英文:
Update dataframe if certain condition is met
问题
以下是翻译好的代码部分：
df['Column5'] = np.where(df['Column6'] == "TEST", df['Column5'] + 1, df['Column5'])
请注意，此代码将检查在"Column6"列中是否存在"TEST"条件，如果是，则将"Column5"中的值加1。
英文:
Any help would be greatly appreciated here:
Lets say I have a Pandas DataFrame such as:
Column1 Column2 Column3 Column4 Column5 Column60 aaa abb jnhs 01/01/2020 40 TEST1 aba vvv jnjh 01/01/2020 34 TEST
I am looking for the best way to be able to test if a certain condition exists, if it does to take
the number on the same row under the heading "column5" below and add 1 to that number. Then to return the full dataframe with just 1 added to the cells in col5 where col6 ="TEST".
The below is the Dataframe result I am looking for:
Column1 Column2 Column3 Column4 Column5 Column60 aaa abb jnhs 01/01/2020 41 TEST1 aba vvv jnjh 01/01/2020 35 TEST
What I have tried so far:
df['Column5'] = np.where(df['Colum6'] == "TEST", +1, "NA")
This howevever replaces the value in column5 with 1 and does not add 1 to the current value to make them 41 and 35 respectively.
Thank you for any help.
答案1
得分: 2
你可以查找满足条件的行，然后在 Column5 上加1。
mask = df["Column6"] == "TEST"df.loc[mask, "Column5"] += 1
英文:
You can look for the rows where the condition is met, then add +1 to Column5
mask = df["Column6"] == "TEST"df.loc[mask, "Column5"] += 1
答案2
得分: 1
df['Column5'] = np.where(df['Colum6'] == "TEST", df['Column5'] + 1, "NA")
英文:
This work:
df['Column5'] = np.where(df['Colum6'] == "TEST", df['Column5']+1, "NA")
答案3
得分: 1
你可以执行以下操作：
m = df["Column6"].eq("TEST")df.loc[m, "Column5"] = df.loc[m, "Column5"].add(1)
英文:
You can do:
m = df["Column6"].eq("TEST")df.loc[m, "Column5"] = df.loc[m, "Column5"].add(1)
答案4
得分: 1
以下是翻译好的部分：
The pandas documentation recommends using a mask to index the desired rows. And then use the += operator as suggested in another answer or index any other row and just add +1 or any other desired value.
import pandas as pdimport numpy as npdata = {"Index": [0,1, 2], "A": [1, 1, 1],"B": [42, 42, 42], "C": ["test","test", "NA"]}df = pd.DataFrame(data)
>>>dfIndex A B C0 0 1 42 test1 1 1 42 test2 2 1 42 NA
mask = df["C"]=="test";df.loc[mask, "B"] = df["B"]+1
>>> dfIndex A B C0 0 1 43 test1 1 1 43 test2 2 1 42 NA
英文:
The pandas documentation recommends using a mask to index the desired rows. And then use the += operator as suggested in another answer or index any other row and just add +1 or any other desired value.
import pandas as pdimport numpy as npdata = {"Index" : [0,1, 2], "A": [1, 1, 1],"B": [42, 42, 42], "C": ["test","test", "NA"]}df = pd.DataFrame(data)
>>>dfIndex A B C0 0 1 42 test1 1 1 42 test2 2 1 42 NA
mask = df["C"]=="test"df.loc[mask, "B"] = df["B"]+1
>>> dfIndex A B C0 0 1 43 test1 1 1 43 test2 2 1 42 NA
答案5
得分: 0
尝试这样做：
import pandas as pdresult = df["column6"] == "TEST"df.loc[result, "column5"] = df["column5"] + 1
英文:
Try this:
import pandas as pdresult=df["column6"]=="TEST"df.loc[result,"column5"]=df["column5"]+1
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