pandas 将所有数据框列转换为浮动的最快方法 - 熊猫 astype 慢
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Fastest way to cast all dataframe columns to float - pandas astype slow
提问by elleciel
Is there a faster way to cast all columns of a pandas dataframe to a single type? This seems particularly slow:
有没有更快的方法将Pandas数据帧的所有列转换为单一类型？这似乎特别慢：
df = df.apply(lambda x: x.astype(np.float64), axis=1)
I suspect there's not much I can do about it because of the memory allocation overhead of numpy.ndarray.astype.
我怀疑由于numpy.ndarray.astype.
I've also tried pd.to_numericbut it arbitrarily chooses to cast a few of my columns into inttypes instead.
我也尝试过，pd.to_numeric但它任意选择将我的一些列转换为int类型。
回答by miradulo
No need for apply, just use DataFrame.astypedirectly.
不需要apply，直接使用DataFrame.astype即可。
df.astype(np.float64)
apply-ing is also going to give you a pretty bad performance hit.
apply-ing 也会给你带来非常糟糕的性能损失。
Example
例子
df = pd.DataFrame(np.arange(10**7).reshape(10**4, 10**3))
%timeit df.astype(np.float64)
1 loop, best of 3: 288 ms per loop
%timeit df.apply(lambda x: x.astype(np.float64), axis=0)
1 loop, best of 3: 748 ms per loop
%timeit df.apply(lambda x: x.astype(np.float64), axis=1)
1 loop, best of 3: 2.95 s per loop回答by Divakar
One efficient way would be to work with array data and cast it back to a dataframe, like so -
一种有效的方法是使用数组数据并将其转换回数据帧，如下所示 -
pd.DataFrame(df.values.astype(np.float64))
Runtime test -
运行时测试 -
In [144]: df = pd.DataFrame(np.random.randint(11,99,(5000,5000)))
In [145]: %timeit df.astype(np.float64) # @Mitch's soln
10 loops, best of 3: 121 ms per loop
In [146]: %timeit pd.DataFrame(df.values.astype(np.float64))
10 loops, best of 3: 42.5 ms per loop
The casting back to dataframe wasn't that costly -
转换回数据帧并没有那么昂贵 -
In [147]: %timeit df.values.astype(np.float64)
10 loops, best of 3: 42.3 ms per loop # Casting to dataframe costed 0.2ms
