使用循环填充空的python数据框

Filling empty python dataframe using loops(使用循环填充空的python数据框)

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

<小时>

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-efficient than 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, 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 once outside the list, then Pandas only needs to perform n copies to make dn.

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