Question

我正在尝试创建一个脚本，该脚本循环遍历数据帧中的行，并根据C列中的条件从A列或B列中附加值，从而创建新列。但是，附加列中的所有行，因为我的新列包含多个值。

import pandas as pd
import numpy as np

#Loading in the csv file
filename = '35180_TRA_data.csv'
df1 = pd.read_csv(filename, sep=',', nrows=1300, skiprows=25, index_col=False, header=0)

#Calculating the B concentration using column A and a factor
B_calc = df1['A']*137.818

#The measured B concentration
B_measured = df1['B']

#Looping through the dataset, and append the B_calc values where the C column is 2, while appending the B_measured values where the C column is 1.
calculations = []

for row in df1['C']:
    if row == 2:
        calculations.append(B_calc)
    if row ==1:
        calculations.append(B_measured)

df1['B_new'] = calculations

我的新列（B_new）的值都是错误的。例如，在第一行中，它应该仅为0.00，但是它包含许多值。因此在附加中出了点问题。谁能发现这个问题？

Answer 1

B_calc和B_measured是数组。这样，您必须指定要分配的值，否则将分配整个数组。这是您的方法：

df1 = pd.DataFrame({"A":[1,3,5,7,9], "B" : [9,7,5,3,1], "C":[1,2,1,2,1]})
#Calculating the B concentration using column A and a factor
B_calc = df1['A']*137.818

#The measured B concentration
B_measured = df1['B']
#Looping through the dataset, and append the B_calc values where the C column is 2, while appending the B_measured values where the C column is 1.
calculations = []

for index, row in df1.iterrows():
    if row['C'] == 2:
        calculations.append(B_calc[index])
    if row['C'] ==1:
        calculations.append(B_measured[index])

df1['B_new'] = calculations

但是在行上进行迭代是一种不好的做法，因为它需要很长时间。更好的方法是使用熊猫面具，这是它的工作方式：

mask_1 = df1['C'] == 1
mask_2 = df1['C'] == 2

df1.loc[mask_1, 'C'] = df1[mask_1]['A']*137.818
df1.loc[mask_2, 'C'] = df1[mask_2]['B']

在带有熊猫的新列中获取不需要的值附加在for循环中

1 个答案: