[英]Calculation between pandas dataframe returns NaN
我有一個名為df_mod
的pandas數據幀。 此數據Evap_mod
一個感興趣的變量稱為Evap_mod
。 當我使用命令print(df_mod['Evap_mod'])
,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
我有另一個叫做dff
pandas數據幀。 該數據幀中的一個感興趣的變量稱為PET_PT
。 當我使用命令print(dff['PET_PT'])
,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
我想在這兩個變量之間運行簡單的以下計算:
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,這個計算只返回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
有沒有人知道為什么它返回NaN以及如何克服這個問題?
原因是不同的索引值,所以在除以索引值之后不匹配並創建NaN
s。
解決方案是map
Series ddf['PET_PT']
由DatetimeIndex.normalize
創建的輔助列date
刪除時間,並使用pandas mean
s函數:
#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
如果相同長度的DataFrame
和inde值的差異是次數,則可以將一個索引重新分配給另一個索引:
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
您的列包含缺失的數據,因此您應該根據您的目標通過不同的方法(平均值,零,中位數,隨機等)來估算值
這里有pandas
和numpy
行為之間的區別。 無論何時計算np.mean(x)
如果x
包含NaN
您將使用NaN
作為結果,同時使用pandas NaN
將被忽略。 以下應該有效
df_mod['ER_mod'] = (df_mod['Evap_mod'] + ddf['PET_PT'].mean())/\
(ddf['PET_PT'] + ddf['PET_PT'].mean())
否則你可以使用np.nanmean
而不是np.mean
。
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