How to convert HoltWinter of R into one of Python

Question

I have to convert the code of R that predicts the temperature into one of Python. But the code of R was not made by someone, so I need a help.

In R,

ts.train <- ts(train["avg"], frequency=52, start=c(1,1))
holt <- as.numeric(forecast(HoltWinters(ts.train, seasonal="additive"), h=4)$mean

train data has the weekly data with a yearly cycle like below,

yyyyuu	avg
201201	23.6
201202	22.4

yyyyuu means the weekly number uu of year yyyy.

According to the implementation of HoltWinters in R,

HoltWinters(x, alpha = NULL, beta = NULL, gamma = NULL,
            seasonal = c("additive", "multiplicative"),
            start.periods = 2, l.start = NULL, b.start = NULL,
            s.start = NULL,
            optim.start = c(alpha = 0.3, beta = 0.1, gamma = 0.1),
            optim.control = list())

So, I think that HoltWinters(ts.train, seasonal="additive") is referred by HoltWinters model like below,

smoothing_level: 0.3,
smoothing_trend: 0.1,
smoothing_seasonal: 0.1,
method: ls

trend: add
seasonal: add

And I think that ts(train["avg"], frequency=52, start=c(1,1)) means seasonal_periods = 52

So, I have implemented it like below in Python,

fit_exp = ExponentialSmoothing(train['avg'], trend='add', seasonal_periods=52, seasonal='additive').fit(
    smoothing_level=0.3,
    smoothing_trend=0.1,
    smoothing_seasonal=0.1,
    method = "ls"
fit_exp.forecast(4)
)

But the results between R and Python are different each other, In R

20.857783
20.788399
20.928776
21.582850

In Python,

20.683062
20.941403
21.183668
21.927177

So, I have some questions about this,

1. In R, there is a code to make time-series like ts.train <- ts(train["avg"], frequency=52, start=c(1,1)), but In python, there is no code to make time-series data. is it affected to the model result?

2. If so, How should I implement to make time-series data? In train data, there are only two columns, yyyyuu and avg.

3. Are there another parameters to synchronize R Holtwinter with PYthon Holtwinter?

Thank you.