数据框的条件操作

Question

我的目标是根据考虑其他列值的条件向数据框添加一列。

我创建了一个生成相同错误的简单示例：

numbers = {'A': [1,2,3,4,5], "B":[2,4,3,3,2]}
df = pd.DataFrame(numbers)

if df.A - df.B > 0:
    df["C"] = df.B*5
else: df["C"] = 0

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我确信解决方案很简单，但我是初学者。感谢支持

Answer 1

c = []

for lab, row in df.iterrows():
    curr = 0
    if row['A'] > row['B']:
        curr = row['B'] * 5

    c.append(curr)

df['C'] = c

Answer 2

你可以这样做：

df["C"] = df.A - df.B

# First turns negative values into 0s
df["C"].mask(df["C"] <= 0, 0, inplace=True)

# Then changes the value as needed if C > 0.
df["C"].mask(df["C"] > 0, df["B"]*5, inplace=True)

Answer 3

#Import pandas module
import pandas as pd

#Lists of data 
list_A = [1,2,3,4,5]
list_B = [2,4,3,3,2]

#Define a dictionary containing lists of data
dictionary = {'A':  list_A,
        'B': list_B}

#Convert the dictionary into DataFrame
data = pd.DataFrame(dictionary)
data

#New list
data_diff = data.A - data.B 

new_list=[]
for i in data_diff:
    if i > 0:
        new_list.append(i*5)
    else:
        new_list.append(0)

#New dataframe
new_dictionary = {'A':  [1,2,3,4,5],
        'B': [2,4,3,3,2],
        'C': new_list}

new_data=pd.DataFrame(new_dictionary)
new_data

注意事项：这是我的非常简单的版本。当然，还有许多其他更智能、更“pythonic”的版本。最后，我觉得this教程网站可以帮到你。

Answer 4

你可以使用 numpy 的 where:

df["C"] = np.where(df["A"] > df["B"], df["B"]*5, 0)

数据框的条件操作

conditional operation on dataframe

python

lambda

numpy

dataframe

pandas