我拥有的是一个名为“报告”的数据集,其中包含送货司机的详细信息。 “通过”意味着他们按时交付,“失败”意味着他们没有按时交付
Name|Outcome
A |Pass
B |Fail
C |Pass
D |Pass
A |Fail
C |Pass
我想要什么
Name|Pass|Fail|Total
A |1 |1 |2
B |0 |1 |1
C |2 |0 |2
D |1 |0 |1
我尝试过:
report.groupby(['Name','outcome']).agg(['count'])
但它没有给我所需的输出。
crosstab
与 margins=True
和 margins_name
参数一起使用:
print (pd.crosstab(df['Name'], df['Outcome'], margins=True, margins_name='Total'))
Outcome Fail Pass Total
Name
A 1 1 2
B 1 0 1
C 0 2 2
D 0 1 1
Total 2 4 6
DataFrame.iloc
:
df = pd.crosstab(df['Name'], df['Outcome'], margins=True, margins_name='Total').iloc[:-1]
print (df)
Outcome Fail Pass Total
Name
A 1 1 2
B 1 0 1
C 0 2 2
D 0 1 1
这是
pd.crosstab
与 sum
超过 axis=1
:
df = pd.crosstab(df['Name'], df['Outcome'])
df['Total'] = df[['Fail', 'Pass']].sum(axis=1)
Outcome Fail Pass Total
Name
A 1 1 2
B 1 0 1
C 0 2 2
D 0 1 1
或者删除列轴名称,我们使用
rename_axis
:
df = pd.crosstab(df['Name'], df['Outcome']).reset_index().rename_axis(None, axis='columns')
df['Total'] = df[['Fail', 'Pass']].sum(axis=1)
Name Fail Pass Total
0 A 1 1 2
1 B 1 0 1
2 C 0 2 2
3 D 0 1 1
In [1]: from io import StringIO
In [2]: df_string = '''Name|Outcome^M
...: A |Pass^M
...: B |Fail^M
...: C |Pass^M
...: D |Pass^M
...: A |Fail^M
...: C |Pass'''
In [3]: report = pd.read_csv(StringIO(df_string), sep='|')
In [4]: report.assign(count=1).groupby(["Name", "Outcome"])["count"].sum().unstack().assign(Total=lambda df: df.sum(axis=1))
Out[4]:
Outcome Fail Pass Total
Name
A 1.0 1.0 2.0
B 1.0 NaN 1.0
C NaN 2.0 2.0
D NaN 1.0 1.0
现在您可以使用
fillna(0)
方法填充 NA 值
一种使用
pandas.dummies
和 groupby
的方法:
report = pd.get_dummies(df1, columns=['outcome']).groupby(['name'], as_index=False).sum().rename(columns={"outcome_Fail":"Fail", "outcome_Pass":"Pass"})
report["Total"] = report["Pass"] + report["Fail"]
print(report)
输出:
name Fail Pass Total
0 A 1 1 2
1 B 1 0 1
2 C 0 2 2
3 D 0 1 1