Pandas：根据其他列值有条件地替换值
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/52224142/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas: Conditionally replace values based on other columns values
提问by Martin Müsli
I have a dataframe (df) that looks like this:
我有一个如下所示的数据框 (df)：
environment event
time
2017-04-28 13:08:22 NaN add_rd
2017-04-28 08:58:40 NaN add_rd
2017-05-03 07:59:35 test add_env
2017-05-03 08:05:14 prod add_env
...
Now my goal is for each add_rdin the eventcolumn, the associated NaN-value in the environmentcolumn should be replaced with a string RD.
现在我的目标是对于列中的每个add_rd，event列中关联的NaN-valueenvironment应该替换为 string RD。
environment event
time
2017-04-28 13:08:22 RD add_rd
2017-04-28 08:58:40 RD add_rd
2017-05-03 07:59:35 test add_env
2017-05-03 08:05:14 prod add_env
...
What I did so far
到目前为止我做了什么
I stumbled across df['environment'] = df['environment].fillna('RD')which replaces everyNaN(which is not what I am looking for), pd.isnull(df['environment'])which is detecting missing values and np.where(df['environment'], x,y)which seems to be what I want but isn't working. Furthermore did I try this:
我偶然发现df['environment'] = df['environment].fillna('RD')哪个替换了每个NaN（这不是我要找的），pd.isnull(df['environment'])哪个正在检测缺失值，np.where(df['environment'], x,y)哪个似乎是我想要的但不起作用。此外，我是否尝试过：
import pandas as pd
for env in df['environment']:
if pd.isnull(env) and df['event'] == 'add_rd':
env = 'RD'
The indexes are missing or some kind of iterator to access the equivalent value in the eventcolumn.
And I tried this:
缺少索引或某种迭代器来访问event列中的等效值。
我试过这个：
df['environment'] = np.where(pd.isnull(df['environment']), df['environment'] = 'RD', df['environment'])
SyntaxError: keyword can't be an expression
which obviously didn't worked.
这显然没有用。
I took a look at several questions but couldn't build on the suggestions in the answers. Black's questionSimon's questionszli's questionJan Willems Tulp's question
我查看了几个问题，但无法建立在答案中的建议之上。Black 的问题Simon 的问题szli 的问题Jan Willems Tulp 的问题
So, how do I replace a value in a column based on another columns values?
那么，如何根据另一列值替换列中的值？
采纳答案by jpp
Now my goal is for each add_rd in the event column, the associated NaN-value in the environment column should be replaced with a string RD.
现在我的目标是对于事件列中的每个 add_rd，应将环境列中的关联 NaN 值替换为字符串 RD。
As per @Zero's comment, use pd.DataFrame.locand Boolean indexing:
根据@Zero 的评论，使用pd.DataFrame.loc布尔索引：
df.loc[df['event'].eq('add_rd') & df['environment'].isnull(), 'environment'] = 'RD'
回答by CT Zhu
回答by Herc01
Here it is:
这里是：
df['environment']=df['environment'].fillna('RD')
回答by Naga kiran
if you want to replace just 'add_rd' with 'RD', this can be useful to you
如果您只想将 'add_rd' 替换为 'RD'，这对您很有用
keys_to_replace = {'add_rd':'RD','add_env':'simple'}
df['environment'] = df.groupby(['event'])['environment'].fillna(keys_to_replace['add_rd'])
df
output:
输出：
environment event
0 RD add_rd
1 RD add_rd
2 test add_env
3 prod add_env
if you have many values to replace based on event, then you may need to follow groupby with 'event' column values
如果您有许多要根据事件替换的值，那么您可能需要使用“事件”列值跟随 groupby
keys_to_replace = {'add_rd':'RD','add_env':'simple'}
temp = df.groupby(['event']).apply(lambda x: x['environment'].fillna(keys_to_replace[x['event'].values[0]]))
temp.index = temp.index.droplevel(0)
df['environment'] = temp.sort_index().values
output:
输出：
environment event
0 RD add_rd
1 RD add_rd
2 test add_env
3 prod add_env
