英文:
apply function in Python throws the error - object not iterable
问题
我在应用函数时遇到了错误。对于每个条件，我应该在当前行中参考前一行的值以及当前行中的下一行值。如果我有多个引用前一行值的情况，那么就会出现错误。请建议我以不同的方式解决这个问题。
import pandas as pddata = {'A': [1, 1, 1, 1, 1],'B': [6, 7, 8, 9, 10]}df = pd.DataFrame(data)# 定义一个自定义函数，使用前一行和后一行的值def process_row(row):previous_row = df.shift(1).loc[row.name]next_row = df.shift(-1).loc[row.name]# 访问前一行和后一行的值prev_value_A = previous_row['A']prev_value_B = previous_row['B']next_value_A = next_row['A']next_value_B = next_row['B']# 使用前一行和后一行的值进行计算result = {}result['x'] = ((prev_value_B - prev_value_A) - (next_value_B - next_value_A))return result# 将该函数应用于DataFrame中的每一行df = df.apply(process_row, axis=1)print(df)
英文:
I am getting an error while applying a function. For each condition, I am supposed to refer to the previous row values and also the next values while present in the current row. If i have more than one reference of previous row values, then it is giving an error. Please advise how do i fix this in a different way.
import pandas as pddata = {'A': [1, 1, 1, 1, 1],'B': [6, 7, 8, 9, 10]}df = pd.DataFrame(data)# Define a custom function that uses values from the previous and next rowsdef process_row(row):previous_row = df.shift(1).loc[row.name]next_row = df.shift(-1).loc[row.name]# Access values from previous and next rowsprev_value_A = previous_row['A']prev_value_B = previous_row['B']next_value_A = next_row['A']next_value_B = next_row['B']# Perform calculations using previous and next valuesresult = {}result['x'] = ((prev_value_B - prev_value_A) - (next_value_B - next_value_A))return result# Apply the function to each row in the DataFramedf = df.apply(process_row, axis=1)print(df)
答案1
得分: 2
你不应该使用函数和 apply 来实现这个，直接使用 shift 来向量化你的计算：
s = df['B'] - df['A']df['x'] = s.shift() - s.shift(-1)
输出：
A B x0 1 6 NaN1 1 7 -2.02 1 8 -2.03 1 9 -2.04 1 10 NaN
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