Question

我试图用大熊猫将这张专辑中的第一张图像转换成第二张图像，但我得到的只是第三张...

原始

F.mean_squared_error

想要的结果：

from chainer import iterators, optimizers, training
from chainer import Chain
from chainer.datasets import mnist
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions
import numpy as np


# simple addition data

N = 1000
x_ = np.random.choice(10, size=(N, 2)).astype(np.float32)
y_ = x_.sum(axis=1).astype(np.float32)
train = [(x[:,None], np.asarray([y])) for x, y in zip(x_, y_)]

train_iter = iterators.SerialIterator(train, 1000)

# model

class Model(Chain):
    def __init__(self):
        super(Model, self).__init__()

        with self.init_scope():
            self.l_out = L.Linear(2, 1)

    def forward(self, x):
        return self.l_out(x)

model = Model()
model = F.mean_squared_error

# run

optimizer = optimizers.Adam()
optimizer.setup(model)

updater = training.updaters.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (1000, 'epoch'), out='mnist_result')
trainer.run()

我所拥有的：

Year    Jan     Feb     Mar     Apr     May     Jun     Jul     Aug     Sep     Oct     Nov     Dec
1981    453.1   126.3   5.8     47.1    25.3    16.8    0       1.1     4.4     17.8    52.5    72.4
1982    211.4   23.1    231.2   0.8     0.2     0       0       0       15.3    0.9     8.6     59.9
1983    45.2    22.1    537.7   22.8    29.9    0       0       0.1     0.7     1.2     47      20.9
1984    390.2   514.2   140.3   7.3     0       0       2.8     0.1     0       18.3    23.2    91.7

我的代码就是这样：

Year    Month   Value
1981    Jan     453.1
1981    Feb     126.3
1981    Mar     5.8 
1981    Apr     47.1
...

我该如何替代每年的几个月？我想每个月都有第一年，然后每个月都有第二年……而不是每年的第一个月。

Answer 1

对列名称使用有序分类法，因此两列都可以DataFrame.sort_values进行正确排序：

spring.data.web.pageable.default-page-size

或将Series转换为months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] data = pd.read_csv("Burketown.csv", index_col=['Year'])[months] data.columns = pd.CategoricalIndex(data.columns, ordered=True, categories=months) df = data.reset_index()[months] fixed_data = (pd.melt(data, id_vars=['Year'], value_vars=months) .sort_values(['Year', 'variable']))：

ordered categorical

熊猫融化得到交替结果

1 个答案: