pandas 使用循环填充空的python数据框
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Filling empty python dataframe using loops
提问by ccsv
Lets say I want to create and fill an empty dataframe with values from a loop.
假设我想用循环中的值创建并填充一个空数据框。
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)
The append gives a diagonal matrix even when ignore index is false:
即使忽略索引为假，附加也会给出对角矩阵：
>>> 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]
It should look like this:
它应该是这样的：
>>> dn
2013 2014 2015
Incidents
C 1 1 1
B 1 1 1
A 1 1 1
[3 rows x 3 columns]
Is there a better way of doing this? and is there a way to fix the append?
有没有更好的方法来做到这一点？有没有办法修复附加？
I have pandas version '0.13.1-557-g300610e'
我有Pandas版本'0.13.1-557-g300610e'
回答by unutbu
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)
yields
产量
2013 2014 2015
Incidents
C 1 1 1
B 1 1 1
A 1 1 1
Note that calling pd.concatonceoutside the loop is more time-efficient
than calling pd.concatwith each iteration of the loop.
请注意，在循环外调用pd.concat一次比pd.concat在循环的每次迭代中调用更省时。
Each time you call pd.concatnew 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.concatfrom within the for-loop then you end up doing on the order
of n**2copies, where nis the number of years.
每次调用时都会pd.concat为一个新的DataFrame 分配新的空间，并且每个组件DataFrame 中的所有数据都被复制到新的DataFrame 中。如果您pd.concat从 for 循环内调用，那么您最终会按照n**2副本的顺序进行操作，其中n是年数。
If you accumulate the partial DataFrames in a list and call pd.concatonce
outside the list, then Pandas only needs to perform ncopies to make dn.
如果将部分 DataFrame 累积在一个列表中并在列表pd.concat外调用一次，那么 Pandas 只需要执行n复制即可dn。
回答by Donbeo
As far as I know you should avoid to add line by line to the dataframe due to speed issue
据我所知，由于速度问题，您应该避免逐行添加到数据帧
What I usually do is:
我通常做的是：
l1 = []
l2 = []
for i in range(n):
compute value v1
compute value v2
l1.append(v1)
l2.append(v2)
d = pd.DataFrame()
d['l1'] = l1
d['l2'] = l2
