在 for 循环中使用 pandas .append [英] Using pandas .append within for loop

问题描述

我在 for 循环中将行附加到 Pandas DataFrame，但最后数据框始终为空.我不想将行添加到数组然后调用 DataFrame 构造函数，因为我的实际 for 循环处理大量数据.我也试过 pd.concat 没有成功.任何人都可以突出显示我缺少什么以使 append 语句起作用吗?这是一个虚拟示例:

I am appending rows to a pandas DataFrame within a for loop, but at the end the dataframe is always empty. I don't want to add the rows to an array and then call the DataFrame constructer, because my actual for loop handles lots of data. I also tried pd.concat without success. Could anyone highlight what I am missing to make the append statement work? Here's a dummy example:

import pandas as pd
import numpy as np

data = pd.DataFrame([])

for i in np.arange(0, 4):
    if i % 2 == 0:
        data.append(pd.DataFrame({'A': i, 'B': i + 1}, index=[0]), ignore_index=True)
    else:
        data.append(pd.DataFrame({'A': i}, index=[0]), ignore_index=True)

print data.head()

Empty DataFrame
Columns: []
Index: []
[Finished in 0.676s]

推荐答案

您需要将变量 data 设置为等于附加的数据框.与 python 列表上的 append 方法不同，pandas append 不会就地发生

You need to set the the variable data equal to the appended data frame. Unlike the append method on a python list the pandas append does not happen in place

import pandas as pd
import numpy as np

data = pd.DataFrame([])

for i in np.arange(0, 4):
    if i % 2 == 0:
        data = data.append(pd.DataFrame({'A': i, 'B': i + 1}, index=[0]), ignore_index=True)
    else:
        data = data.append(pd.DataFrame({'A': i}, index=[0]), ignore_index=True)

print(data.head())

   A    B
0  0  1.0
1  2  3.0
2  3  NaN

注意:此答案旨在回答提出的问题.然而，这并不是组合大量数据帧的最佳策略.有关更优化的解决方案，请查看下面的亚历山大的回答

NOTE: This answer aims to answer the question as it was posed. It is not however the optimal strategy for combining large numbers of dataframes. For a more optimal solution have a look at Alexander's answer below

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