您可以使用.iloc[]函数来选择所需的行和列。

dataframe.iloc[5:15,6:15]

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这应该选择第5-14行和第6-14列。不确定数字是否正确，但我认为这种方法是你要找的。
edit：将.loc[]更改为.iloc[]，因为我们使用了索引值，并对其进行了一些清理
下面是代码来覆盖整个框架

#df = big data frame
shape = (10,10) #shape of matrix to be analyzed, here is 10x10
step = 1 #step size, iterate over every number
        #or
step = 10 #step size, iterate block by block
        #keep in mind, iterating by block will leave some data out at the end of the rows and columns
#you can set step = shape if you are working  with a matrix that isn't square, just be sure to change step in the code below to step[0] and step[1] respectively 
for row in range( 0, len(df[0]) - shape[0]+1, step): #number of rows of big dataframe - number of rows of matrix to be analyzed 
   for col in range(0, len(df.iloc[0,:]) - shape[1]+1, step): #number of columns of big dataframe - number of columns of matrix to be analized 
        matrix = df.iloc[row:shape[0]+row, col:shape[1]+col] #slice out matrix and set it equal to 'matrix'
        #analize matrix here

型
这基本上和@dafmedinama说的一样，我只是添加了更多的注解和简化了矩阵的形状，如果你不想在每次移动矩阵时都忽略每个数字，我还添加了一个步长变量。

Be sub_rows和sub_rows是要提取的内存的尺寸：

import pandas as pd

sub_rows = 10 # Amount of rows to be extracted
sub_cols = 3  # Amount of columns to be extracted

if sub_rows > len(df.index):
    print("Defined sub dataframe rows are more than in the original dataframe")
elif sub_cols > len(df.columns):
    print("Defined sub dataframe columns are more than in the original dataframe")
else:
    for i in range(0,len(df.index)-sub_rows):
        for j in range(0, len(df.columns)):
            d.iloc[i:i+sub_rows, j:j+sub_cols] # Extracted dataframe
            # Put here the code you need for your analysis

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