Question

我有一份报告说我正在努力显示两个季度之间的差异。我有一个SQL查询，我正在阅读一个pandas数据帧，然后转动。

这是我的代码：

    df = pd.read_sql_query(mtd_query, cnxn, params=[report_start, end_mtd, report_start, end_mtd, whse])
    ##(m-1)//3 + 1  Determine which Quarter each month is
    ## Create the "Period" column by combining the Quater and the Month
    df['QUARTER'] = (df['INV_MONTH'].astype(int) - 1)//3 + 1
    df['PERIOD'] = df['INV_YEAR'].astype(str) + 'Q' + df['QUARTER'].astype(int).astype(str)
    df['MARGIN'] = (df['PROFIT'].astype(float) / df['SALES'].astype(float))

    df = df.drop('INV_MONTH', axis=1)
    df = df.drop('INV_YEAR', axis=1)
    df = pd.pivot_table(df, index=['REP', 'REP_NAME', 'CUST_NO', 'CUST_NAME', 'TOTALSALES'], columns=['PERIOD'], values=['SALES', 'PROFIT', 'MARGIN'], fill_value=0)
    df = df.reorder_levels([1, 0], axis=1).sort_index(axis=1, ascending=False)
    df = df.sortlevel(level=0, ascending=True)

我正在尝试确定'PERIOD'之间'MARGIN'列之间的差异。我一直无法找到任何方法来实现这一目标。任何建议表示赞赏。

当前输出显示：

PERIOD                                                                                            2017Q4                                 2017Q3                                 2017Q2                                 2017Q1                                 2016Q4                        
                                                                                                   SALES        PROFIT    MARGIN          SALES        PROFIT    MARGIN          SALES        PROFIT    MARGIN          SALES        PROFIT    MARGIN          SALES        PROFIT    MARGIN
REP    REP_NAME                       CUST_NO  CUST_NAME                      TOTALSALES                                                                                                                                                                                                    
1.0    Greensboro - House             245.0    TE CONNECTIVITY CORPORATION    103361.05         0.000000      0.000000  0.000000     434.500000     69.520000  0.160000   20391.666667   3262.666667  0.160000       0.000000      0.000000  0.000000       0.000000      0.000000  0.000000
                                      1789.0   GOOD HOUSEKEEPER               50108.47        678.508182     80.170909  0.145883     585.301429     64.180476  0.121915     718.685000     92.033125  0.130453     720.729333     97.955333  0.134821    1237.308333     88.210000  0.099450

所需的输出如下所示：

PERIOD                                                                                            2017Q4                                 2017Q3                                 2017Q2                                 2017Q1                                 2016Q4                        
                                                                                                   SALES        PROFIT    MARGIN   VARIANCE          SALES        PROFIT    MARGIN    VARIANCE          SALES        PROFIT    MARGIN    VARIANCE          SALES        PROFIT    MARGIN    VARIANCE          SALES        PROFIT    MARGIN
REP    REP_NAME                       CUST_NO  CUST_NAME                      TOTALSALES                                                                                                                                                                                                    
1.0    Greensboro - House             245.0    TE CONNECTIVITY CORPORATION    103361.05         0.000000      0.000000  0.000000    -.16         434.500000     69.520000  0.160000    0           20391.666667   3262.666667  0.160000    .16           0.000000      0.000000  0.000000      0            0.000000      0.000000  0.000000
                                      1789.0   GOOD HOUSEKEEPER               50108.47        678.508182     80.170909  0.145883    .023968     585.301429     64.180476  0.121915    -0.008537     718.685000     92.033125  0.130453    -.004368     720.729333     97.955333  0.134821     .035372       1237.308333     88.210000  0.099450

下面是

df.to_dict（'r'）：

[{('2016Q4', 'SALES'): 0.0, ('2017Q3', 'PROFIT'): 69.520000000000067, ('2017Q1', 'PROFIT'): 0.0, ('2017Q2', 'SALES'): 20391.666666666668, ('2017Q3', 'MARGIN'): 0.16, ('2016Q4', 'PROFIT'): 0.0, ('2017Q3', 'SALES'): 434.5, ('2017Q1', 'SALES'): 0.0, ('2017Q4', 'SALES'): 0.0, ('2016Q4', 'MARGIN'): 0.0, ('2017Q4', 'PROFIT'): 0.0, ('2017Q1', 'MARGIN'): 0.0, ('2017Q4', 'MARGIN'): 0.0, ('2017Q2', 'MARGIN'): 0.16, ('2017Q2', 'PROFIT'): 3262.6666666666665}, {('2016Q4', 'SALES'): 1237.3083333333332, ('2017Q3', 'PROFIT'): 64.180476190476185, ('2017Q1', 'PROFIT'): 97.9553333333333, ('2017Q2', 'SALES'): 718.68500000000006, ('2017Q3', 'MARGIN'): 0.1219152103415191, ('2016Q4', 'PROFIT'): 88.209999999999994}]

Answer 1

IIUC：

来源DF：

enrolment

解决方案：

In [60]: df
Out[60]:
  2016Q4                     2017Q1                  2017Q2               \
  MARGIN PROFIT        SALES MARGIN     PROFIT SALES MARGIN       PROFIT
0    0.0   0.00     0.000000    0.0   0.000000   0.0   0.16  3262.666667
1    NaN  88.21  1237.308333    NaN  97.955333   NaN    NaN          NaN

                   2017Q3                   2017Q4
          SALES    MARGIN     PROFIT  SALES MARGIN PROFIT SALES
0  20391.666667  0.160000  69.520000  434.5    0.0    0.0   0.0
1    718.685000  0.121915  64.180476    NaN    NaN    NaN   NaN

Answer 2

我无法使上述解决方案起作用所以我这样做了：

df['MARGIN'] = (df['PROFIT'].astype(float) / df['SALES'].astype(float))
df['MARGIN'] = df['MARGIN'].astype(float)
df['PREV_MARGIN'] = df['MARGIN'].shift(-1)
df['VARIANCE'] = df['MARGIN'] - df['PREV_MARGIN']
df = df.drop('PREV_MARGIN', axis=1)

这为我提供了完成工作所需的数据。

根据“值”数据

2 个答案: