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[英]How to replace values in a dataframe with values in another dataframe based on certain condition?
[英]How to replace Specific values of a particular column in Pandas Dataframe based on a certain condition?
我有一個Pandas數據框,其中包含學生和他們獲得的分數百分比。 有些學生的分數顯示大於100%。 顯然這些值是不正確的,我想用NaN替換大於100%的所有百分比值。
我已經嘗試了一些代碼,但不能完全得到我想要的東西。
import numpy as np
import pandas as pd
new_DF = pd.DataFrame({'Student' : ['S1', 'S2', 'S3', 'S4', 'S5'],
'Percentages' : [85, 70, 101, 55, 120]})
# Percentages Student
#0 85 S1
#1 70 S2
#2 101 S3
#3 55 S4
#4 120 S5
new_DF[(new_DF.iloc[:, 0] > 100)] = np.NaN
# Percentages Student
#0 85.0 S1
#1 70.0 S2
#2 NaN NaN
#3 55.0 S4
#4 NaN NaN
正如您可以看到代碼類型的工作,但它實際上替換了NaN中Percentages大於100的特定行中的所有值。 我只想用NaN替換百分比列中的值,其中大於100.有沒有辦法做到這一點?
嘗試並使用np.where
:
new_DF.Percentages=np.where(new_DF.Percentages.gt(100),np.nan,new_DF.Percentages)
要么
new_DF.loc[new_DF.Percentages.gt(100),'Percentages']=np.nan
print(new_DF)
Student Percentages
0 S1 85.0
1 S2 70.0
2 S3 NaN
3 S4 55.0
4 S5 NaN
也,
df.Percentages = df.Percentages.apply(lambda x: np.nan if x>100 else x)
要么,
df.Percentages = df.Percentages.where(df.Percentages<100, np.nan)
你可以使用.loc :
new_DF.loc[new_DF['Percentages']>100, 'Percentages'] = np.NaN
輸出:
Student Percentages
0 S1 85.0
1 S2 70.0
2 S3 NaN
3 S4 55.0
4 S5 NaN
import numpy as np
import pandas as pd
new_DF = pd.DataFrame({'Student' : ['S1', 'S2', 'S3', 'S4', 'S5'],
'Percentages' : [85, 70, 101, 55, 120]})
#print(new_DF['Student'])
index=-1
for i in new_DF['Percentages']:
index+=1
if i > 100:
new_DF['Percentages'][index] = "nan"
print(new_DF)
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