Python 根据长度将数据帧拆分为相对均匀的块
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Split dataframe into relatively even chunks according to length
提问by YKY
I have to create a function which would split provided dataframe into chunks of needed size. For instance if dataframe contains 1111 rows, I want to be able to specify chunk size of 400 rows, and get three smaller dataframes with sizes of 400, 400 and 311. Is there a convenience function to do the job? What would be the best way to store and iterate over sliced dataframe?
我必须创建一个函数，它将提供的数据帧拆分为所需大小的块。例如，如果数据帧包含 1111 行，我希望能够指定 400 行的块大小，并获得三个大小分别为 400、400 和 311 的较小数据帧。是否有方便的功能来完成这项工作？存储和迭代切片数据帧的最佳方法是什么？
Example DataFrame
示例数据帧
import numpy as np
import pandas as pd
test = pd.concat([pd.Series(np.random.rand(1111)), pd.Series(np.random.rand(1111))], axis = 1)
采纳答案by sinhrks
You can take the floor divisionof a sequence up to the amount of rows in the dataframe, and use it to groupbysplitting the dataframe into equally sized chunks:
您可以将序列的楼层划分达到数据帧中的行数，并使用它来groupby将数据帧拆分为大小相等的块：
n = 400
for g, df in test.groupby(np.arange(len(test)) // n):
print(df.shape)
# (400, 2)
# (400, 2)
# (311, 2)
回答by Scott Boston
A more pythonic way to break large dataframes into smaller chunks based on fixed number of rows is to use list comprehension:
基于固定行数将大数据帧分解成更小的块的一种更 Pythonic 的方法是使用列表理解：
n = 400 #chunk row size
list_df = [test[i:i+n] for i in range(0,test.shape[0],n)]
[i.shape for i in list_df]
Output:
输出：
[(400, 2), (400, 2), (311, 2)]
