擅长:python、mysql、java
<p>您可以使用groupby或pivot_table函数聚合call_volume和call_drop。在</p>
<h2>Python代码:</h2>
<pre><code># Method 1: Using pivot_table
pd.pivot_table(df,index=["kpi_date","bts_name"],aggfunc=np.average)
# Method 2: Using groupby
df.groupby(["kpi_date", "bts_name"]).agg({"call_volume": np.average, "call_drop": np.average})
</code></pre>
<h2>输出:</h2>
^{pr2}$
<h2>编辑</h2>
<p>下面是将<code>kpi_date</code>作为列的代码</p>
<pre><code># Python code
df.pivot_table(['call_volume', 'call_drop'], ['bts_name'], 'kpi_date')
call_volume call_drop
kpi_date 9/1/2015 9/2/2015 9/3/2015 9/1/2015 9/2/2015 9/3/2015
bts_name
1002_NUc_Marathalli 8962 7033 6567 0.62 0.63 1.19
1003_IU2_Munnekolalu 4659 6671 NaN 1.17 0.46 NaN
</code></pre>