我希望将完成交易的百分比换算为每月的总交易量。以前我的数据仅用了一个月,并通过以下方式解决:
total_trades = df['state'].count()
RFQ_Hit_Rate = done_trades / total_trades
RFQ_Hit_Rate = round(RFQ_Hit_Rate, 6)
现在有12个月的数据,所以我需要更新代码。新数据
dfHit_Rate_All = df[['Year_Month','state']].copy()
dfHit_Rate_All = dfHit_Rate_All.groupby(['Year_Month','state']).size().reset_index(name='count')
Year_Month state Counts
2017-11 Customer Reject 1
2017-11 Customer Timeout 2
2017-11 Dealer Reject 3
2017-12 Dealer Timeout 4
2017-12 Done 5
2017-12 Done 6
2018-01 Tied Covered 7
2018-01 Tied Done 8
2018-01 Tied Traded Away 9
2018-02 Traded Away 10
2018-02 Done 11
2018-02 Customer Reject 12
每个月查找总交易数,总完成交易数并计算比率。注意任何带有'Done'的字符串都是完成交易,即[df ['state']。str.contains('Done'):
Year_Month Total_state_count Total_state_count_Done Done_To_Total_Ratio
2017-11 6 0 0%
2017-12 15 11 73%
2018-01 24 8 33%
2018-02 33 11 33%
答案 0 :(得分:1)
我认为需要通过agg
和元组聚合 - 具有聚合函数的新列名:
agg = [('Total_state_count_Done',lambda x: x.str.contains('Done').sum()),
('Total_state_count', 'size')]
df = df.groupby('Year_Month')['state'].agg(agg)
对于新的列除以及100
的倍数:
df['Done_To_Total_Ratio'] = df['Total_state_count_Done'].div(df['Total_state_count']).mul(100)
print (df)
Total_state_count_Done Total_state_count Done_To_Total_Ratio
Year_Month
2017-11 0 3 0.000000
2017-12 2 3 66.666667
2018-01 1 3 33.333333
2018-02 1 3 33.333333
如果需要将最后一列转换为整数并添加百分比:
df['Done_To_Total_Ratio'] = (df['Total_state_count_Done']
.div(df['Total_state_count'])
.mul(100)
.astype(int)
.astype(str)
.add('%'))
print (df)
Total_state_count_Done Total_state_count Done_To_Total_Ratio
Year_Month
2017-11 0 3 0%
2017-12 2 3 66%
2018-01 1 3 33%
2018-02 1 3 33%