使用pandas选择多列和多列中的fillna（）的另一种方法

Question

我正在尝试使用pandas从数据帧中选择数据类型为整数的三列[“attacktype1”，“attacktype2”，“attacktype3”]，并希望将na（0）填充到这些列中，然后将这些列合计到一个新列中。[“total\ u attacks”]

数据集可从以下位置下载： 单击[此处]https://s3.amazonaws.com/datasetsgun/data/terror.csv

我曾经尝试过一次将fillna（0）应用于一列，然后将它们合计到一个新的单列中。你知道吗

我的第一条路：

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)

我的第二种方法（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

Answer 1

首先用^{}按^{}筛选，然后用^{}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addsum：

Total_attack = Df['Total_attack'].sum()
print (Total_attack)
35065.0