我正在处理一份报告，以显示两个季度之间的差异。我有一个 SQL 查询，我正在将其读入 Pandas 数据框，然后进行旋转。
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“期间”之间的“ margin ”列之间的差异。我一直无法找到任何方法来实现这一点。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
df.to_dict('r') 下面:
[{('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}]
最佳答案
IIUC:
来源 DF:
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
解决方法:
In [61]: tmp = (df.loc[:, pd.IndexSlice[:, 'MARGIN']]
...: .fillna(0)
...: .diff(axis=1)
...: .rename(columns=lambda x: 'VARIANCE' if x=='MARGIN' else x))
...:
In [62]: pd.concat([df, tmp], axis=1).sort_index(axis=1)
Out[62]:
2016Q4 2017Q1 2017Q2 \
MARGIN PROFIT SALES VARIANCE MARGIN PROFIT SALES VARIANCE MARGIN
0 0.0 0.00 0.000000 NaN 0.0 0.000000 0.0 0.0 0.16
1 NaN 88.21 1237.308333 NaN NaN 97.955333 NaN 0.0 NaN
2017Q3 \
PROFIT SALES VARIANCE MARGIN PROFIT SALES VARIANCE
0 3262.666667 20391.666667 0.16 0.160000 69.520000 434.5 0.000000
1 NaN 718.685000 0.00 0.121915 64.180476 NaN 0.121915
2017Q4
MARGIN PROFIT SALES VARIANCE
0 0.0 0.0 0.0 -0.160000
1 NaN NaN NaN -0.121915
关于python - 根据 'Columns' 数据在 Pivot 'Values' 之间添加计算字段，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/47759982/