我试图从使用熊猫的数据框中选择三列[“ attacktype1”，“ attacktype2”，“ attacktype3”]的数据类型为整数，并想将那几列中的fillna（0）总计为新列。[ “总攻击”]
可以从以下位置下载数据集：
单击[此处] https://s3.amazonaws.com/datasetsgun/data/terror.csv
我尝试一次将fillna（0）应用于一列，然后将它们总计到一个新的单列中。
我的第一种方式：
da1 = pd.read_csv('terror.csv', sep = ',', header=0 , encoding='latin' , na_values=['Missing', ' '])
da1.head()
#Handling missing values
da1['attacktype3'] = da1['attacktype3'].fillna(0)
da1['attacktype2'] = da1['attacktype2'].fillna(0)
da1['attacktype1'] = da1['attacktype1'].fillna(0)
da1['total_attacks'] = da1['attacktype3'] + da1['attacktype2'] + da1['attacktype1']
#country_txt is a column which consists of different countries.Want to find "Total_atacks" only for India. Therefore, the condition applied is country_txt=='India'.
a1 = da1.query("country_txt=='India'").agg({'total_attacks':np.sum})
print(a1)
我的第二种方式（不起作用）：
da1 = pd.read_csv('terror.csv', sep = ',', header=0 , encoding='latin' , na_values=['Missing', ' '])
da1.head()
#Handling missing values
check1=Df.country_txt=="India"
store=Df[["attacktype1","attacktype2","attacktype3"]].apply(lambda x:x.fillna(0))
Total_attack=Df.loc[check1,store].sum(axis=1)
print(Total_attack)
I want to apply fillna(0) to multiple columns in a single line and also total those columns in an alternate and effective way.
The error that I get when I use my second way is:
ValueError: Cannot index with multidimensional key
最佳答案
首先用boolean indexing用DataFrame.loc过滤，然后用DataFrame.fillna替换缺少的值：
check1 = Df.country_txt == "India"
cols = ["attacktype1","attacktype2","attacktype3"]
Df['Total_attack'] = Df.loc[check1, cols].fillna(0).sum(axis=1)
对于标量，一个数字输出加
sum：Total_attack = Df['Total_attack'].sum()
print (Total_attack)
35065.0
关于python - 使用pandas选择多个列和fillna()的另一种方法，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/56205958/