Question

我有一个名为df_mod的熊猫数据框。此数据帧中感兴趣的一个变量称为Evap_mod。当我使用命令print(df_mod['Evap_mod'])时，它返回：

2003-12-20 00:30:00    1.930664
2003-12-21 00:30:00    1.789290
2003-12-22 00:30:00    2.318347
2003-12-23 00:30:00    1.741943
2003-12-24 00:30:00    1.686124
2003-12-25 00:30:00    1.852876
2003-12-26 00:30:00    1.759650
2003-12-27 00:30:00    1.566521
2003-12-28 00:30:00    1.496039
2003-12-29 00:30:00    1.540751
2003-12-30 00:30:00    2.006475
2003-12-31 00:30:00    1.920912
Name: Evap_mod, Length: 729, dtype: float32

我还有另一个名为dff的熊猫数据框。此数据帧中感兴趣的一个变量称为PET_PT。当我使用命令print(dff['PET_PT'])时，它返回：

2003-12-20    4.810697
2003-12-21    4.739378
2003-12-22    4.994467
2003-12-23    5.138086
2003-12-24    5.024226
2003-12-25    4.937206
2003-12-26    4.551416
2003-12-27         NaN
2003-12-28         NaN
2003-12-29         NaN
2003-12-30         NaN
2003-12-31         NaN
Freq: D, Name: PET_PT, Length: 729, dtype: float64

我想在这两个变量之间进行以下简单计算：

df_mod['ER_mod']=(df_mod['Evap_mod']+np.mean(ddf['PET_PT']))/(ddf['PET_PT']+np.mean(ddf['PET_PT']))

不幸的是，此计算仅返回NaN：

2003-12-20 00:30:00   NaN
2003-12-21 00:30:00   NaN
2003-12-22 00:30:00   NaN
2003-12-23 00:30:00   NaN
2003-12-24 00:30:00   NaN
2003-12-25 00:30:00   NaN
2003-12-26 00:30:00   NaN
2003-12-27 00:30:00   NaN
2003-12-28 00:30:00   NaN
2003-12-29 00:30:00   NaN
2003-12-30 00:30:00   NaN
2003-12-31 00:30:00   NaN
Name: ER_mod, Length: 729, dtype: float64

有人知道为什么它会返回NaN以及如何解决此问题吗？

Answer 1

原因是不同的索引值，因此在划分索引值后不匹配并创建了NaN s。

解决方案是map系列ddf['PET_PT']的{{1}系列，由DatetimeIndex.normalize创建的辅助列date用于删除时间，并且还使用了熊猫mean的功能：

#same index values like df_mod
new = df_mod.assign(date = df_mod.index.normalize())['date'].map(ddf['PET_PT'])
print (new)
2003-12-20 00:30:00    4.810697
2003-12-21 00:30:00    4.739378
2003-12-22 00:30:00    4.994467
2003-12-23 00:30:00    5.138086
2003-12-24 00:30:00    5.024226
2003-12-25 00:30:00    4.937206
2003-12-26 00:30:00    4.551416
2003-12-27 00:30:00         NaN
2003-12-28 00:30:00         NaN
2003-12-29 00:30:00         NaN
2003-12-30 00:30:00         NaN
2003-12-31 00:30:00         NaN
Name: date, dtype: float64

df_mod['ER_mod']= df_mod['Evap_mod'] + ddf['PET_PT'].mean())/(new+ddf['PET_PT'].mean()
print (df_mod)
                     Evap_mod    ER_mod
2003-12-20 00:30:00  1.930664  0.702960
2003-12-21 00:30:00  1.789290  0.693480
2003-12-22 00:30:00  2.318347  0.729125
2003-12-23 00:30:00  1.741943  0.661170
2003-12-24 00:30:00  1.686124  0.663134
2003-12-25 00:30:00  1.852876  0.685986
2003-12-26 00:30:00  1.759650  0.704152
2003-12-27 00:30:00  1.566521       NaN
2003-12-28 00:30:00  1.496039       NaN
2003-12-29 00:30:00  1.540751       NaN
2003-12-30 00:30:00  2.006475       NaN
2003-12-31 00:30:00  1.920912       NaN

如果长度DataFrame相同且仅inde值不同，则可以将一个索引重新分配给另一个：

ddf.index = df_mod.index

df_mod['ER_mod'] = (df_mod['Evap_mod'] + ddf['PET_PT'].mean())/\
                   (ddf['PET_PT'] + ddf['PET_PT'].mean())
print (df_mod)
                     Evap_mod    ER_mod
2003-12-20 00:30:00  1.930664  0.702960
2003-12-21 00:30:00  1.789290  0.693480
2003-12-22 00:30:00  2.318347  0.729125
2003-12-23 00:30:00  1.741943  0.661170
2003-12-24 00:30:00  1.686124  0.663134
2003-12-25 00:30:00  1.852876  0.685986
2003-12-26 00:30:00  1.759650  0.704152
2003-12-27 00:30:00  1.566521       NaN
2003-12-28 00:30:00  1.496039       NaN
2003-12-29 00:30:00  1.540751       NaN
2003-12-30 00:30:00  2.006475       NaN
2003-12-31 00:30:00  1.920912       NaN

Answer 2

您的列中包含缺少的数据，因此您应根据目标使用不同的方法（平均值，零，中位数，随机等）来估算值

Answer 3

此处pandas和numpy的行为有所不同。每当计算np.mean(x)时，如果x包含NaN，在处理大熊猫NaN时，您将得到NaN的结果。以下应该可以工作

df_mod['ER_mod'] = (df_mod['Evap_mod'] + ddf['PET_PT'].mean())/\
                   (ddf['PET_PT'] + ddf['PET_PT'].mean())

否则，您可以使用np.nanmean代替np.mean。

熊猫数据框之间的计算返回NaN

3 个答案: