<p>你当然可以!你知道吗</p>
<p>下面是一个如何使用numpy的示例:</p>
<h2>使用Numpy</h2>
<pre><code>import math
import numpy as np
# Declare your Diametro_m, Espesor_mmhere just like you did in your example
# Transpose and merge the columns
arr = np.concatenate((Diametro_m, Espesor_mm.T), axis=1)
selection = arr[np.ix_(abs(arr[:0])<0.05,abs(arr[:1]-(math.e*1000)) > <1.2 )]
</code></pre>
<p><a href="https://stackoverflow.com/questions/23911875/select-certain-rows-condition-met-but-only-some-columns-in-python-numpy">Example usage from John Zwinck's answer</a></p>
<h2>使用数据帧</h2>
<p>如果您需要执行更重的查询或混合列数据类型,数据帧也可能非常适合您的应用程序。如果您选择以下选项,则此代码应适用于您:</p>
<pre><code># These imports go at the top of your document
import pandas as pd
import numpy as np
import math
# Declare your Diametro_m, Espesor_mmhere just like you did in your example
df_d = pd.DataFrame(data=Diametro_m,
index=np.array(range(1, len(Diametro_m))),
columns=np.array(range(1, len(Diametro_m))))
df_e = pd.DataFrame(data=Espesor_mm,
index=np.array(range(1, len(Diametro_m))),
columns=np.array(range(1, len(Diametro_m))))
# Merge the dataframes
merged_df = pd.merge(left=df_d , left_index=True
right=df_e , right_index=True,
how='inner')
# Now you can perform your selections like this:
selection = merged_df.loc[abs(merged_df['df_d']) <0.05, abs(merged_df['df_e']-(math.e*1000))) <1.2]
# This "mask" of the dataframe will return all results that satisfy your query.
print(selection)
</code></pre>