填充一个空数据框.
Lets say I want to create and fill an empty dataframe with values from a loop.
import pandas as pd
import numpy as np
years = [2013, 2014, 2015]
dn=pd.DataFrame()
for year in years:
df1 = pd.DataFrame({'Incidents': [ 'C', 'B','A'],
year: [1, 1, 1 ],
}).set_index('Incidents')
print (df1)
dn=dn.append(df1, ignore_index = False)
即使忽略索引为假,追加也会给出对角矩阵:
The append gives a diagonal matrix even when ignore index is false:
>>> dn
2013 2014 2015
Incidents
C 1 NaN NaN
B 1 NaN NaN
A 1 NaN NaN
C NaN 1 NaN
B NaN 1 NaN
A NaN 1 NaN
C NaN NaN 1
B NaN NaN 1
A NaN NaN 1
[9 rows x 3 columns]
应该是这样的:
>>> dn
2013 2014 2015
Incidents
C 1 1 1
B 1 1 1
A 1 1 1
[3 rows x 3 columns]
有没有更好的方法来做到这一点?有没有办法修复附加?
Is there a better way of doing this? and is there a way to fix the append?
我有熊猫版本'0.13.1-557-g300610e'
I have pandas version '0.13.1-557-g300610e'
推荐答案
import pandas as pd
years = [2013, 2014, 2015]
dn = []
for year in years:
df1 = pd.DataFrame({'Incidents': [ 'C', 'B','A'],
year: [1, 1, 1 ],
}).set_index('Incidents')
dn.append(df1)
dn = pd.concat(dn, axis=1)
print(dn)
产量
2013 2014 2015
Incidents
C 1 1 1
B 1 1 1
A 1 1 1
请注意,在循环外调用 pd.concat
once 更省时而不是在循环的每次迭代中调用 pd.concat
.
Note that calling pd.concat
once outside the loop is more time-efficientthan calling pd.concat
with each iteration of the loop.
每次调用 pd.concat
都会为新的 DataFrame 分配新空间,并且每个组件 DataFrame 中的所有数据都被复制到新的 DataFrame 中.如果你从 for 循环中调用 pd.concat
然后你最终在订单上做n**2
个副本,其中 n
是年数.
Each time you call pd.concat
new space is allocated for a new DataFrame, andall the data from each component DataFrame is copied into the new DataFrame. Ifyou call pd.concat
from within the for-loop then you end up doing on the orderof n**2
copies, where n
is the number of years.
如果您将部分 DataFrames 累积在一个列表中并调用一次 pd.concat
在列表之外,那么 Pandas 只需要执行 n
个副本即可制作 dn
.
If you accumulate the partial DataFrames in a list and call pd.concat
onceoutside the list, then Pandas only needs to perform n
copies to make dn
.
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