[英]Using Python Pandas, can I replace values of one column in a df based on another column only when a "nan" value does not exist?
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假设我有一个这样的数据框：
import pandas as pd
data1 = {
"date": [1, 2, 3],
"height": [420.3242, 380.1, 390],
"height_new": [300, 380.1, "nan"],
"duration": [50, 40, 45],
"feeling" : ["great","good","great"]
}
df = pd.DataFrame(data1)
我想用“height_new”列更新“height”列，但不是在“height_new”的值为“nan”时更新。 关于如何以 Pythonic 方式执行此操作的任何提示？
我有一个粗略的代码可以完成工作但感觉很笨拙（代码行太多）。
for x, y in zip(df['height'], df['height_new']) :
if y != 'nan':
df['height'].replace(x, y, inplace= True)
x = y
您可以将pandas.Series.where与pandas.Series.notna一起使用：
df["height"] = df["height_new"].where(df["height_new"].notna(), df["height"])
print(df)
date height height_new duration feeling
0 1 300.0 300.0 50 great
1 2 380.1 380.1 40 good
2 3 390.0 NaN 45 great
注意：如果"nan"是文字字符串，请改用它：
df["height"] = df["height_new"].where(df["height_new"].ne("nan"), df["height"])
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