我有一本词典
Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771}
我想将keys和values分成2 numpy个数组。
我尝试np.array(Samples.keys(),dtype=np.float)，但收到错误TypeError: float() argument must be a string or a number
答案 0 :(得分：21)
您可以使用np.fromiter从字典键和值视图直接创建numpy数组：
在python 3中：
keys = np.fromiter(Samples.keys(), dtype=float)
vals = np.fromiter(Samples.values(), dtype=float)
在python 2中：
keys = np.fromiter(Samples.iterkeys(), dtype=float)
vals = np.fromiter(Samples.itervalues(), dtype=float)
答案 1 :(得分：10)
在python 3.4上，以下只是起作用：
Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771}
keys = np.array(list(Samples.keys()))
values = np.array(list(Samples.values()))
np.array(Samples.values())没有给出你在Python 3中的期望是因为在Python 3中，dict的values（）方法返回一个可迭代的视图，而在Python 2中，它返回一个实际的列表钥匙。
keys = np.array(list(Samples.keys()))实际上也可以在Python 2.7中使用，并且会使您的代码更加与版本无关。但对list()的额外调用会使其缓慢减速。
答案 2 :(得分：1)
只需将所有值分配到列表中，然后转换为np.array()。
import numpy as np
Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771}
keys = np.array(Samples.keys())
vals = np.array(Samples.values())
或者，如果you want to iterate over it：
import numpy as np
Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771}
keys = vals = []
for k, v in Samples.items():
keys.append(k)
vals.append(v)
keys = np.array(keys)
vals = np.array(vals)
答案 3 :(得分：1)
在Python 3.7中：
<LinearLayout
android:layout_width="match_parent"
android:layout_height="match_parent"
android:id="@+id/linearlayoutweblist"
android:layout_above="@+id/gettitle"
android:orientation="vertical">
<ListView
android:id="@+id/listView"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:layout_weight=".53"
android:choiceMode="singleChoice"
android:listSelector="#a2aed3"
android:layout_above="@+id/progressBar" />
<Button
android:layout_width="48dp"
android:layout_height="wrap_content"
android:maxHeight="5dp"
android:minWidth="5dp"
android:visibility="visible"
android:background="@drawable/ic_highlight_off_black_24dp"
android:id="@+id/closebtn"
android:layout_gravity="right|end"
android:gravity="right|end" />
<ProgressBar
android:id="@+id/progressBar"
android:layout_above="@+id/webviewlay"
android:layout_width="fill_parent"
android:layout_height="wrap_content"
android:indeterminate="true"
android:layout_marginTop="-7dp"
android:layout_marginBottom="-7dp"
android:visibility="visible"
style="@style/Widget.AppCompat.ProgressBar.Horizontal"
/>
<LinearLayout
android:layout_width="match_parent"
android:layout_height="match_parent"
android:id="@+id/webviewlay"
android:visibility="visible"
android:paddingTop="1dp"
android:layout_weight=".47"
android:paddingBottom="2dp"
android:background="@drawable/topline"
android:layout_alignParentRight="true"
android:layout_alignParentEnd="true"
android:layout_alignParentLeft="true"
android:layout_alignParentStart="true"
android:layout_above="@+id/gettitle">
<WebView
android:id="@+id/arabicfont"
android:layout_width="match_parent"
android:layout_height="match_parent"
/>
</LinearLayout>
</LinearLayout>
注意：重要的一点是，在此Python版本import numpy as np
Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771}
keys = np.array(list(Samples.keys()))
vals = np.array(list(Samples.values()))
和dict.keys()中，它们分别返回类型为dict.values()和dict_keys的对象。
答案 4 :(得分：0)
keys = np.array(dictionary.keys())
values = np.array(dictionary.values())
答案 5 :(得分：0)
如果您关心速度（Python 3.7）
rnd = np.random.RandomState(10)
for i in [10,100,1000,10000,100000]:
test_dict = {j:j for j in rnd.uniform(-100,100,i)}
assert len(test_dict) == i
print(f"\nFor {i} keys\n-----------")
%timeit keys = np.fromiter(test_dict.keys(), dtype=float)
%timeit keys = np.array(list(test_dict.keys()))
np.fromiter快5-7倍
For 10 keys
-----------
712 ns ± 4.77 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.65 µs ± 9.15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
For 100 keys
-----------
1.87 µs ± 13.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
8.02 µs ± 22.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
For 1000 keys
-----------
13.7 µs ± 27.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
70.5 µs ± 251 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
For 10000 keys
-----------
128 µs ± 70.6 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
698 µs ± 455 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
For 100000 keys
-----------
1.45 ms ± 374 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
7.14 ms ± 6.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)