[英]How to iterate through columns of the dataframe?
I want go through the all the columns of the dataframe.我想要 go 通过 dataframe 的所有列。 so that I will get a particular data of the column, using these data I have to calculate for another dataframe.
这样我将获得该列的特定数据,使用这些数据我必须为另一个 dataframe 计算。 Here i have:
在这里我有:
DP1 DP2 DP3 DP4 DP5 DP6 DP7 DP8 DP9 DP10 Total
OP1 357848.0 1124788.0 1735330.0 2218270.0 2745596.0 3319994.0 3466336.0 3606286.0 3833515.0 3901463.0 3901463.0
OP2 352118.0 1236139.0 2170033.0 3353322.0 3799067.0 4120063.0 4647867.0 4914039.0 5339085.0 NaN 5339085.0
OP3 290507.0 1292306.0 2218525.0 3235179.0 3985995.0 4132918.0 4628910.0 4909315.0 NaN NaN 4909315.0
OP4 310608.0 1418858.0 2195047.0 3757447.0 4029929.0 4381982.0 4588268.0 NaN NaN NaN 4588268.0
OP5 443160.0 1136350.0 2128333.0 2897821.0 3402672.0 3873311.0 NaN NaN NaN NaN 3873311.0
OP6 396132.0 1333217.0 2180715.0 2985752.0 3691712.0 NaN NaN NaN NaN NaN 3691712.0
OP7 440832.0 1288463.0 2419861.0 3483130.0 NaN NaN NaN NaN NaN NaN 3483130.0
OP8 359480.0 1421128.0 2864498.0 NaN NaN NaN NaN NaN NaN NaN 2864498.0
OP9 376686.0 1363294.0 NaN NaN NaN NaN NaN NaN NaN NaN 1363294.0
OP10 344014.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 344014.0
Total 3671385.0 11614543.0 17912342.0 21930921.0 21654971.0 19828268.0 17331381.0 13429640.0 9172600.0 3901463.0 34358090.0
Latest Observation 344014.0 1363294.0 2864498.0 3483130.0 3691712.0 3873311.0 4588268.0 4909315.0 5339085.0 3901463.0 NaN
From this table I would like to calculate formula this formula:in column DP1,Total/Last observation and this answer is divides by DP2 columns total.从这个表我想计算公式这个公式:在 DP1 列中,总/最后一次观察,这个答案是除以 DP2 列总数。 Like this we have to calculate all the columns and save it in another dataframe.
像这样,我们必须计算所有列并将其保存在另一个 dataframe 中。
we need row like this:我们需要这样的行:
Weighted Average 3.491 1.747 1.457 1.174 1.104 1.086 1.054 1.077 1.018
This code we tried:我们尝试的这段代码:
LDFTriangledf['Weighted Average'] =CumulativePaidTriangledf.loc['Total','DP2']/(CumulativePaidTriangledf.loc['Total','DP1'] - CumulativePaidTriangledf.loc['Latest Observation','DP1'])
You can remove the column names from .loc
and just shift(-1, axis=1)
to get the next column's Total
.您可以从
.loc
中删除列名,然后只需shift(-1, axis=1)
即可获得下一列的Total
。 This lets you apply the formula to all columns in a single operation:这使您可以将公式应用于单个操作中的所有列:
CumulativePaidTriangledf.shift(-1, axis=1).loc['Total'] / (CumulativePaidTriangledf.loc['Total'] - CumulativePaidTriangledf.loc['Latest Observation'])
# DP1 3.490607
# DP2 1.747333
# DP3 1.457413
# DP4 1.173852
# DP5 1.103824
# DP6 1.086269
# DP7 1.053874
# DP8 1.076555
# DP9 1.017725
# DP10 inf
# Total NaN
# dtype: float64
Here is a breakdown of what the three components are doing:以下是这三个组件正在执行的操作的细分:
DP1 ![]() |
DP2 ![]() |
DP3 ![]() |
DP4 ![]() |
DP5 ![]() |
DP6 ![]() |
DP7 ![]() |
DP8 ![]() |
DP9 ![]() |
DP10 ![]() |
Total![]() |
|
---|---|---|---|---|---|---|---|---|---|---|---|
A: .shift(-1, axis=1).loc['Total'] -- We are shifting the whole Total row to the left, so every column now has the next Total value. ![]() .shift(-1, axis=1).loc['Total'] -- 我们将整个Total 行向左移动,所以现在每一列都有下一个Total 值。 |
1.161454e+07 ![]() |
1.791234e+07 ![]() |
2.193092e+07 ![]() |
2.165497e+07 ![]() |
1.982827e+07 ![]() |
1.733138e+07 ![]() |
1.342964e+07 ![]() |
9.172600e+06 ![]() |
3.901463e+06 ![]() |
34358090.0 ![]() |
NaN![]() |
B: .loc['Total'] -- This is the normal Total row. ![]() .loc['Total'] -- 这是正常的Total 行。 |
3.671385e+06 ![]() |
1.161454e+07 ![]() |
1.791234e+07 ![]() |
2.193092e+07 ![]() |
2.165497e+07 ![]() |
1.982827e+07 ![]() |
1.733138e+07 ![]() |
1.342964e+07 ![]() |
9.172600e+06 ![]() |
3901463.0 ![]() |
34358090.0 ![]() |
C: .loc['Latest Observation'] -- This is the normal Latest Observation . ![]() .loc['Latest Observation'] -- 这是正常的Latest Observation 。 |
3.440140e+05 ![]() |
1.363294e+06 ![]() |
2.864498e+06 ![]() |
3.483130e+06 ![]() |
3.691712e+06 ![]() |
3.873311e+06 ![]() |
4.588268e+06 ![]() |
4.909315e+06 ![]() |
5.339085e+06 ![]() |
3901463.0 ![]() |
NaN![]() |
A / (BC) -- This is what the code above does. ![]() Total row (A) and divides it by the difference of the current Total row (B) and current Latest observation row (C).![]() Total 行 (A) 并将其除以当前Total 行 (B) 和当前Latest observation 行 (C) 的差值。 |
3.490607 ![]() |
1.747333 ![]() |
1.457413 ![]() |
1.173852 ![]() |
1.103824 ![]() |
1.086269 ![]() |
1.053874 ![]() |
1.076555 ![]() |
1.017725 ![]() |
inf![]() |
NaN![]() |
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