使用循环填充空python数据帧 [英] Filling empty python dataframe using loops
问题描述
假设我想用循环中的值创建和填充空数据框。
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index为false,append也会给出一个对角矩阵:
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]
它应如下所示:
>>> dn
2013 2014 2015
Incidents
C 1 1 1
B 1 1 1
A 1 1 1
[3 rows x 3 columns]
有更好的方法吗?有没有办法解决附加问题?
Is there a better way of doing this? and is there a way to fix the append?
我有pandas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)
收益率
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
一次更多时间 - 比循环的每次迭代调用 pd.concat
高效
。
Note that calling pd.concat
once outside the loop is more time-efficient
than calling pd.concat
with each iteration of the loop.
每次调用 pd.concat
为新分配新空间DataFrame和
将每个组件DataFrame中的所有数据复制到新的DataFrame中。如果
你从for循环中调用 pd.concat
那么你最终会在 n *的订单
副本,其中
上做* 2 n
是年数。
Each time you call pd.concat
new space is allocated for a new DataFrame, and
all the data from each component DataFrame is copied into the new DataFrame. If
you call pd.concat
from within the for-loop then you end up doing on the order
of n**2
copies, where n
is the number of years.
如果你累积列表中的部分DataFrame并在列表外调用 pd.concat
一次
,然后Pandas只需要执行 n
要复制的副本 dn
。
If you accumulate the partial DataFrames in a list and call pd.concat
once
outside the list, then Pandas only needs to perform n
copies to make dn
.
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