pandas 值的分布以固定百分比为基础,并将值垂直相加

v8wbuo2f  于 12个月前  发布在  其他
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我已经在Excel中创建了下面的表,现在我希望在Python中复制相同的内容。

  • 已结帐目按

    栏内其后各月的固定百分率作进一步分配

我希望在python中创建类似的框架,并开始使用以下方法:

data = {
    'Assigned Accounts': [1428, 1415, 1398, 1402, 1468, 1503, 1694],
    'Month': ['Jun-22', 'Jul-22', 'Aug-22', 'Sep-22', 'Oct-22', 'Nov-22', 'Dec-22']
}

df = pd.DataFrame(data)

df['Settled Accounts'] = int(data['Assigned Accounts']*0.17)

# Fixed percentage distribution
percentages = [0.46, 0.36, 0.06, 0.03, 0.02, 0.01, 0.01]

# Creating columns for subsequent months and distributing the settled debtors
months = ['Jun-22', 'Jul-22', 'Aug-22', 'Sep-22', 'Oct-22', 'Nov-22', 'Dec-22', 'Jan-23', 'Feb-23', 'Mar-23', 'Apr-23', 'May-23', 'Jun-23']

how it could be approached further.?

字符串
我正在寻找最后的框架,使垂直列的总和如下:

Month        Total
Jan - 23     350
Feb - 23     335
Mar - 23     343
Apr - 23     341 and so on

更新问题:

对于下面的图表,方法将发生什么变化:

data = {
    'Assigned Accounts': [674, 779, 812, 735, 753, 677, 628, 589, 570, 581, 550, 595, 596, 659, 754, 731, 739, 741, 766, 796, 752, 821, 818, 868, 850, 909, 927, 915],
    'Month': ['Jun-22', 'Jul-22', 'Aug-22', 'Sep-22', 'Oct-22', 'Nov-22', 'Dec-22', 'Jan-23', 'Feb-23', 'Mar-23', 'Apr-23', 'May-23', 'Jun-23', 'Jul-23', 'Aug-23', 'Sep-23', 'Oct-23', 'Nov-23', 'Dec-23', 'Jan-24', 'Feb-24', 'Mar-24', 'Apr-24', 'May-24', 'Jun-24', 'Jul-24', 'Aug-24', 'Sep-24']
}

df = pd.DataFrame(data)

months: ['Jun-22', 'Jul-22', 'Aug-22', 'Sep-22', 'Oct-22', 'Nov-22', 'Dec-22', 'Jan-23', 'Feb-23', 'Mar-23', 'Apr-23', 'May-23', 'Jun-23', 'Jul-23', 'Aug-23', 'Sep-23', 'Oct-23', 'Nov-23', 'Dec-23', 'Jan-24', 'Feb-24', 'Mar-24', 'Apr-24', 'May-24', 'Jun-24', 'Jul-24', 'Aug-24', 'Sep-24']


其余的问题陈述将保持不变。

ss2ws0br

ss2ws0br1#

如果我理解正确的话,您希望将每一行的值乘以百分比,并每月移动这些值。
您可以使用numpybroadcasting轻松完成此操作。

  • 注意:在你的图片中,看起来你使用了~23%的因子(而不是17%),我在下面使用它来更好地比较数字。
# 23% of initial value
df['Settled Accounts'] = df['Assigned Accounts'].mul(0.23).astype(int)

# create output array
tmp = np.full((len(df), len(months)), np.nan)
# indexer for rows
a = np.arange(len(df))
# indexer for shifted values
col = (a[:,None] + np.arange(len(percentages))).ravel()
m = col<len(a)
idx = np.repeat(a, len(percentages))[m]

# broadcast the multiplication by "percent"
tmp[idx, col[m]] =  (df['Settled Accounts'].to_numpy()[:,None]
                     * np.array(percentages)
                    ).ravel()[m].astype(int)

# convert to DataFrame
tmp = pd.DataFrame(tmp, index=df.index, columns=months)
# add sum as new row
tmp.loc['sum'] = tmp.sum()

out = df.join(tmp, how='outer')

字符串
输出量:

Assigned Accounts   Month  Settled Accounts  Jun-22  Jul-22  Aug-22  Sep-22  Oct-22  Nov-22  Dec-22  Jan-23  Feb-23  Mar-23  Apr-23  May-23  Jun-23
0               1428.0  Jun-22             328.0   150.0   118.0    19.0     9.0     6.0     3.0     3.0     NaN     NaN     NaN     NaN     NaN     NaN
1               1415.0  Jul-22             325.0     NaN   149.0   117.0    19.0     9.0     6.0     3.0     3.0     NaN     NaN     NaN     NaN     NaN
2               1398.0  Aug-22             321.0     NaN     NaN   147.0   115.0    19.0     9.0     6.0     3.0     3.0     NaN     NaN     NaN     NaN
3               1402.0  Sep-22             322.0     NaN     NaN     NaN   148.0   115.0    19.0     9.0     6.0     3.0     3.0     NaN     NaN     NaN
4               1468.0  Oct-22             337.0     NaN     NaN     NaN     NaN   155.0   121.0    20.0    10.0     6.0     3.0     3.0     NaN     NaN
5               1503.0  Nov-22             345.0     NaN     NaN     NaN     NaN     NaN   158.0   124.0    20.0    10.0     6.0     3.0     3.0     NaN
6               1694.0  Dec-22             389.0     NaN     NaN     NaN     NaN     NaN     NaN   178.0   140.0    23.0    11.0     7.0     3.0     3.0
sum                NaN     NaN               NaN   150.0   267.0   283.0   291.0   304.0   316.0   343.0   182.0    45.0    23.0    13.0     6.0     3.0

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