Question

下午全部，

我有一个非常大的数据集，我已将其分组。这是一个示例：

<Results>
    <ResultSet fetchSize="10">
             <Row rowNumber="1">
                <REL_ID>22439129</REL_ID>
                <EFF_TMSTP>2015-09-14 07:08:31.246</EFF_TMSTP>
                <RETIRED_TRAN_ID>63859659</RETIRED_TRAN_ID>
                <OBJ_ID>1371718</OBJ_ID>
                <REL_OBJ_ID>1350658</REL_OBJ_ID>
                <CUST_VIEW_CD>CORE</CUST_VIEW_CD>
                <CUST_MODEL_CD>CESE</CUST_MODEL_CD>
                <TYPE_CD>DRVD</TYPE_CD>
                <UNLINK_FLG>N</UNLINK_FLG>
                <EXPR_TMSTP>9999-12-31 23:59:59.999</EXPR_TMSTP>
                <TRAN_TMSTP>2015-09-14 07:08:31.246</TRAN_TMSTP>
                <RETIRED_TRAN_TMSTP>9999-12-31 23:59:59.999</RETIRED_TRAN_TMSTP>
                <TRAN_ID>63859659</TRAN_ID>
            </Row>
            <Row rowNumber="2">
                <REL_ID>22482704</REL_ID>
                <EFF_TMSTP>2015-09-24 06:27:43.358</EFF_TMSTP>
                <RETIRED_TRAN_ID>64285012</RETIRED_TRAN_ID>
                <OBJ_ID>11983064</OBJ_ID>
                <REL_OBJ_ID>1350658</REL_OBJ_ID>
                <CUST_VIEW_CD>CORE</CUST_VIEW_CD>
                <CUST_MODEL_CD>CESE</CUST_MODEL_CD>
                <TYPE_CD>DRVD</TYPE_CD>
                <UNLINK_FLG>N</UNLINK_FLG>
                <EXPR_TMSTP>9999-12-31 23:59:59.999</EXPR_TMSTP>
                <TRAN_TMSTP>2015-09-24 06:27:43.358</TRAN_TMSTP>
                <RETIRED_TRAN_TMSTP>9999-12-31 23:59:59.999</RETIRED_TRAN_TMSTP>
                <TRAN_ID>64285012</TRAN_ID>
            </Row>
            <Row rowNumber="3">
                <REL_ID>25372326</REL_ID>
                <EFF_TMSTP>2016-07-08 04:46:02.591</EFF_TMSTP>
                <RETIRED_TRAN_ID>81170279</RETIRED_TRAN_ID>
                <OBJ_ID>13613079</OBJ_ID>
                <REL_OBJ_ID>1350658</REL_OBJ_ID>
                <CUST_VIEW_CD>CORE</CUST_VIEW_CD>
                <CUST_MODEL_CD>CI</CUST_MODEL_CD>
                <TYPE_CD>DRVD</TYPE_CD>
                <UNLINK_FLG>N</UNLINK_FLG>
                <EXPR_TMSTP>9999-12-31 23:59:59.999</EXPR_TMSTP>
                <TRAN_TMSTP>2016-07-08 04:46:02.595</TRAN_TMSTP>
                <RETIRED_TRAN_TMSTP>9999-12-31 23:59:59.999</RETIRED_TRAN_TMSTP>
                <TRAN_ID>81170279</TRAN_ID>
            </Row>
    </ResultSet>
</Results>

输出：

df_ccy = df.groupby(['currency_str','state' 
 ['state'].count().reset_index(name='count').sort_values(['count'], ascending=False)

display(df_ccy)

我只想表明：

currency_str    state           count
USD             Traded Away     148
AUD             Dealer Timeout  52
CAD             Done            44
USD             Covered         38
USD             Dealer Timeout  29
ZAR             Done            22

我是通过以下方式实现的：

CAD             Done            44
ZAR             Done            22

我应该如上所述在原始groupby语句或过滤器上使用Lambda函数吗？什么是最佳做法？

Answer 1

df = pd.DataFrame({'currency_str': ['USD', 'AUD', 'CAD', 'CAD', 'ZAR', 
                                    'ZAR', 'USD', 'USD', 'ZAR'], 
                   'state': ['Traded Away', 'Dealer Timeout', 'Done', 'Done', 'Done',
                             'Done', 'Covered', 'Dealer Timeout', 'Done']})

print (df)
  currency_str           state
0          USD     Traded Away
1          AUD  Dealer Timeout
2          CAD            Done
3          CAD            Done
4          ZAR            Done
5          ZAR            Done
6          USD         Covered
7          USD  Dealer Timeout
8          ZAR            Done

我认为你需要先过滤：

df1 = df[df['state']=='Done']
#alternative
#df1 = df.query("state == 'Done'")

然后数：

df_ccy = (df1.groupby(['currency_str','state'])['state']
            .count()
            .reset_index(name='count')
            .sort_values(['count'], ascending=False))

print (df_ccy)
  currency_str state  count
1          ZAR  Done      3
0          CAD  Done      2

或者，如果不是具有相同过滤值的重要列状态：

df_ccy = (df1['currency_str'].value_counts()
                            .reset_index(name='count')
                            .rename(columns={'index':'currency_str'}))
print (df_ccy)
  currency_str  count
0          ZAR      3
1          CAD      2

分组数据帧然后在pandas中过滤的最有效方法

1 个答案: