Question

我有一个数据集，显示每个类别的负载计数。以下是我的数据。

Name,Count1,Count2,PercentDiff,Category
Store A,10,4,0.4,Less than 1%
Store B,20,26,1.3,Less than 5%
Store C,12,48,4,Less than 5%
Store D,30,180,6,Less than 10%

我想获得以下每个类别的计数

1. Less than 0
2. Less than 1%
3. Less than 5%
4. Less than 10%
5. More than 10%

我使用以下规则对每个条目进行分类：

new.loc[new['PercentDiff'] < 0, 'Category'] = 'Less than 0%'
new.loc[new['PercentDiff'] == 0, 'Category'] = 'Exact match'
new.loc[new['PercentDiff'] < 0.01, 'Category'] = 'Less than 1%'
new.loc[new['PercentDiff'] < 0.05, 'Category'] = 'Less than 5%'
new.loc[new['PercentDiff'] < 0.1, 'Category'] = 'Less than 10%'
new.loc[new['PercentDiff'] == 0, 'Category'] = 'Exact match'
new.loc[new['PercentDiff'] > 0.1, 'Category'] = 'Greater than 10%'
new['PercentDiff1'] = new['PercentDiff'].astype(int)

Output1 = new.groupby(['Category']).agg(lambda x: x.mad())
Output1 = Output1.replace(np.nan, '', regex=True)
SumMail = pd.value_counts(Output1['Category'].values)

但是，如果数据集没有任何类别的值，则会收到错误消息，指出没有找到特定类别的值。

TypeError：＆＃39; str＆＃39; object不能解释为整数

KeyError：＆＃39;超过10％＆＃39;

任何人都可以帮我修改这段代码，使其对没有记录的类别返回0。

提前致谢。

Answer 1

您需要定义'类别'列astype分类dtype：

df['Category'] = df['Category'].astype('category')
df['Category'] = df['Category'].cat.set_categories(['Less than 0',
                                                    'Less than 1%',
                                                    'Less than 5%',
                                                    'Less than 10%',
                                                    'More than 10%'],
                                                    ordered=True)

df['Category'].value_counts(sort=False)

输出：

Less than 0      0
Less than 1%     1
Less than 5%     2
Less than 10%    1
More than 10%    0
Name: Category, dtype: int64

Answer 2

在进行标记之前，请检查您的数据框是否为空。

if new['PercentDiff'].empty:
    return 0
else:
    new.loc[new['PercentDiff'] < 0, 'Category'] = 'Less than 0%'
    new.loc[new['PercentDiff'] == 0, 'Category'] = 'Exact match'
    new.loc[new['PercentDiff'] < 0.01, 'Category'] = 'Less than 1%'
    new.loc[new['PercentDiff'] < 0.05, 'Category'] = 'Less than 5%'
    new.loc[new['PercentDiff'] < 0.1, 'Category'] = 'Less than 10%'
    new.loc[new['PercentDiff'] == 0, 'Category'] = 'Exact match'
    new.loc[new['PercentDiff'] > 0.1, 'Category'] = 'Greater than 10%'
    new['PercentDiff1'] = new['PercentDiff'].astype(int)

    Output1 = new.groupby(['Category']).agg(lambda x: x.mad())
    Output1 = Output1.replace(np.nan, '', regex=True)
    SumMail = pd.value_counts(Output1['Category'].values)

Pandas groupby / value_counts没有值

2 个答案: