Python熊猫为多个输出变量生成一个表。

Question

我有事故数据，其中部分数据包括事故发生年份、受伤程度和受伤者的年龄。这是DataFrame的一个例子：

df = pd.DataFrame({'Year': ['2010', '2010','2010','2010','2010','2011','2011','2011','2011'], 
                        'Degree_injury': ['no_injury', 'death', 'first_aid', 'minor_injury','disability','disability', 'disability', 'death','first_aid'],
                        'Age': [50,31,40,20,45,29,60,18,48]})

print(df)                   

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我希望将三个输出变量按年龄小于40岁的年份分组，并统计残疾人数、死亡人数和轻伤人数。

输出应该类似于这个

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当年龄< 40时，我生成了三个变量(num_disability、num_death、num_minor_injury)，如下所示。

disability_filt = (df['Degree_injury'] =='disability') &\
                   (df['Age'] <40)
num_disability = df[disability_filt].groupby('Year')['Degree_injury'].count()
death_filt = (df['Degree_injury'] == 'death')& \
                    (df['Age'] <40)
num_death = df[death_filt].groupby('Year')['Degree_injury'].count()
minor_injury_filt = (df['Degree_injury'] == 'death') & \
                   (df['Age'] <40)
num_minor_injury = df[minor_injury_filt].groupby('Year')['Degree_injury'].count()

如何将这些变量组合在一个表中，如上表所示

提前谢谢你，

Answer 1

在根据您的条件过滤行之后使用pivot_table：

out = df[df['Age'].lt(40)].pivot_table(index='Year', columns='Degree_injury', 
                                       values='Age', aggfunc='count', fill_value=0)
print(out)

# Output:
Degree_injury  death  disability  minor_injury
Year                                          
2010               1           0             1
2011               1           1             0

Answer 2

# prep data
df2 = df.loc[df.Age<40,].groupby("Year").Degree_injury.value_counts().to_frame().reset_index(level=0, inplace=False)
df2 = df2.rename(columns={'Degree_injury': 'Count'})
df2['Degree_injury'] = df2.index
df2
#                   Year    Count   Degree_injury
# death             2010    1       death
# minor_injury      2010    1       minor_injury
# death             2011    1       death
# disability        2011    1       disability

# pivot result
df2.pivot(index='Year',columns='Degree_injury')
#       death   disability  minor_injury
# Year          
# 2010  1.0     NaN         1.0
# 2011  1.0     1.0         NaN